Online Supplementary Material
Be Yourself and Behave Appropriately: Exploring Associations between Incongruent Personality States and Positive Affect, Tiredness, and Cognitive Performance
ReadMe
This document includes an overview of all analyses reported in the paper as well as additional tables and figures for the paper “Be Yourself and Behave Appropriately: Exploring Associations between Incongruent Personality States and Positive Affect, Tiredness, and Cognitive Performance”.
Tabs
To make the document easier to read, we use tabs. You will find tabs in gray in many places, where you can choose between different information (e.g., tables or graphics on a topic). Note: Sometimes there are several levels of tabs, so for example, on the first level you decide whether you want to see tables or graphics and on the next level you choose a sport.
Analysis Code
The document includes all R code used to create the graphs and tables or to perform the analyses. However, the code is hidden by default to enhance readability. If you want to see the code, simply click on the respective CODE button in the right hand side just above the figure or table—you will then see the associated codechunk. If you want to show all codechunks by default, click CODE > Show all Code at the very top of the document. Moreover, you can also download the RMarkdown source document there.
Methodological Aspects
Participants
data.raw <- data.raw %>%
mutate(screening = factor(ifelse(screener.1==1 & screener.2==4 & screener.3==2 & screener.4==1 & screener.5==1,"passed", "failed")),
requirements = factor(ifelse(consent==1 & old.enough==1 & device%in%c(1,2),"passed", "failed")),
careful.responding = factor(ifelse(bhi_25==3 & sit_13==6,"passed", "failed")),
exclude = ifelse(screening=="passed" & requirements=="passed" & careful.responding=="passed", FALSE, TRUE))
start <- boxGrob(glue("N = {pop}",
"participants started the study",
pop = txtInt(nrow(data.raw)),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
screening <- boxGrob(glue("Language comprehension and\nattention screening",
"N = {pop}",
pop = nrow(subset(data.raw, requirements!="failed")),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
traits <- boxGrob(glue("Personality trait assessment &\ndemographics",
"N = {incl}",
incl = nrow(subset(data.raw, screening=="passed" & requirements=="passed")),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
manip <- boxGrob(glue("Introduction and instruction for the game",
"(incl. manipulation of situation and behavior)",
"N = {incl}",
incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[3:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
game <- boxGrob(glue("Prisoner's Dilemma game",
"N = {incl}",
incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[5:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
states <- boxGrob(glue("Assessment of situation characteristics,",
" personality states, positive affect, and",
" tiredness during the game",
"N = {incl}",
incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[14:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
stroop <- boxGrob(glue("Numerical Stroop task",
"N = {incl}",
incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[15:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
comments <- boxGrob(glue("Comments and personalized code",
"N = {incl}",
incl = table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[18],
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#d6d6d6"),
width = 0.25)
excluded <- boxGrob(glue("Exclusion Criteria:",
" - No consent (N = {uninterested})",
" - Younger than 18 (N = {tooyoung})",
" - No desktop or laptop (N = {device})",
uninterested = nrow(subset(data.raw, consent==2 | is.na(consent))),
tooyoung = nrow(subset(data.raw, old.enough==2 | is.na(old.enough))) - nrow(subset(data.raw, consent==2 | is.na(consent))),
device = nrow(subset(data.raw, !(device%in%c(1,2)))) - nrow(subset(data.raw, old.enough==2 | is.na(old.enough))),
.sep = "\n"),
just = "left",
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#ffafaf"),
width = 0.2)
excluded2 <- boxGrob(glue("Exclusion Criteria:",
" - Failed screening (N = {screening})",
screening = nrow(subset(data.raw, requirements=="passed" & screening=="failed")),
.sep = "\n"),
just = "left",
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#ffafaf"),
width = 0.2)
final1 <- boxGrob(glue("Final sample for analyses\nwith positive affect and\ntiredness as DV:",
"N = {final1}*",
final1 = nrow(data),
.sep = "\n"),
just = "center",
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#add8a4"),
width = 0.2)
final2 <- boxGrob(glue("Final sample for analyses\nwith Stroop performance as DV:",
"N = {final2}*",
final2 = nrow(subset(data, progress==96)),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "#add8a4"),
width = 0.2,
just = "center")
note <- boxGrob(glue("* N = {careless} participants were exluded because of careless responding",
careless = nrow(subset(data.raw, pb>65)) - nrow(subset(data, progress>65)),
.sep = "\n"),
txt_gp = gpar(cex = 0.8),
box_gp = gpar(fill = "white", col="white"),
just = "left")
grid.newpage()
vert <- spreadVertical(start = start,
screening = screening,
traits = traits,
manip = manip,
game = game,
states = states,
stroop = stroop,
comments = comments,
final2 = final2)
excluded <- moveBox(excluded, x = .8, y = coords(vert$screening)$top + distance(vert$start, vert$screening, half = TRUE))
excluded2 <- moveBox(excluded2, x = .8, y = coords(vert$traits)$top + distance(vert$screening, vert$traits, half = TRUE))
final1 <- moveBox(final1, x = .2, y = coords(vert$stroop)$top + distance(vert$states, vert$stroop, half = TRUE))
vert$final2 <- moveBox(vert$final2, x = .2)
note <- moveBox(note, x=.8, y=.01)
# print arrows
for (i in 1:(length(vert) - 2)) {
connectGrob(vert[[i]], vert[[i + 1]], type = "vert", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches")))) %>%
print
}
connectGrob(vert$start, excluded, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$traits, excluded2, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$states, final1, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$comments, vert$final2, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
# Print boxes
vert
excluded
excluded2
final1
note
Figure 1: Flow of participants through the study including exclusion criteria, dropouts, and final sample sizes.
Manipulations
Situation Manipulations
Further information on the game and the manipulations can also be found in the “Documentation of the Game”-file that was upload with the preregistration.
The situation was manipulated in two ways. First, during the explanation of the game, the other partner was framed either as a partner or an opponent and was said the behave accordingly. Second, also during the explanation of the game, the payoffs were altered such that they either signal that stealing is not worthwhile or that stealing does pay off:
High Adversity and Deception-Condition
Let’s play a game!
You were randomly assigned an opponent. You and your opponent will play a game: In every round, there is a jackpot of coins to win. Both of you have the choice to either share the jackpot with your opponent or to steal the jackpot from your opponent.
If you both choose to share, each of you will receive 15 coins.
If both steal, each will receive 10 coins.
If you share, but your opponent steals, your opponent will receive 40 coins and you none.
The other way around, if you steal and your opponent shares, you will receive 40 coins and your opponent will receive none.You will both decide simultaneously without knowing what the other will choose. However, in each round, you will have the chance to communicate with your opponent which decision you plan to make; but you are both allowed to lie. Unfortunately, your opponent is a rather dishonest person and will often play dirty. You will play multiple rounds.
t <- data.frame(a=c("",
"<b style='color:red;display:inline;'>you</b> share",
"<b style='color:red;display:inline;'>you</b> steal"),
b=c("<b style='color:blue;display:inline;'>your opponent</b> shares",
"<b style='color:red;display:inline;'>15</b> | <b style='color:blue;display:inline;'>15</b>",
"<b style='color:red;display:inline;'>40</b> | <b style='color:blue;display:inline;'>0</b>"),
c=c("<b style='color:blue;display:inline;'>your opponent</b> steals",
"<b style='color:red;display:inline;'>0</b> | <b style='color:blue;display:inline;'>40</b>",
"<b style='color:red;display:inline;'>10</b> | <b style='color:blue;display:inline;'>10</b>"))
kable(t,
col.names=c("", "", ""),
align=c("r", "c", "c"),
escape = F) %>%
kable_paper(bootstrap_options = "bordered", full_width = F, position = "left") %>%
column_spec(1, bold = T) %>%
row_spec(1, bold = T)| your opponent shares | your opponent steals | |
| you share | 15 | 15 | 0 | 40 |
| you steal | 40 | 0 | 10 | 10 |
Low Adversity and Deception-Condition
Let’s play a game!
You were randomly assigned a partner. You and your partner will play a game: In every round, there is a jackpot of coins to win. Both of you have the choice to either share the jackpot with your partner or to steal the jackpot from your partner.
you both choose to share, each of you will receive 20 coins. If both steal, the jackpot is lost and none of you receives anything.
If you share, but your partner steals, your partner will receive 30 coins and you will receive 5 coins. The other way around, if you steal and your partner cooperates, you will receive 30 coins and your partner will receive 5 coins.You will both decide simultaneously without knowing what the other will choose. However, in each round, you will have the chance to communicate with your partner which decision you plan to make; but you are both allowed to lie. Luckily, your partner is a pretty honest person and is most of the time true to their word. You will play multiple rounds.
t <- data.frame(a=c("",
"<b style='color:red;display:inline;'>you</b> share",
"<b style='color:red;display:inline;'>you</b> steal"),
b=c("<b style='color:blue;display:inline;'>your partner</b> shares",
"<b style='color:red;display:inline;'>20</b> | <b style='color:blue;display:inline;'>20</b>",
"<b style='color:red;display:inline;'>30</b> | <b style='color:blue;display:inline;'>5</b>"),
c=c("<b style='color:blue;display:inline;'>your partner</b> steals",
"<b style='color:red;display:inline;'>5</b> | <b style='color:blue;display:inline;'>30</b>",
"<b style='color:red;display:inline;'>0</b> | <b style='color:blue;display:inline;'>0</b>"))
kable(t,
col.names=c("", "", ""),
align=c("r", "c", "c"),
escape = F) %>%
kable_paper(bootstrap_options = "bordered", full_width = F, position = "left") %>%
column_spec(1, bold = T) %>%
row_spec(1, bold = T)| your partner shares | your partner steals | |
| you share | 20 | 20 | 5 | 30 |
| you steal | 30 | 5 | 0 | 0 |
Behavior Manipulations
Further information on the game and the manipulations can also be found in the “Documentation of the Game”-file that was upload with the preregistration.
Participants’ behaviors and personality states were manipulated during the instructions for the Prisoner’s Dilemma game. Following the general explanation of the game, participants were informed about their task:
High Agreeableness and Honesty-Condition
Your task
Your task is to behave honestly and cooperatively with your [partner/opponent]. Please try to be true to your word most of the time. Of course, you do not always have to do as you proposed, but please try to be honest and cooperative as much as you can."
Low Agreeableness and Honesty-Condition
Your task
Your task is to behave dishonestly and uncooperatively with your [partner/opponent]. Please try to play dirty most of the time. Of course, you do not always have to deceive your [partner/opponent], but please try to be dishonest and uncooperative as much as you can.
Measures
Personality Traits
HEXACO model personality traits were measured with the Brief Hexaco Inventory (de Vries, 2019). The original article including the items is:
De Vries, R. E. (2013). The 24-item brief hexaco inventory (bhi). Journal of Research in Personality, 47(6), 871–880. https://doi.org/10.1016/j.jrp.2013.09.003
tab <- codebook.raw %>%
filter(stringr::str_detect(`Variable Name`, 'bhi')) %>%
filter(`Variable Name` != "bhi_25") %>%
arrange(Description) %>%
mutate(`Reverse Coding` = ifelse(is.na(`Reverse Coding`),"No", `Reverse Coding`))
kable(tab[c(1:24),c(2,4,6,5)]
, col.names = c("Item text", "Scale", "Facet", "Reverse coding necessary")
, caption="Items of the BHI used for personality trait assessment"
, escape=F
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)| Item text | Scale | Facet | Reverse coding necessary |
|---|---|---|---|
| I remain unfriendly to someone who was mean to me. | Agreeableness | Forgiveness | Yes |
| I often express criticism. | Agreeableness | Gentleness | Yes |
| I tend to quickly agree with others. | Agreeableness | Flexibility | No |
| Even when I’m treated badly, I remain calm. | Agreeableness | Patience | No |
| I make sure that things are in the right spot. | Conscientiousness | Organization | No |
| I postpone complicated tasks as long as possible. | Conscientiousness | Diligence | Yes |
| I work very precisely. | Conscientiousness | Perfectionism | No |
| I often do things without really thinking. | Conscientiousness | Prudence | Yes |
| I am afraid of feeling pain. | Emotionality | Fearfulness | No |
| I worry less than others. | Emotionality | Anxiety | Yes |
| I can easily overcome difficulties on my own. | Emotionality | Dependence | Yes |
| I have to cry during sad or romantic movies. | Emotionality | Sentimentality | No |
| Nobody likes talking with me. | Extraversion | Social Self-esteem | Yes |
| I easily approach strangers. | Extraversion | Social Boldness | No |
| I like to talk with others. | Extraversion | Sociability | No |
| I am seldom cheerful. | Extraversion | Liveliness | Yes |
| I find it difficult to lie. | Honesty-Humility | Sincerity | No |
| I would like to know how to make lots of money in a dishonest manner. | Honesty-Humility | Fairness | Yes |
| I want to be famous. | Honesty-Humility | Greed Avoidance | Yes |
| I am entitled to special treatment. | Honesty-Humility | Modesty | Yes |
| I can look at a painting for a long time. | Openness to Experience | Aesthetic Appreciation | No |
| I think science is boring. | Openness to Experience | Inquisitiveness | Yes |
| I have a lot of imagination. | Openness to Experience | Creativity | No |
| I like people with strange ideas. | Openness to Experience | Unconventionality | No |
| Note: | |||
|
The instruction was ’First, we would like to learn a bit about you. Please indicate to what extent you agree with the following statements, using the following answering categories:’ and the response scale was 1=strongly disagree 2=disagree 3=neutral (neither agree, nor disagree) 4=agree 5=strongly agree. |
Personality States
HEXACO model personality states were measured with bipolar items adopted from Sherman et al. (2015) and Churchyard (2013). All personality states were measured with one bipolar item each adopted from Sherman et al. (2015). State honesty-humility and state agreeableness were additionally measured with three items from Churchyard (2013), and all four items were combined into one scale score for these states. The original articles using these items are:
Sherman, R. A., Rauthmann, J. F., Brown, N. A., Serfass, D. G., & Jones, A. B. (2015). The independent effects of personality and situations on real-time expressions of behavior and emotion. Journal of Personality and Social Psychology, 109(5), 872–888. https://doi.org/10.1037/pspp0000036
Churchyard, J. S. (2013). Within-person variation in personality and psychological well-being [Dissertation]. University of Hertfordshire, Hertfordshire. https://doi.org/10.18745/th.15432
tab <- codebook.raw %>%
filter(Description=="behavior")
kable(tab[,c(2,6,5)]
, col.names = c("Item text (bipolar)", "Scale", "Reverse coding necessary")
, caption="Items of the MDMQ used for the assessment of tiredness"
, escape=F
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general=paste0("The instruction was '", tab$Instruction[1], "'."), escape = F)| Item text (bipolar) | Scale | Reverse coding necessary |
|---|---|---|
| arrogant, dishonest - humble, honest | Honesty-Humility | No |
| insincere - sincere | Honesty-Humility | No |
| unfair - fair | Honesty-Humility | No |
| arrogant - modest | Honesty-Humility | No |
| calm, unemotional - nervous, emotional | Emotionality | No |
| reserved, quiet - outgoing, sociable | Extraversion | No |
| cold, quarrelsome - warm, agreeable | Agreeableness | No |
| harsh - gentle | Agreeableness | No |
| inflexible - flexible | Agreeableness | No |
| temperamental - patient | Agreeableness | No |
| disorganized, lazy - organized, hardworking | Conscientiousness | No |
| unintelligent, uncreative - intelligent, creative | Openness | No |
| Note: | ||
| The instruction was ‘Please indicate on a scale from 1 (not at all) to 7 (totally) how well each of the following adjectives describes how you behaved in the game that you just played.’. |
Situation Characteristics
Perceived situation characteristics from the Situational Eight DIAMONDS taxonomy (Rauthmann et al., 2014) were measured with the S8* (Rauthmann & Sherman, 2016a) for adversity and deception and with the shorter S8-I Rauthmann & Sherman, 2016b) for the remaining situation characteristics. The original articles including the items are:
Rauthmann, J. F., & Sherman, R. A. (2016). Measuring the situational eight diamonds characteristics of situations. European Journal of Psychological Assessment, 32(2), 155–164. https://doi.org/10.1027/1015-5759/a000246
Rauthmann, J. F., & Sherman, R. A. (2016). Ultra-brief measures for the situational eight diamonds domains. European Journal of Psychological Assessment, 32(2), 165–174. https://doi.org/10.1027/1015-5759/a000245
tab <- codebook.raw %>%
filter(stringr::str_detect(`Variable Name`, 'sit_')) %>%
filter(`Variable Name` != "sit_13") %>%
mutate(Description = ifelse(Subscale=="Adversity" | Subscale=="Deception", "S8* (Rauthmann & Sherman, 2016a)", "S8-I (Rauthmann & Sherman, 2016b)"),
`Reverse Coding` = ifelse(is.na(`Reverse Coding`),"No", `Reverse Coding`))
kable(tab[,c(2,6,4,5)]
, col.names = c("Item text", "Scale", "Questionnaire", "Reverse coding necessary")
, caption="Items of the S8* and S8-I used for the assessment of perceived situation characteristics"
, escape=F
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)| Item text | Scale | Questionnaire | Reverse coding necessary |
|---|---|---|---|
| Work has to be done. | Duty | S8-I (Rauthmann & Sherman, 2016b) | No |
| Deep thinking is required. | Intellect | S8-I (Rauthmann & Sherman, 2016b) | No |
| Potential romantic partners are present. | Mating | S8-I (Rauthmann & Sherman, 2016b) | No |
| The situation is pleasant. | Positivity | S8-I (Rauthmann & Sherman, 2016b) | No |
| The situation contains negative feelings (e.g., stress, anxiety, guilt, etc.). | Negativity | S8-I (Rauthmann & Sherman, 2016b) | No |
| Social interactions are possible or required. | Sociality | S8-I (Rauthmann & Sherman, 2016b) | No |
| I am being blamed for something. | Adversity | S8* (Rauthmann & Sherman, 2016a) | No |
| I am being criticized. | Adversity | S8* (Rauthmann & Sherman, 2016a) | No |
| I am being threatened by someone or something. | Adversity | S8* (Rauthmann & Sherman, 2016a) | No |
| It is possible to deceive someone. | Deception | S8* (Rauthmann & Sherman, 2016a) | No |
| Someone in this situation could be deceptive. | Deception | S8* (Rauthmann & Sherman, 2016a) | No |
| Not dealing with others in an honest way is possible. | Deception | S8* (Rauthmann & Sherman, 2016a) | No |
| Note: | |||
| The instruction was ‘We will now ask you some questions about the game you just played. Recall that we are interested in your subjective opinion and ratings. Therefore, answers cannot be correct or incorrect. Please answer all questions as honestly and openly as possible.Please indicate on a scale from 1 (=not at all) to 7 (=totally) how well each of the following sentences describes the game situation you just encountered’ and the response scale was 1= not at all 7 = totally. |
Positive Affect
Positive affect was measured with a short form of the English version of the multidimensional mood state questionnaire (Steyer et al., 1994). The original version can be found here.
tab <- codebook.raw %>%
filter(stringr::str_detect(Subscale, 'GB')) %>%
mutate(Subscale = "positive affect (good-bad mood)")
kable(tab[,c(2,4,6,5)]
, col.names = c("Item text", "Scale", "Subscale", "Reverse coding necessary")
, caption="Items of the MDMQ used for the assessment of positive affect"
, escape=F
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)| Item text | Scale | Subscale | Reverse coding necessary |
|---|---|---|---|
| content | affect | positive affect (good-bad mood) | No |
| bad | affect | positive affect (good-bad mood) | Yes |
| great | affect | positive affect (good-bad mood) | No |
| uncomfortable | affect | positive affect (good-bad mood) | Yes |
| Note: | |||
| The instruction was ‘In the following you find a list of expressions that characterize different moods. Please take a look at the list, word by word, and mark for each word the answer that represents best the actual intensity of your mood status at the end of the game.’ and the response scale was 1= not at all 5= totally. |
Tiredness
Tiredness was measured with a short form of the English version of the multidimensional mood state questionnaire (Steyer et al., 1994). The original version can be found here. Tiredness was reverse-coded such that higher levels indicate less tiredness, or more active mood.
tab <- codebook.raw %>%
filter(stringr::str_detect(Subscale, 'AT')) %>%
mutate(Subscale = "tiredness (active-tired mood)")
kable(tab[,c(2,4,6,5)]
, col.names = c("Item text", "Scale", "Subscale", "Reverse coding necessary")
, caption="Items of the MDMQ used for the assessment of tiredness"
, escape=F
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)| Item text | Scale | Subscale | Reverse coding necessary |
|---|---|---|---|
| rested | affect | tiredness (active-tired mood) | No |
| worn-out | affect | tiredness (active-tired mood) | Yes |
| tired | affect | tiredness (active-tired mood) | Yes |
| energetic | affect | tiredness (active-tired mood) | No |
| Note: | |||
| The instruction was ‘In the following you find a list of expressions that characterize different moods. Please take a look at the list, word by word, and mark for each word the answer that represents best the actual intensity of your mood status at the end of the game.’ and the response scale was 1= not at all 5= totally. |
Analytical Strategy
R Version and Packages
This is a list of the packages used to prepare and analyze the data and to present the results. We explicitly thank the authors of all packages for the work they have put and are putting into the development and maintenance of the packages.
report_packages(include_R=FALSE, prefix="\n * ")papaja (version 0.1.0.9997; Aust, Barth, 2020)
tinylabels (version 0.2.1; Barth, 2021)
cowplot (version 1.1.1; Claus Wilke, 2020)
Rcpp (version 1.0.6; Dirk Eddelbuettel and Romain Francois, 2011)
Matrix (version 1.2.18; Douglas Bates and Martin Maechler, 2019)
lme4 (version 1.1.27; Douglas Bates et al., 2015)
ggplot2 (version 3.3.4; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.)
stringr (version 1.4.0; Hadley Wickham, 2019)
forcats (version 0.5.1; Hadley Wickham, 2021)
tidyr (version 1.1.3; Hadley Wickham, 2021)
readxl (version 1.3.1; Hadley Wickham and Jennifer Bryan, 2019)
readr (version 1.4.0; Hadley Wickham and Jim Hester, 2020)
dplyr (version 1.0.6; Hadley Wickham et al., 2021)
kableExtra (version 1.3.4; Hao Zhu, 2021)
afex (version 0.28.1; Henrik Singmann et al., 2021)
corx (version 1.0.6.1; James Conigrave, 2020)
glue (version 1.4.2; Jim Hester, 2020)
tibble (version 3.1.2; Kirill Müller and Hadley Wickham, 2021)
purrr (version 0.3.4; Lionel Henry and Hadley Wickham, 2020)
report (version 0.3.0; Makowski et al., 2020)
specr (version 0.2.2; Masur et al., 2020)
Gmisc (version 2.0.2; Max Gordon, 2021)
htmlTable (version 2.1.0; Max Gordon, Stephen Gragg and Peter Konings, 2020)
psych (version 2.1.3; Revelle, 2020)
lattice (version 0.20.41; Sarkar, Deepayan, 2008)
RSA (version 0.10.4; Schönbrodt et al., 2021)
patchwork (version 1.1.1; Thomas Lin Pedersen, 2020)
tidyverse (version 1.3.1; Wickham et al., 2019)
DT (version 0.18; Yihui Xie, Joe Cheng and Xianying Tan, 2021)
lavaan (version 0.6.8; Yves Rosseel, 2012)
report_system()Analyses were conducted using the R Statistical language (version 4.0.3; R Core Team, 2020) on Windows 10 x64 (build 19042)
Planned Analyses
The planned analyses were preregistered along with the study design. This preregistration, including a detailed account of the analytic strategy, can be found on OSF: https://osf.io/hnu4b/
Codebook of the Final Data
This is the codebook for the final data (without the reaction time and error-rate specifications) to give an overview of the data.
# print table
datatable(codebook,
caption="Codebook of the final dataset.") Pretest
We tested an earlier version of the experimental paradigm in a preliminary study to determine if the manipulation works. Here, we briefly summarize the design of the game and the manipulations and the findings from this pretest.
Game
These are the original instructions for the prisoner’s dilemma game in the pretest:
“You were randomly assigned a partner[opponent]. You and your partner[opponent] will play a game: You are standing in front of a machine that multiplies and distributes coins. In every round, each of you will receive a coin. You have the choice to either put your coin into the machine (i.e., cooperate with your partner[opponent]) or keep your coin (i.e., cheat on your partner[opponent]). Your partner[opponent] will have the same choice.
If you both cooperate, that is, both put their coin into the machine, each of you will receive 19[10] coins out of the machine. If both cheat, each will receive 1[8] coin. If you cooperate, but your partner[opponent] cheats, your partner[opponent] will receive 20 coins and you none. The other way around, if you cheat and your partner[opponent] cooperates, you will receive 20 coins and your partner[opponent] will receive none. You will both decide simultaneously without knowing what the other will choose. You will play multiple rounds.”
In this version, we aimed to covertly manipulate the participants’ behaviors by varying the goal of the game:
“Your task is to play with your partner/opponent such that you will together gain as many coins as possible. In the end, your result will be number of coins that you have gained on average, that is, the mean of your and your partner/opponent’s coins. If you gained, for example, 300 coins and your partner/opponent gained 100, you will receive (300+100)/2, that is, 200 tickets for the lottery.”
— high agreeableness and honesty-condition
“Your task is to play against your partner/opponent such that you will gain more points than your partner/opponent. In the end, your result will be your advantage over your partner/opponent, that is, the difference between your and your partner/opponent’s coins. If you gained, for example, 300 coins and your partner/opponent gained 100, you will receive 300-100, that is, 200 tickets for the lottery.”
— low agreeableness and honesty-condition
The situation was manipulated by the description of the computer (i.e., partner or opponent) and by changing the payoffs of the game.
Results
aov.coop <- aov_car(coopSum ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.agree <- aov_car(state.agreeableness ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.hon <- aov_car(state.honesty.humility ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.adv <- aov_car(adversity ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.dec <- aov_car(deception ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
tab <- bind_rows(as.data.frame(nice(aov.coop, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.agree, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.hon, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.adv, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.dec, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
)
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and the targeted personality states and situation characteristics in the pretest.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
pack_rows("DV: Sum of cooperative decisions", 1, 3) %>%
pack_rows("DV: State agreeableness", 4, 6) %>%
pack_rows("DV: State honesty-humility", 7, 9) %>%
pack_rows("DV: Adversity", 10, 12) %>%
pack_rows("DV: Deception", 13, 15) | Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| DV: Sum of cooperative decisions | ||||
| Behavior conditions | 1, 174 | 34.52 | .166 | <.001 |
| Situation conditions | 1, 174 | 0.91 | .005 | .340 |
| Behavior x Situation conditions | 1, 174 | 0.18 | .001 | .676 |
| DV: State agreeableness | ||||
| Behavior conditions | 1, 174 | 27.14 | .135 | <.001 |
| Situation conditions | 1, 174 | 0.79 | .005 | .376 |
| Behavior x Situation conditions | 1, 174 | 1.63 | .009 | .204 |
| DV: State honesty-humility | ||||
| Behavior conditions | 1, 174 | 20.45 | .105 | <.001 |
| Situation conditions | 1, 174 | 0.71 | .004 | .401 |
| Behavior x Situation conditions | 1, 174 | 0.12 | <.001 | .735 |
| DV: Adversity | ||||
| Behavior conditions | 1, 174 | 2.83 | .016 | .094 |
| Situation conditions | 1, 174 | 0.78 | .004 | .379 |
| Behavior x Situation conditions | 1, 174 | 2.85 | .016 | .093 |
| DV: Deception | ||||
| Behavior conditions | 1, 174 | 0.01 | <.001 | .938 |
| Situation conditions | 1, 174 | 0.08 | <.001 | .783 |
| Behavior x Situation conditions | 1, 174 | 4.37 | .024 | .038 |
# plot interaction diagram
r1 <- ggplot(pretest %>%
group_by(situation_factor, behavior_factor) %>%
summarise(groups = mean(coopSum, na.rm=TRUE)),
aes(x = situation_factor, y = groups, color = behavior_factor)) +
theme_pub() + xlab("Situation conditions") + ylab("Sum of cooperative decisions") +
scale_y_continuous(limits=c(1,15), labels=c(1:15), breaks=c(1:15)) +
ggtitle("Cooperative Decisions") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = behavior_factor), size=1.5) +
geom_point(size=2.5) + theme(legend.title = element_text(size=9))
r2 <- ggplot(pretest %>%
group_by(situation_factor, behavior_factor) %>%
summarise(groups = mean(state.agreeableness, na.rm=TRUE)),
aes(x = situation_factor, y = groups, color = behavior_factor)) +
theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("State Agreeableness") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = behavior_factor), size=1.5) +
geom_point(size=2.5) + theme(legend.title = element_text(size=9))
r3 <- ggplot(pretest %>%
group_by(situation_factor, behavior_factor) %>%
summarise(groups = mean(state.honesty.humility, na.rm=TRUE)),
aes(x = situation_factor, y = groups, color = behavior_factor)) +
theme_pub() + xlab("Situation conditions") + ylab("State honesty-humility score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("State Honesty-Humility") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = behavior_factor), size=1.5) +
geom_point(size=2.5) + theme(legend.title = element_text(size=9))
r4 <- ggplot(pretest %>%
group_by(situation_factor, behavior_factor) %>%
summarise(groups = mean(adversity, na.rm=TRUE)),
aes(x = behavior_factor, y = groups, color = situation_factor)) +
theme_pub() + xlab("Behavior conditions") + ylab("Adversity score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Adversity") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Situation conditions") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = situation_factor), size=1.5) +
geom_point(size=2.5) + theme(legend.title = element_text(size=9))
r5 <- ggplot(pretest %>%
group_by(situation_factor, behavior_factor) %>%
summarise(groups = mean(deception, na.rm=TRUE)),
aes(x = behavior_factor, y = groups, color = situation_factor)) +
theme_pub() + xlab("Behavior conditions") + ylab("Deception score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Deception") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Situation conditions") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = situation_factor), size=1.5) +
geom_point(size=2.5) + theme(legend.title = element_text(size=9))
### combine plots
r1 + r2 + r3 + r4 + r5 + plot_layout(ncol=2) + plot_annotation(tag_levels = "A") 
Figure 2: Associations between the experimental conditions and the targeted personality states and situation characteristics in the pretest.
Conclusion
The game in the pretest successfully manipulated participant’s levels of state agreeableness and state honesty-humility, but it had no effect on the perceptions of adversity and deception. In sum, this pretest thus revealed that the manipulation of the situation was unsuccessful and that aiming to covertly manipulate behavior via goal instructions may have unwanted effect on the perception of the situations. We therefore adapted the experimental paradigm for the main study described in the methods below.
Descriptives
Descriptive Statistics
| n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | Internal consistency (α) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Personality Traits | |||||||||||||
| Trait Honesty-Humility | 307 | 4.04 | 0.77 | 4.25 | 4.12 | 0.74 | 2.0 | 5.0 | 3.0 | -0.80 | -0.03 | 0.04 | 0.61 |
| Trait Emotionality | 307 | 2.88 | 0.66 | 3.00 | 2.90 | 0.74 | 1.0 | 4.5 | 3.5 | -0.26 | -0.11 | 0.04 | 0.28 |
| Trait Extraversion | 307 | 3.62 | 0.77 | 3.75 | 3.65 | 0.74 | 1.5 | 5.0 | 3.5 | -0.31 | -0.33 | 0.04 | 0.59 |
| Trait Agreeableness | 307 | 3.01 | 0.57 | 3.00 | 3.00 | 0.37 | 1.5 | 5.0 | 3.5 | 0.35 | 0.82 | 0.03 | 0.22 |
| Trait Conscientiousness | 307 | 3.37 | 0.51 | 3.25 | 3.37 | 0.37 | 2.0 | 5.0 | 3.0 | 0.04 | 0.10 | 0.03 | 0.52 |
| Trait Openness | 306 | 3.38 | 0.72 | 3.50 | 3.39 | 0.74 | 1.5 | 5.0 | 3.5 | -0.09 | -0.12 | 0.04 | 0.54 |
| Personality States | |||||||||||||
| State Honesty-Humility | 257 | 4.43 | 1.93 | 4.75 | 4.51 | 2.59 | 1.0 | 7.0 | 6.0 | -0.24 | -1.17 | 0.12 | 0.93 |
| State Emotionality | 257 | 2.68 | 1.73 | 2.00 | 2.43 | 1.48 | 1.0 | 7.0 | 6.0 | 0.91 | -0.02 | 0.11 | |
| State Extraversion | 257 | 3.88 | 1.91 | 4.00 | 3.86 | 1.48 | 1.0 | 7.0 | 6.0 | 0.16 | -0.93 | 0.12 | |
| State Agreeableness | 257 | 4.77 | 1.60 | 5.00 | 4.87 | 1.48 | 1.0 | 7.0 | 6.0 | -0.45 | -0.47 | 0.10 | 0.84 |
| State Conscientiousness | 257 | 5.22 | 1.64 | 5.00 | 5.44 | 1.48 | 1.0 | 7.0 | 6.0 | -0.88 | 0.33 | 0.10 | |
| State Openness | 257 | 5.09 | 1.66 | 5.00 | 5.29 | 1.48 | 1.0 | 7.0 | 6.0 | -0.77 | 0.00 | 0.10 | |
| Concrete Behaviors | |||||||||||||
| Sharing Behaviors | 250 | 7.25 | 4.68 | 6.50 | 7.04 | 5.19 | 1.0 | 15.0 | 14.0 | 0.35 | -1.23 | 0.30 | |
| Honest Behaviors | 297 | 10.72 | 3.63 | 11.00 | 11.07 | 4.45 | 1.0 | 15.0 | 14.0 | -0.59 | -0.46 | 0.21 | |
| Situation Characteristics | |||||||||||||
| Duty | 305 | 4.42 | 1.89 | 4.00 | 4.53 | 1.48 | 1.0 | 7.0 | 6.0 | -0.33 | -0.81 | 0.11 | |
| Intellect | 304 | 3.69 | 2.02 | 4.00 | 3.61 | 2.97 | 1.0 | 7.0 | 6.0 | 0.07 | -1.24 | 0.12 | |
| Adversity | 304 | 2.42 | 1.56 | 2.00 | 2.20 | 1.48 | 1.0 | 7.0 | 6.0 | 0.98 | 0.17 | 0.09 | 0.83 |
| Mating | 307 | 2.30 | 1.79 | 1.00 | 1.98 | 0.00 | 1.0 | 7.0 | 6.0 | 1.19 | 0.23 | 0.10 | |
| pOsitivity | 307 | 3.90 | 1.88 | 4.00 | 3.88 | 1.48 | 1.0 | 7.0 | 6.0 | 0.00 | -0.95 | 0.11 | |
| Negativity | 307 | 3.55 | 2.11 | 4.00 | 3.45 | 2.97 | 1.0 | 7.0 | 6.0 | 0.22 | -1.27 | 0.12 | |
| Deception | 304 | 5.35 | 1.41 | 5.67 | 5.50 | 1.48 | 1.0 | 7.0 | 6.0 | -0.77 | 0.15 | 0.08 | 0.72 |
| Sociality | 306 | 4.27 | 1.98 | 4.00 | 4.34 | 2.97 | 1.0 | 7.0 | 6.0 | -0.32 | -1.02 | 0.11 | |
| Outcomes | |||||||||||||
| Positive Affect | 255 | 3.59 | 1.05 | 3.75 | 3.67 | 1.11 | 1.0 | 5.0 | 4.0 | -0.54 | -0.38 | 0.07 | 0.80 |
| Tiredness(r) | 255 | 3.39 | 0.92 | 3.25 | 3.42 | 1.11 | 1.0 | 5.0 | 4.0 | -0.32 | -0.04 | 0.06 | 0.67 |
| Note: | |||||||||||||
| Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Internal consistencies are reported for all measured that included more than one item. | |||||||||||||
Intercorrelations
rel <- data %>%
ungroup() %>%
select(# Traits
"trait.h", "trait.e", "trait.x", "trait.a", "trait.c", "trait.o",
# Behaviors
"state.h", "state.e", "state.x", "state.a", "state.c", "state.o", "nCoop", "nHon",
# Situation
"dut", "int", "adv", "mat", "pos", "neg", "dec", "soc",
# Outcomes
"mood.gb", "mood.at")
x <- corx::corx(rel,
triangle = "lower",
stars = c(0.05))
row.names(x$apa) <- c(# Traits
"1 Trait Honesty-Humility", "2 Trait Emotionality", "3 Trait Extraversion", "4 Trait Agreeableness", "5 Trait Conscientiousness", "6 Trait Openness",
# Behaviors
"7 State Honesty-Humility", "8 State Emotionality", "9 State Extraversion", "10 State Agreeableness", "11 State Conscientiousness", "12 State Openness", "13 Sharing Behaviors", "14 Honest Behaviors",
# Situation
"15 Duty", "16 Intellect", "17 Adversity", "18 Mating", "19 pOsitivity", "20 Negativity", "21 Deception", "22 Sociality",
# Outcomes
"23 Positive Affect", "24 Tiredness(r)")
x$apa[which(x$apa==' - ', arr.ind=T)] <- "--"
kable(x$apa
, caption="Intercorrelations of the relevant variables."
, escape=FALSE
) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), fixed_thead = T) %>%
column_spec(c(1), width = "16em")| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 Trait Honesty-Humility | – | ||||||||||||||||||||||
| 2 Trait Emotionality | -.06 | – | |||||||||||||||||||||
| 3 Trait Extraversion | .21* | -.19* | – | ||||||||||||||||||||
| 4 Trait Agreeableness | .08 | -.11* | .29* | – | |||||||||||||||||||
| 5 Trait Conscientiousness | -.05 | -.12* | .15* | .08 | – | ||||||||||||||||||
| 6 Trait Openness | -.17* | -.12* | .19* | .19* | .25* | – | |||||||||||||||||
| 7 State Honesty-Humility | -.05 | -.01 | .15* | .23* | .14* | .09 | – | ||||||||||||||||
| 8 State Emotionality | -.18* | .24* | -.11 | -.07 | -.02 | .02 | -.14* | – | |||||||||||||||
| 9 State Extraversion | -.24* | -.03 | .19* | .05 | .16* | .15* | .28* | .15* | – | ||||||||||||||
| 10 State Agreeableness | .00 | -.10 | .18* | .24* | .15* | .11 | .82* | -.24* | .34* | – | |||||||||||||
| 11 State Conscientiousness | -.03 | -.10 | .17* | .17* | .22* | .12 | .45* | -.18* | .25* | .53* | – | ||||||||||||
| 12 State Openness | -.13* | -.14* | .10 | .21* | .20* | .24* | .41* | -.06 | .31* | .47* | .61* | – | |||||||||||
| 13 Sharing Behaviors | .17* | .02 | .03 | .08 | -.07 | -.09 | .50* | -.05 | .01 | .39* | .11 | -.02 | – | ||||||||||
| 14 Honest Behaviors | .06 | .04 | -.01 | .05 | .03 | -.01 | .23* | .05 | -.01 | .14* | .06 | .01 | .47* | – | |||||||||
| 15 Duty | -.23* | .00 | .10 | .11 | .20* | .07 | .03 | .07 | .10 | .01 | .22* | .11 | -.10 | -.06 | – | ||||||||
| 16 Intellect | -.25* | .05 | .08 | .24* | .17* | .23* | .16* | .12 | .24* | .18* | .21* | .25* | -.06 | -.12* | .35* | – | |||||||
| 17 Adversity | -.45* | .10 | -.20* | -.04 | .14* | .06 | -.08 | .27* | .00 | -.19* | -.09 | -.01 | -.15* | -.12* | .30* | .32* | – | ||||||
| 18 Mating | -.54* | -.01 | -.01 | .08 | .12* | .16* | .20* | .18* | .30* | .15* | .07 | .16* | .01 | -.03 | .32* | .42* | .49* | – | |||||
| 19 pOsitivity | -.21* | -.11 | .08 | .19* | .08 | .09 | .52* | -.16* | .37* | .54* | .26* | .34* | .16* | .04 | .17* | .31* | .01 | .37* | – | ||||
| 20 Negativity | -.18* | .12* | -.11* | -.10 | .13* | .02 | -.30* | .23* | -.21* | -.34* | -.17* | -.16* | -.08 | -.04 | .18* | .15* | .55* | .17* | -.31* | – | |||
| 21 Deception | .11* | .04 | .03 | -.14* | .01 | -.05 | -.16* | -.10 | -.16* | -.06 | .08 | -.04 | -.04 | -.11 | -.02 | .00 | .10 | -.15* | -.17* | .27* | – | ||
| 22 Sociality | -.21* | .06 | .07 | .06 | .03 | .07 | .12 | .08 | .31* | .10 | .07 | .04 | -.03 | -.09 | .31* | .39* | .28* | .35* | .22* | .18* | .12* | – | |
| 23 Positive Affect | -.01 | -.18* | .20* | .13* | .07 | .03 | .52* | -.26* | .28* | .52* | .43* | .43* | .18* | .13* | .04 | .12 | -.27* | .07 | .57* | -.53* | -.14* | .03 | – |
| 24 Tiredness(r) | .01 | -.16* | .21* | .19* | .11 | .10 | .37* | -.19* | .30* | .41* | .34* | .38* | .12 | .16* | .06 | .10 | -.20* | .08 | .49* | -.37* | -.09 | .07 | .67* |
Histograms
th <- ggplot(data=data, aes(x=trait.h)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait H-H") + xlab("") + theme_pub()
te <- ggplot(data=data, aes(x=trait.e)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait E") + xlab("") + theme_pub()
tx <- ggplot(data=data, aes(x=trait.x)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait X") + xlab("") + theme_pub()
ta <- ggplot(data=data, aes(x=trait.a)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait A") + xlab("") + theme_pub()
tc <- ggplot(data=data, aes(x=trait.c)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait C") + xlab("") + theme_pub()
to <- ggplot(data=data, aes(x=trait.o)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Trait O") + xlab("") + theme_pub()
sh <- ggplot(data=data, aes(x=state.h)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State H-H") + xlab("") + theme_pub()
se <- ggplot(data=data, aes(x=state.e)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State E") + xlab("") + theme_pub()
sx <- ggplot(data=data, aes(x=state.x)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State X") + xlab("") + theme_pub()
sa <- ggplot(data=data, aes(x=state.a)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State A") + xlab("") + theme_pub()
sc <- ggplot(data=data, aes(x=state.c)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State C") + xlab("") + theme_pub()
so <- ggplot(data=data, aes(x=state.o)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("State O") + xlab("") + theme_pub()
d <- ggplot(data=data, aes(x=dut)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Duty") + xlab("") + theme_pub()
i <- ggplot(data=data, aes(x=int)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Intellect") + xlab("") + theme_pub()
a <- ggplot(data=data, aes(x=adv)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Adversity") + xlab("") + theme_pub()
m <- ggplot(data=data, aes(x=mat)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Mating") + xlab("") + theme_pub()
o <- ggplot(data=data, aes(x=pos)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("pOsitivity") + xlab("") + theme_pub()
n <- ggplot(data=data, aes(x=neg)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Negativity") + xlab("") + theme_pub()
de <- ggplot(data=data, aes(x=dec)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Deception") + xlab("") + theme_pub()
s <- ggplot(data=data, aes(x=soc)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
ggtitle("Sociality") + xlab("") + theme_pub()
# (th | te | tx | ta | tc | to) /
# (sh | se | sx | sa | sc | so) /
# (d | i | a | m) /
# (o | n | de | s)
traits <- cowplot::plot_grid(th, te, tx, ta, tc, to, ncol=4)
states <- cowplot::plot_grid(sh, se, sx, sa, sc, so, ncol=4)
sits <- cowplot::plot_grid(d, i, a, m, o, n, de, s, ncol=4)
cowplot::plot_grid(traits, NULL, states, NULL, sits, ncol=1, rel_heights = c(1, 0.05, 1, 0.05, 1))
Figure 3: Histograms of the distributions of personality traits, personality states, and situation characteristics in the sample.
Manipulation Check
Share Decisions
Hypothesis
The behavior manipulation is related to participants’ actual behavior (i.e., number of share decisions and/or number of honest trials) such that participants in the ‘act honestly and agreeably’ condition make more share decisions and/or have more honest trials than participants in the ‘act dishonestly and disagreeably’ condition.
Descriptives
kable(describeBy(data$nCoop, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
row.names = FALSE,
caption="Descriptive statistics of the number of share decisions in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 63 | 6.41 | 3.95 | 6.0 | 6.08 | 4.45 | 1 | 15 | 14 | 0.50 | -0.82 |
| high agreeableness and honesty | low adversity and deception | 1 | 76 | 11.00 | 3.69 | 11.5 | 11.39 | 4.45 | 1 | 15 | 14 | -0.71 | -0.39 |
| low agreeableness and honesty | high adversity and deception | 1 | 49 | 3.92 | 3.48 | 3.0 | 3.27 | 2.97 | 1 | 15 | 14 | 1.78 | 2.76 |
| high agreeableness and honesty | high adversity and deception | 1 | 62 | 6.13 | 4.32 | 4.0 | 5.66 | 2.97 | 1 | 15 | 14 | 0.91 | -0.32 |
ANOVA
aov <- aov_car(nCoop ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and the number of share decisions.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 246 | 46.76 | .160 | <.001 |
| Situation conditions | 1, 246 | 54.89 | .182 | <.001 |
| Behavior x Situation conditions | 1, 246 | 5.72 | .023 | .018 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=nCoop)) +
theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") +
ggtitle("Means and distributions") + ylim(c(0,15)) +
geom_violin(aes(x = condition.sit, y = nCoop), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(m = mean(nCoop, na.rm=TRUE),
sd = sd(nCoop, na.rm=T)),
aes(x = condition.sit, y = m, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") +
ggtitle("Main effects and interactions") + ylim(c(0,15)) +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
scale_fill_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 4: Associations between the experimental conditions and the number of share decisions.
Honest Decisions
Hypothesis
The behavior manipulation is related to participants’ actual behavior (i.e., number of share decisions and/or number of honest trials) such that participants in the ‘act honestly and agreeably’ condition make more share decisions and/or have more honest trials than participants in the ‘act dishonestly and disagreeably’ condition.
Descriptives
kable(describeBy(data$nHon, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
row.names = FALSE,
caption="Descriptive statistics of the number of honest decisions in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 80 | 9.76 | 3.96 | 10.0 | 9.95 | 5.19 | 2 | 15 | 13 | -0.21 | -1.12 |
| high agreeableness and honesty | low adversity and deception | 1 | 78 | 12.09 | 2.78 | 12.5 | 12.38 | 3.71 | 5 | 15 | 10 | -0.64 | -0.66 |
| low agreeableness and honesty | high adversity and deception | 1 | 73 | 10.21 | 4.05 | 11.0 | 10.63 | 4.45 | 1 | 15 | 14 | -0.67 | -0.44 |
| high agreeableness and honesty | high adversity and deception | 1 | 66 | 10.83 | 3.16 | 11.0 | 11.00 | 4.45 | 4 | 15 | 11 | -0.24 | -1.03 |
ANOVA
hon.aov <- aov_car(nHon ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and the number of honest decisions.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 246 | 46.76 | .160 | <.001 |
| Situation conditions | 1, 246 | 54.89 | .182 | <.001 |
| Behavior x Situation conditions | 1, 246 | 5.72 | .023 | .018 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=nHon)) +
theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") +
ggtitle("Means and distributions") + ylim(c(0,15)) +
geom_violin(aes(x = condition.sit, y = nHon), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(nHon, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") +
ggtitle("Main effects and interactions") + ylim(c(0,15)) +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 5: Associations between the experimental conditions and the number of honest decisions.
State Agreeableness
Hypothesis
The behavior manipulation is also related to participants’ self-reported behavior (i.e., state Agreeableness and/or state Honesty-Humility) such that participants in the ‘act honestly and agreeably’ condition report higher levels of state Agreeableness and/or state Honesty-Humility than participants in the ‘act dishonestly and disagreeably’ condition.
Descriptives
kable(describeBy(data$state.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
row.names = FALSE,
caption="Descriptive statistics of state agreeableness in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 4.33 | 1.76 | 4.50 | 4.39 | 1.85 | 1.00 | 7 | 6.00 | -0.20 | -0.87 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 5.53 | 1.21 | 5.75 | 5.65 | 1.11 | 1.75 | 7 | 5.25 | -0.83 | 0.50 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 4.32 | 1.54 | 4.00 | 4.30 | 1.11 | 1.00 | 7 | 6.00 | 0.17 | -0.52 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 4.90 | 1.52 | 5.25 | 5.02 | 1.48 | 1.00 | 7 | 6.00 | -0.69 | -0.01 |
ANOVA
aov <- aov_car(state.a ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and state agreeableness.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 253 | 21.92 | .080 | <.001 |
| Situation conditions | 1, 253 | 2.86 | .011 | .092 |
| Behavior x Situation conditions | 1, 253 | 2.64 | .010 | .106 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=state.a)) +
theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") +
ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.a), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.a, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 6: Associations between the experimental conditions and state agreeableness.
State Honesty-Humility
Hypothesis
The behavior manipulation is also related to participants’ self-reported behavior (i.e., state Agreeableness and/or state Honesty-Humility) such that participants in the ‘act honestly and agreeably’ condition report higher levels of state Agreeableness and/or state Honesty-Humility than participants in the ‘act dishonestly and disagreeably’ condition.
Descriptives
kable(describeBy(data$state.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
row.names = FALSE,
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
caption="Descriptive statistics of state honesty-humility in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 3.69 | 2.00 | 3.38 | 3.62 | 2.41 | 1.00 | 7 | 6.00 | 0.27 | -1.22 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 5.55 | 1.33 | 5.50 | 5.69 | 1.85 | 2.25 | 7 | 4.75 | -0.68 | -0.49 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 3.45 | 1.84 | 3.25 | 3.32 | 2.22 | 1.00 | 7 | 6.00 | 0.46 | -0.88 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 5.03 | 1.65 | 5.50 | 5.17 | 1.85 | 1.00 | 7 | 6.00 | -0.64 | -0.51 |
ANOVA
aov <- aov_car(state.h ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and state honesty-humility.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 253 | 63.16 | .200 | <.001 |
| Situation conditions | 1, 253 | 3.14 | .012 | .077 |
| Behavior x Situation conditions | 1, 253 | 0.42 | .002 | .516 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=state.h)) +
theme_pub() + xlab("Situation conditions") + ylab("State honesty-humility score") +
ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.h), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.h, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State honesty-humility score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 7: Associations between the experimental conditions and state honesty-humility.
Adversity
Hypothesis
The situation manipulation is related to participants’ self-reported situation perceptions (i.e., Deception and/or Adversity of the situation) such that participants in the ‘trustworthy partner’ condition report lower levels of perceived Deception and/or Adversity than participants in the ‘untrustworthy opponent’ condition.
Descriptives
kable(describeBy(data$adv, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
row.names = FALSE,
caption="Descriptive statistics of adversity in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 83 | 2.37 | 1.47 | 2.00 | 2.19 | 1.48 | 1 | 7.00 | 6.00 | 0.84 | -0.20 |
| high agreeableness and honesty | low adversity and deception | 1 | 77 | 2.05 | 1.27 | 1.33 | 1.87 | 0.49 | 1 | 5.33 | 4.33 | 1.03 | -0.23 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 2.52 | 1.58 | 2.33 | 2.34 | 1.98 | 1 | 7.00 | 6.00 | 0.69 | -0.60 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 2.80 | 1.83 | 2.33 | 2.56 | 1.98 | 1 | 7.00 | 6.00 | 0.92 | -0.15 |
ANOVA
aov <- aov_car(adv ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and adversity.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(1, width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 300 | 0.01 | <.001> | .930 | </.001>
| Situation conditions | 1, 300 | 6.44 | .021 | .012 |
| Behavior x Situation conditions | 1, 300 | 2.90 | .010 | .090 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.beh, y=adv)) +
theme_pub() + xlab("Behavior conditions") + ylab("Adversity score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = adv), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(adv, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Adversity score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 8: Associations between the experimental conditions and adversity.
Deception
Hypothesis
The situation manipulation is related to participants’ self-reported situation perceptions (i.e., Deception and/or Adversity of the situation) such that participants in the ‘trustworthy partner’ condition report lower levels of perceived Deception and/or Adversity than participants in the ‘untrustworthy opponent’ condition.
Descriptives
kable(describeBy(data$dec, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
row.names = FALSE,
caption="Descriptive statistics of deception in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| low agreeableness and honesty | low adversity and deception | 1 | 82 | 5.46 | 1.34 | 5.67 | 5.58 | 1.48 | 2.67 | 7 | 4.33 | -0.49 | -1.00 |
| high agreeableness and honesty | low adversity and deception | 1 | 79 | 5.08 | 1.44 | 5.00 | 5.22 | 0.99 | 1.00 | 7 | 6.00 | -0.93 | 0.75 |
| low agreeableness and honesty | high adversity and deception | 1 | 75 | 5.47 | 1.37 | 6.00 | 5.60 | 1.48 | 2.00 | 7 | 5.00 | -0.54 | -0.71 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 5.39 | 1.50 | 5.67 | 5.57 | 1.48 | 1.00 | 7 | 6.00 | -0.98 | 0.52 |
ANOVA
aov <- aov_car(dec ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analysis of variance examining associations between experimental conditions and deception.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| Behavior conditions | 1, 300 | 2.11 | .007 | .147 |
| Situation conditions | 1, 300 | 0.97 | .003 | .326 |
| Behavior x Situation conditions | 1, 300 | 0.88 | .003 | .350 |
Visualization
# plot means and distributions
l <- ggplot(data, aes(x=condition.beh, y=dec)) +
theme_pub() + xlab("Behavior conditions") + ylab("Deception score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = dec), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(dec, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Deception score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))
Figure 9: Associations between the experimental conditions and deception.
Not Targeted Personality States and Situation Characteristics
Hypothesis
The manipulations are neither related to the reported levels of state openness, state conscientiousness, state extraversion, and state emotional stability nor to the reported levels of Duty, Intellect, Mating, pOsitivity, Negativity, and Sociality.
Descriptives
tab <- bind_rows(describeBy(data$state.e, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$state.x, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$state.c, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$state.o, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$dut, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$int, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$mat, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$pos, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$neg, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$soc, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],)
kable(tab,
row.names = FALSE,
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
caption="Descriptive statistics of the remaining personality states and situation characteristics in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
pack_rows("State Emotionality", 1, 4) %>%
pack_rows("State Extraversion", 5, 8) %>%
pack_rows("State Conscientiousness", 9, 12) %>%
pack_rows("State Openness", 13, 16) %>%
pack_rows("Duty", 17, 20) %>%
pack_rows("Intellect", 21, 24) %>%
pack_rows("Mating", 25, 28) %>%
pack_rows("pOsitivity", 29, 32) %>%
pack_rows("Negativity", 33, 36) %>%
pack_rows("Sociality", 37, 40)| behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| State Emotionality | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 2.94 | 1.80 | 3.0 | 2.70 | 1.48 | 1 | 7 | 6 | 0.82 | -0.09 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 2.46 | 1.64 | 2.0 | 2.20 | 1.48 | 1 | 7 | 6 | 1.15 | 0.49 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 2.90 | 1.89 | 2.0 | 2.67 | 1.48 | 1 | 7 | 6 | 0.64 | -0.73 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 2.41 | 1.54 | 2.0 | 2.22 | 1.48 | 1 | 7 | 6 | 0.92 | -0.07 |
| State Extraversion | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 4.07 | 1.83 | 4.0 | 4.09 | 1.48 | 1 | 7 | 6 | 0.06 | -0.82 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 4.12 | 1.84 | 4.0 | 4.15 | 1.48 | 1 | 7 | 6 | -0.04 | -0.85 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 3.70 | 1.89 | 4.0 | 3.63 | 1.48 | 1 | 7 | 6 | 0.22 | -0.91 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 3.58 | 2.08 | 3.0 | 3.49 | 1.48 | 1 | 7 | 6 | 0.46 | -1.05 |
| State Conscientiousness | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 4.87 | 1.83 | 5.0 | 5.07 | 1.48 | 1 | 7 | 6 | -0.72 | -0.40 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 5.54 | 1.23 | 5.0 | 5.60 | 1.48 | 3 | 7 | 4 | -0.23 | -1.14 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 5.00 | 1.87 | 5.0 | 5.24 | 1.48 | 1 | 7 | 6 | -0.74 | -0.36 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 5.51 | 1.44 | 6.0 | 5.65 | 1.48 | 1 | 7 | 6 | -0.98 | 0.98 |
| State Openness | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 4.73 | 1.89 | 5.0 | 4.91 | 1.48 | 1 | 7 | 6 | -0.70 | -0.44 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 5.60 | 1.26 | 6.0 | 5.71 | 1.48 | 2 | 7 | 5 | -0.62 | -0.35 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 4.92 | 1.73 | 5.0 | 5.10 | 1.48 | 1 | 7 | 6 | -0.64 | -0.36 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 5.12 | 1.60 | 5.0 | 5.24 | 1.48 | 1 | 7 | 6 | -0.44 | -0.71 |
| Duty | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 83 | 4.43 | 1.87 | 4.0 | 4.54 | 1.48 | 1 | 7 | 6 | -0.26 | -0.75 |
| high agreeableness and honesty | low adversity and deception | 1 | 79 | 4.23 | 1.85 | 4.0 | 4.28 | 1.48 | 1 | 7 | 6 | -0.26 | -0.89 |
| low agreeableness and honesty | high adversity and deception | 1 | 75 | 4.47 | 1.93 | 4.0 | 4.57 | 2.97 | 1 | 7 | 6 | -0.33 | -0.98 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 4.59 | 1.93 | 5.0 | 4.71 | 1.48 | 1 | 7 | 6 | -0.52 | -0.71 |
| Intellect | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 83 | 3.80 | 2.10 | 4.0 | 3.75 | 2.97 | 1 | 7 | 6 | -0.01 | -1.38 |
| high agreeableness and honesty | low adversity and deception | 1 | 77 | 3.51 | 1.84 | 4.0 | 3.46 | 1.48 | 1 | 7 | 6 | -0.02 | -1.20 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 3.58 | 1.91 | 4.0 | 3.52 | 2.97 | 1 | 7 | 6 | 0.14 | -1.20 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 3.88 | 2.23 | 4.0 | 3.86 | 2.97 | 1 | 7 | 6 | 0.03 | -1.41 |
| Mating | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 84 | 2.21 | 1.76 | 1.0 | 1.90 | 0.00 | 1 | 7 | 6 | 1.14 | 0.02 |
| high agreeableness and honesty | low adversity and deception | 1 | 79 | 2.43 | 1.79 | 2.0 | 2.17 | 1.48 | 1 | 7 | 6 | 1.00 | -0.17 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 2.05 | 1.56 | 1.0 | 1.74 | 0.00 | 1 | 7 | 6 | 1.59 | 1.70 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 2.51 | 2.06 | 1.0 | 2.20 | 0.00 | 1 | 7 | 6 | 0.98 | -0.49 |
| pOsitivity | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 84 | 3.75 | 1.96 | 4.0 | 3.69 | 2.97 | 1 | 7 | 6 | 0.12 | -1.12 |
| high agreeableness and honesty | low adversity and deception | 1 | 79 | 4.44 | 1.61 | 4.0 | 4.52 | 1.48 | 1 | 7 | 6 | -0.33 | -0.22 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 3.33 | 1.76 | 3.5 | 3.19 | 2.22 | 1 | 7 | 6 | 0.47 | -0.59 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 4.10 | 2.05 | 4.0 | 4.12 | 2.97 | 1 | 7 | 6 | -0.20 | -1.13 |
| Negativity | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 84 | 3.71 | 2.03 | 4.0 | 3.65 | 2.97 | 1 | 7 | 6 | 0.10 | -1.17 |
| high agreeableness and honesty | low adversity and deception | 1 | 79 | 3.01 | 1.90 | 3.0 | 2.85 | 2.97 | 1 | 7 | 6 | 0.45 | -1.05 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 3.83 | 2.19 | 4.0 | 3.79 | 2.97 | 1 | 7 | 6 | 0.04 | -1.40 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 3.68 | 2.28 | 4.0 | 3.61 | 2.97 | 1 | 7 | 6 | 0.19 | -1.49 |
| Sociality | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 84 | 4.14 | 1.99 | 4.0 | 4.18 | 2.97 | 1 | 7 | 6 | -0.29 | -1.07 |
| high agreeableness and honesty | low adversity and deception | 1 | 78 | 4.44 | 1.83 | 5.0 | 4.53 | 1.48 | 1 | 7 | 6 | -0.50 | -0.73 |
| low agreeableness and honesty | high adversity and deception | 1 | 76 | 4.16 | 2.01 | 4.0 | 4.19 | 2.97 | 1 | 7 | 6 | -0.22 | -1.19 |
| high agreeableness and honesty | high adversity and deception | 1 | 68 | 4.38 | 2.14 | 4.0 | 4.46 | 2.97 | 1 | 7 | 6 | -0.27 | -1.18 |
ANOVA
aov.e <- aov_car(state.e ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.x <- aov_car(state.x ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.c <- aov_car(state.c ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.o <- aov_car(state.o ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.dut <- aov_car(dut ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.int <- aov_car(int ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.mat <- aov_car(mat ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.pos <- aov_car(pos ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.neg <- aov_car(neg ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.soc <- aov_car(dec ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- bind_rows(as.data.frame(nice(aov.e, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.x, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.c, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.o, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.dut, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.int, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.mat, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.pos, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.neg, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
as.data.frame(nice(aov.soc, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
)
tab$p.value[tab$p.value=="<.001"] <- "<.001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- rep(c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions"),10)
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics.") %>%
pack_rows("DV: State Emotionality", 1, 3) %>%
pack_rows("DV: State Extraversion", 4, 6) %>%
pack_rows("DV: State Conscientiousness", 7, 9) %>%
pack_rows("DV: State Openness", 10, 12) %>%
pack_rows("DV: Duty", 13, 15) %>%
pack_rows("DV: Intellect", 16, 18) %>%
pack_rows("DV: Mating", 19, 21) %>%
pack_rows("DV: pOsitivity", 22, 24) %>%
pack_rows("DV: Negativity", 25, 27) %>%
pack_rows("DV: Sociality", 28, 30) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em")| Effect | df | F | ηp2 | p value |
|---|---|---|---|---|
| DV: State Emotionality | ||||
| Behavior conditions | 1, 253 | 5.10 | .020 | .025 |
| Situation conditions | 1, 253 | 0.05 | <.001 | .822 |
| Behavior x Situation conditions | 1, 253 | 0.00 | <.001 | .973 |
| DV: State Extraversion | ||||
| Behavior conditions | 1, 253 | 0.03 | <.001 | .866 |
| Situation conditions | 1, 253 | 3.64 | .014 | .057 |
| Behavior x Situation conditions | 1, 253 | 0.14 | <.001 | .711 |
| DV: State Conscientiousness | ||||
| Behavior conditions | 1, 253 | 8.41 | .032 | .004 |
| Situation conditions | 1, 253 | 0.06 | <.001 | .806 |
| Behavior x Situation conditions | 1, 253 | 0.15 | <.001 | .698 |
| DV: State Openness | ||||
| Behavior conditions | 1, 253 | 6.83 | .026 | .009 |
| Situation conditions | 1, 253 | 0.50 | .002 | .481 |
| Behavior x Situation conditions | 1, 253 | 2.67 | .010 | .104 |
| DV: Duty | ||||
| Behavior conditions | 1, 301 | 0.04 | <.001 | .846 |
| Situation conditions | 1, 301 | 0.82 | .003 | .367 |
| Behavior x Situation conditions | 1, 301 | 0.57 | .002 | .452 |
| DV: Intellect | ||||
| Behavior conditions | 1, 300 | 0.00 | <.001 | .975 |
| Situation conditions | 1, 300 | 0.12 | <.001 | .732 |
| Behavior x Situation conditions | 1, 300 | 1.62 | .005 | .204 |
| DV: Mating | ||||
| Behavior conditions | 1, 303 | 2.73 | .009 | .100 |
| Situation conditions | 1, 303 | 0.04 | <.001 | .851 |
| Behavior x Situation conditions | 1, 303 | 0.36 | .001 | .550 |
| DV: pOsitivity | ||||
| Behavior conditions | 1, 303 | 12.05 | .038 | <.001 |
| Situation conditions | 1, 303 | 3.24 | .011 | .073 |
| Behavior x Situation conditions | 1, 303 | 0.04 | <.001 | .848 |
| DV: Negativity | ||||
| Behavior conditions | 1, 303 | 3.17 | .010 | .076 |
| Situation conditions | 1, 303 | 2.63 | .009 | .106 |
| Behavior x Situation conditions | 1, 303 | 1.31 | .004 | .254 |
| DV: Sociality | ||||
| Behavior conditions | 1, 300 | 2.11 | .007 | .147 |
| Situation conditions | 1, 300 | 0.97 | .003 | .326 |
| Behavior x Situation conditions | 1, 300 | 0.88 | .003 | .350 |
Visualization
#### Personality States
### State Emotionality
# plot means and distributions
l1 <- ggplot(data, aes(x=condition.sit, y=state.e)) +
theme_pub() + xlab("Situation conditions") + ylab("State emotionality score") +
ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.e), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r1 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.e, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State emotionality score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
row1 <- (l1 | plot_spacer() | r1) + plot_layout(width=c(1,0.1,1))
### State Extraversion
# plot means and distributions
l2 <- ggplot(data, aes(x=condition.sit, y=state.x)) +
theme_pub() + xlab("Situation conditions") + ylab("State extraversion score") +
ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.x), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r2 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.x, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State extraversion score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
row2 <- (l2 | plot_spacer() | r2) + plot_layout(width=c(1,0.1,1))
### State Conscientiousness
# plot means and distributions
l3 <- ggplot(data, aes(x=condition.sit, y=state.c)) +
theme_pub() + xlab("Situation conditions") + ylab("State conscientiousness score") +
ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.c), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r3 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.c, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State conscientiousness score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
row3 <- (l3 | plot_spacer() | r3) + plot_layout(width=c(1,0.1,1))
### State Openness
# plot means and distributions
l4 <- ggplot(data, aes(x=condition.sit, y=state.o)) +
theme_pub() + xlab("Situation conditions") + ylab("State openness score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.sit, y = state.o), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.beh) +
facet_wrap(~condition.beh) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r4 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(state.o, na.rm=TRUE)),
aes(x = condition.sit, y = groups, color = condition.beh)) +
theme_pub() + xlab("Situation conditions") + ylab("State openness score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.beh) +
geom_line(aes(group = condition.beh), size=1.5) +
geom_point(size=2.5)
row4 <- (l4 | plot_spacer() | r4) + plot_layout(width=c(1,0.1,1))
### titles
e <- ggdraw() + draw_label("State Emotionality", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
x <- ggdraw() + draw_label("State Extraversion", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
c <- ggdraw() + draw_label("State Conscientiousness", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
o <- ggdraw() + draw_label("State Openness", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
### combine plots
e/
row1 /
x/
row2/
c/
row3/
o/
row4 + plot_layout(heights=rep(c(0.3,1),4)) #+ plot_annotation(tag_levels = c("A", "", "", 
Figure 10: Associations between the experimental conditions and the remaining personality states.
# "B", "", "",
# "C", "", "",
# "D", "", "")) #### Situation characteristics
### Duty
# plot means and distributions
l1 <- ggplot(data, aes(x=condition.beh, y=dut)) +
theme_pub() + xlab("Behavior conditions") + ylab("Duty score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = dut), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r1 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(dut, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Duty score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row1 <- (l1 | plot_spacer() | r1) + plot_layout(widths=c(1,0.1,1))
### Intellect
# plot means and distributions
l2 <- ggplot(data, aes(x=condition.beh, y=int)) +
theme_pub() + xlab("Behavior conditions") + ylab("Intellect score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = int), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r2 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(int, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Intellect score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row2 <- (l2 | plot_spacer() | r2) + plot_layout(widths=c(1,0.1,1))
### Mating
# plot means and distributions
l3 <- ggplot(data, aes(x=condition.beh, y=mat)) +
theme_pub() + xlab("Behavior conditions") + ylab("Mating score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = mat), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r3 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(mat, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Mating score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row3 <- (l3 | plot_spacer() | r3) + plot_layout(widths=c(1,0.1,1))
### pOsitivity
# plot means and distributions
l4 <- ggplot(data, aes(x=condition.beh, y=pos)) +
theme_pub() + xlab("Behavior conditions") + ylab("pOsitivity score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = pos), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r4 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(pos, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("pOsitivity score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row4 <- (l4 | plot_spacer() | r4) + plot_layout(widths=c(1,0.1,1))
### Negativity
# plot means and distributions
l5 <- ggplot(data, aes(x=condition.beh, y=neg)) +
theme_pub() + xlab("Behavior conditions") + ylab("Negativity score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = neg), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r5 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(neg, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Negativity score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row5 <- (l5 | plot_spacer() | r5) + plot_layout(widths=c(1,0.1,1))
### Sociality
# plot means and distributions
l6 <- ggplot(data, aes(x=condition.beh, y=soc)) +
theme_pub() + xlab("Behavior conditions") + ylab("Sociality score") + ggtitle("Means and distributions") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
geom_violin(aes(x = condition.beh, y = soc), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
stat_summary(fun = mean, geom = "crossbar", width = 0.75,
position = position_dodge(width = .75), colour="#808080") +
geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) +
scale_colour_manual(values=cols.sit) +
facet_wrap(~condition.sit) +
theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
theme(legend.position="", strip.text.x = element_text(size=9,face="bold"))
# plot interaction diagram
r6 <- ggplot(data %>%
group_by(condition.sit, condition.beh) %>%
summarise(groups = mean(soc, na.rm=TRUE)),
aes(x = condition.beh, y = groups, color = condition.sit)) +
theme_pub() + xlab("Behavior conditions") + ylab("Sociality score") +
scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
ggtitle("Main effects and interactions") +
theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") +
scale_colour_manual(values=cols.sit) +
geom_line(aes(group = condition.sit), size=1.5) +
geom_point(size=2.5)
row6 <- (l6 | plot_spacer() | r6) + plot_layout(widths=c(1,0.1,1))
### titles
dut <- ggdraw() + draw_label("Duty", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
int <- ggdraw() + draw_label("Intellect", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
mat <- ggdraw() + draw_label("Mating", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
pos <- ggdraw() + draw_label("pOsitivity", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
neg <- ggdraw() + draw_label("Negativity", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
soc <- ggdraw() + draw_label("Sociality", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1)
### combine plots
dut/
row1 /
int/
row2/
mat/
row3/
pos/
row4/
neg/
row5/
soc/
row6 + plot_layout(heights=rep(c(0.3,1),6)) #+ plot_annotation(tag_levels = c("A", "", "", 
Figure 11: Associations between the experimental conditions and the remaining situation characteristics.
# "B", "", "",
# "C", "", "",
# "D", "", "",
# "E", "", "",
# "F", "", "")) Replication of Main Effects
Hypothesis and Analystic Strategy
Hypothesis
H2a: Higher levels of reported state Agreeableness and state Honesty-Humility are associated with more positive mood.
H2b: Higher levels of perceived Deception and Adversity are associated with more negative mood.
Analytic Strategy
We estimated a multiple regression model with the positive affect dimension as the DV and grand-mean centered state Agreeableness, state Honesty-Humility, perceived Adversity, and perceived Deception as IVs. We will add trait Agreeableness and trait Honesty-Humility and their interactions with their corresponding personality states as predictors in a next step to examine between-person differences in the associations between personality states and mood. We included the experimental conditions as covariates in the regressions to control for the hierarchical data structure with participants nested in experimental conditions.
Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant’s and the computer’s points) and the DV by including these variables as covariates in the models changed the results.
Regression Models
## estimate models
h2 <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c +
condition.beh + condition.sit, data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) +
condition.beh + condition.sit, data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
## estimate models
h2a <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c +
points.c + points.diff + condition.beh + condition.sit, data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2a.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) +
scale(points.c) + scale(points.diff) + condition.beh + condition.sit, data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
#summary(h2a)
h2a.2 <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c +
points.c + points.diff + condition.beh + condition.sit +
state.a.c*trait.a.c + state.h.c*trait.h.c, data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2a.2.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) +
scale(points.c) + scale(points.diff) + condition.beh + condition.sit +
scale(state.a)*scale(trait.a) + scale(state.h)*scale(trait.h), data=data,
contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
#summary(h2a.2)
## build table
tab <- data.frame(predictor=rep(NA, nrow(summary(h2a.2)$coefficients)+1), Model1=NA, Model2=NA, Model3=NA)
tab[,1] <- c("(Intercept)", "State Agreeableness", "State Honesty-Humility", "Adversity", "Deception",
"Points earned", "Difference in points", "Behavior conditions", "Situation conditions",
"Trait Agreeableness", "Trait Honesty", "Trait A x State A", "Trait HH x State HH", "")
tab[c(1,2,3,4,5,8,9),2] <- c(paste0(printnum(summary(h2.z)$coefficients[,1]), " ",
stars(summary(h2.z)$coefficients[,4]),
"<br>[",printnum(confint(h2.z)[,1]), ", ",
printnum(confint(h2.z)[,2]), "]"))
tab[1:nrow(summary(h2a)$coefficients),3] <- c(paste0(printnum(summary(h2a.z)$coefficients[,1]), " ",
stars(summary(h2a.z)$coefficients[,4]),
"<br>[",printnum(confint(h2a.z)[,1]), ", ",
printnum(confint(h2a.z)[,2]), "]"))
tab[1:nrow(summary(h2a.2)$coefficients),4] <- c(paste0(printnum(summary(h2a.2.z)$coefficients[,1]), " ",
stars(summary(h2a.2.z)$coefficients[,4]),
"<br>[",printnum(confint(h2a.2.z)[,1]), ", ",
printnum(confint(h2a.2.z)[,2]), "]"))
tab[nrow(summary(h2a.2)$coefficients)+1,2:4] <- c(print.clean(apa_print(h2.z)$estimate$modelfit$r2),
paste0(print.clean(apa_print(h2a.z)$estimate$modelfit$r2),
"<br>Δ *R^2^* = ",
printnum(summary(h2a.z)$r.squared -
summary(h2.z)$r.squared),
", *F*(", anova(h2.z, h2a.z)[2,3], ", ",
anova(h2.z, h2a.z)[2,1], ") = ",
printnum(anova(h2.z, h2a.z)[2,5]), ", *p* ",
printp(anova(h2.z, h2a.z)[2,6],
add_equals = T)),
paste0(print.clean(apa_print(h2a.2.z)$estimate$modelfit$r2),
"<br>Δ *R^2^* = ",
printnum(summary(h2a.2.z)$r.squared -
summary(h2a.z)$r.squared),
", *F*(", anova(h2a.z, h2a.2.z)[2,3], ", ",
anova(h2a.z, h2a.2.z)[2,1], ") = ",
printnum(anova(h2a.z, h2a.2.z)[2,5]), ", *p* ",
printp(anova(h2a.z, h2a.2.z)[2,6],
add_equals = T)))
## print table
kable(
tab
, align = c("l", "c", "c", "c")
, col.names = c("Predictor", "Model 1", "Model 2", "Model 3")
, caption = "Multiple regression models of associations between positive affect and state agreeableness, state honesty-humility, deception, and adversity controlling for the outcome of the game and the experimental groups."
, escape = FALSE
, row.names = FALSE
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
column_spec(1, width = "15em") %>%
pack_rows("Standardized Coefficients", 1, nrow(summary(h2a.2)$coefficients)) %>%
pack_rows("Model Fit", nrow(summary(h2a.2)$coefficients)+1, nrow(summary(h2a.2)$coefficients)+1) %>%
footnote(general="Values standardized regression coefficients, values in brackets represent 95% confidence intervals of the regression coefficients")| Predictor | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| Standardized Coefficients | |||
| (Intercept) |
0.02 [-0.09, 0.13] |
0.03 [-0.08, 0.14] |
0.04 [-0.07, 0.15] |
| State Agreeableness |
0.26 ** [0.07, 0.46] |
0.25 * [0.05, 0.44] |
0.23 * [0.03, 0.43] |
| State Honesty-Humility |
0.25 * [0.04, 0.46] |
0.32 ** [0.10, 0.54] |
0.32 ** [0.10, 0.55] |
| Adversity |
-0.21 *** [-0.32, -0.09] |
-0.21 *** [-0.32, -0.09] |
-0.25 *** [-0.38, -0.12] |
| Deception |
-0.05 [-0.17, 0.07] |
-0.03 [-0.15, 0.09] |
-0.01 [-0.13, 0.11] |
| Points earned |
-0.08 [-0.45, 0.29] |
-0.09 [-0.46, 0.29] |
|
| Difference in points |
0.21 [-0.11, 0.52] |
0.22 [-0.10, 0.54] |
|
| Behavior conditions |
-0.04 [-0.16, 0.08] |
-0.07 [-0.19, 0.05] |
-0.07 [-0.20, 0.05] |
| Situation conditions |
0.01 [-0.09, 0.12] |
-0.02 [-0.21, 0.16] |
-0.03 [-0.22, 0.16] |
| Trait Agreeableness |
0.00 [-0.11, 0.11] |
||
| Trait Honesty |
-0.11 [-0.24, 0.02] |
||
| Trait A x State A |
-0.03 [-0.13, 0.07] |
||
| Trait HH x State HH |
0.09 [-0.03, 0.21] |
||
| Model Fit | |||
| R2 = .33, 90% CI [0.24, 0.40] |
R2 = .35, 90% CI [0.24, 0.41] Δ R2 = 0.01, F(2, 226) = 2.17, p = .117 |
R2 = .36, 90% CI [0.24, 0.41] Δ R2 = 0.01, F(4, 222) = 0.98, p = .421 |
|
| Note: | |||
| Values standardized regression coefficients, values in brackets represent 95% confidence intervals of the regression coefficients | |||
Visualization
a <- ggplot(data, aes(x=state.a, y=mood.gb)) +
theme_pub() + xlab("State Agreeableness") + ylab("Positive affect") +
scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
geom_jitter(size=2.5, alpha=.5, color=cols.beh[1]) +
geom_smooth(method="lm", color="black", size=1.2)
b <- ggplot(data, aes(x=state.h, y=mood.gb)) +
theme_pub() + xlab("State Honesty-Humility") + ylab("Positive affect") +
scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
geom_jitter(size=2.5, alpha=.5, color=cols.beh[2]) +
geom_smooth(method="lm", color="black", size=1.2)
c <- ggplot(data, aes(x=adv, y=mood.gb)) +
theme_pub() + xlab("Adversity") + ylab("Positive affect") +
scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
geom_jitter(size=2.5, alpha=.5, color=cols.sit[1]) +
geom_smooth(method="lm", color="black", size=1.2)
d <- ggplot(data, aes(x=dec, y=mood.gb)) +
theme_pub() + xlab("Deception") + ylab("Positive affect") +
scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
geom_jitter(size=2.5, alpha=.5, color=cols.sit[2]) +
geom_smooth(method="lm", color="black", size=1.2)
r1 <- (a + plot_spacer() + b) + plot_layout(widths=c(1,0.1,1))
r2 <- (c + plot_spacer() + d) + plot_layout(widths=c(1,0.1,1))
r1/
plot_spacer() /
r2 + plot_annotation(tag_level="A") + plot_layout(heights=c(1,0.1,1))
Figure 12: Associations between positive affect and state agreeableness (A), state honesty-humility (B), adversity (C), and deception (D).
Conclusion
Overall, the data mostly supported Hypothesis 2:
Higher levels of state agreeableness, β = 0.23, 95% CI [0.03, 0.43], and state honesty-humility, β = 0.32, 95% CI [0.10, 0.55], were significantly associated with more positive affect within groups
Higher levels of perceived adversity, β = -0.25, 95% CI [-0.38, -0.12], but not deception, β = -0.01, 95% CI [-0.13, 0.11], were significantly associated with less positive affect within groups
Trait agreeableness and trait adversity were not significantly associated with positive affect and neither were their interactions with the respective personality traits
Congruence and Positive Affect
Hypothesis & Analytic Strategy
Hypothesis
H3a: Congruence between personality trait and personality state is associated with positive affect such that trait-congruent personality states are related to more positive mood than trait-incongruent personality states.
H4a: Congruence between personality state and situation characteristic is associated with positive affect such that situation-congruent personality states are related to more positive mood than situation-incongruent personality states.
Analytic Strategy
Step 1: Analysis of Variance
We conducted a median split of trait Agreeableness / trait Honesty-Humility and conduct variance analyses with positive affect as DV and the variables representing behavior conditions and median-split personality traits and their interaction as IVs. This replicates analyses performed in Zelenski et al. (2012).
Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant’s and the computer’s points) and the DV by including these variables as covariates in the models changed the results.
Step 2: Response Surface Analysis
We conducted response surface analyses with midpoint-centered personality state and midpoint-centered personality trait as IVs and positive affect as DV. We included the experimental conditions as covariates in the polynomial regressions to control for the hierarchical data structure with participants nested in experimental conditions.
Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant’s and the computer’s points) and the DV by including these variables as covariates in the models changed the results.
Analysis of Variance
Trait–State Congruence: Agreeableness
Model without Covariates
aov.a <- aov_car(mood.gb ~ trait.a.ms * condition.beh + condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.a, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions",
"Trait x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 250 | 2.29 | .009 | .132 |
| Behavior conditions | 1, 250 | 12.89 | .049 | < .001 |
| Situation conditions | 1, 250 | 3.22 | .013 | .074 |
| Trait x Behavior conditions | 1, 250 | 0.02 | <.001 | .894 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Model with Covariates
lm.a <- lm(mood.gb ~ trait.a.ms * condition.beh + points.c + points.diff + condition.sit, data=data,
contrasts = list(condition.sit=contr.sum, trait.a.ms=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.a, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned",
"Difference in points", "Situation conditions", "Trait x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 248 | 2.17 | .009 | .142 |
| Behavior conditions | 1, 248 | 7.86 | .031 | .005 |
| Points earned | 1, 248 | 0.53 | .002 | .466 |
| Difference in points | 1, 248 | 1.06 | .004 | .305 |
| Situation conditions | 1, 248 | 0.55 | .002 | .458 |
| Trait x Behavior conditions | 1, 248 | 0.01 | .000 | .944 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Trait–State Congruence: Honesty
Model without Covariates
aov.h <- aov_car(mood.gb ~ trait.h.ms * condition.beh + condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.h, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions",
"Trait x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 250 | 0.00 | <.001 | .958 |
| Behavior conditions | 1, 250 | 14.09 | .053 | < .001 |
| Situation conditions | 1, 250 | 2.87 | .011 | .092 |
| Trait x Behavior conditions | 1, 250 | 2.28 | .009 | .132 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Model with Covariates
lm.h <- lm(mood.gb ~ trait.h.ms * condition.beh + points.c + points.diff + condition.sit, data=data,
contrasts = list(condition.sit=contr.sum, trait.h.ms=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.h, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned",
"Difference in points", "Situation conditions", "Trait x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 248 | 0.01 | .000 | .925 |
| Behavior conditions | 1, 248 | 8.85 | .034 | .003 |
| Points earned | 1, 248 | 0.60 | .002 | .438 |
| Difference in points | 1, 248 | 1.06 | .004 | .305 |
| Situation conditions | 1, 248 | 0.38 | .002 | .537 |
| Trait x Behavior conditions | 1, 248 | 2.02 | .008 | .156 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
State–Situation Congruence
Model without Covariates
aov.sit <- aov_car(mood.gb ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.sit, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions",
"Situation x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between the situation conditions and the behavior manipulation and positive affect.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="Situation conditions and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Behavior conditions | 1, 251 | 13.56 | .051 | < .001 |
| Situation conditions | 1, 251 | 2.96 | .012 | .086 |
| Situation x Behavior conditions | 1, 251 | 1.56 | .006 | .212 |
| Note: | ||||
| Situation conditions and behavior conditions were effect-coded. |
Model with Covariates
data$points.c <- scale(data$points, scale=FALSE)
lm.sit <- lm(mood.gb ~ condition.sit * condition.beh + points.c + points.diff, data=data,
contrasts = list(condition.sit=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.sit, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Situation conditions", "Behavior conditions", "Points earned",
"Difference in points", "Situation x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="Situation conditions and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Situation conditions | 1, 249 | 0.62 | .002 | .433 |
| Behavior conditions | 1, 249 | 8.32 | .032 | .004 |
| Points earned | 1, 249 | 0.38 | .002 | .536 |
| Difference in points | 1, 249 | 0.88 | .004 | .349 |
| Situation x Behavior conditions | 1, 249 | 1.33 | .005 | .250 |
| Note: | ||||
| Situation conditions and behavior conditions were effect-coded. |
Response Surface Analysis
contrasts(data$condition.sit) <- contr.sum(2)
contrasts(data$condition.beh) <- contr.sum(2)
data$trait.a.long <- 6*(data$trait.a-1)/4+1
data$trait.h.long <- 6*(data$trait.h-1)/4+1
data$trait.a.long.mc <- data$trait.a.long-4
data$trait.h.long.mc <- data$trait.h.long-4
h3a.rsa.a <- RSA(mood.gb ~ state.a.mc*trait.a.long.mc, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h3a.rsa.h <- RSA(mood.gb ~ state.h.mc*trait.h.long.mc, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.a.a <- RSA(mood.gb ~ state.a.mc*adv.mc.r, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.a.d <- RSA(mood.gb ~ state.a.mc*dec.mc.r, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.h.a <- RSA(mood.gb ~ state.h.mc*adv.mc.r, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.h.d <- RSA(mood.gb ~ state.h.mc*dec.mc.r, data=data,
model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))Trait–State Congruence: Agreeableness
tab <- getPar(h3a.rsa.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Trait A", "State A<sup>2</sup>", "State A x Trait A", "Trait A<sup>2</sup>", "Behavior conditions", "Situation conditions")
## print table
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align = c("l", "r", "r", "r", "r", "r")
, col.names = c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
, caption = "Parameters of the response surface analysis of trait agreeableness and state agreeableness predicting positive affect."
, escape = FALSE
) %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "15em") | Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.10 | [2.56, 3.64] | 2.98 | < .001 |
| State A | b1 | 0.28 | [0.19, 0.38] | 0.43 | < .001 |
| Trait A | b2 | 0.01 | [-0.15, 0.17] | 0.01 | .906 |
| State A2 | b3 | 0.04 | [0.00, 0.08] | 0.14 | .035 |
| State A x Trait A | b4 | -0.01 | [-0.09, 0.06] | -0.02 | .704 |
| Trait A2 | b5 | 0.01 | [-0.07, 0.09] | 0.01 | .808 |
| Behavior conditions | cv1 | 0.19 | [-0.04, 0.41] | 0.09 | .107 |
| Situation conditions | cv2 | -0.10 | [-0.32, 0.13] | -0.05 | .405 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.29 | [0.12, 0.46] | 0.44 | .001 |
| a2:=b3+b4+b5 | a2 | 0.04 | [-0.05, 0.13] | 0.12 | .404 |
| a3:=b1-b2 | a3 | 0.27 | [0.07, 0.48] | 0.42 | .008 |
| a4:=b3-b4+b5 | a4 | 0.07 | [-0.06, 0.19] | 0.17 | .289 |
| a5:=b3-b5 | a5 | 0.03 | [-0.06, 0.13] | 0.12 | .490 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
Trait–State Congruence: Honesty
tab <- getPar(h3a.rsa.h, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Trait HH", "State HH<sup>2</sup>", "State HH x Trait HH", "Trait HH<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of trait honesty-humility and state honesty-humility predicting positive affect."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.60 | [3.06, 4.14] | 3.45 | < .001 |
| State HH | b1 | 0.20 | [0.09, 0.32] | 0.37 | .001 |
| Trait HH | b2 | -0.13 | [-0.32, 0.06] | -0.14 | .186 |
| State HH2 | b3 | 0.00 | [-0.04, 0.03] | -0.01 | .902 |
| State HH x Trait HH | b4 | 0.04 | [-0.02, 0.10] | 0.15 | .176 |
| Trait HH2 | b5 | 0.05 | [-0.03, 0.12] | 0.13 | .224 |
| Behavior conditions | cv1 | 0.04 | [-0.21, 0.29] | 0.02 | .747 |
| Situation conditions | cv2 | -0.09 | [-0.32, 0.13] | -0.04 | .423 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.07 | [-0.18, 0.33] | 0.23 | .576 |
| a2:=b3+b4+b5 | a2 | 0.08 | [-0.01, 0.17] | 0.27 | .068 |
| a3:=b1-b2 | a3 | 0.33 | [0.15, 0.51] | 0.51 | < .001 |
| a4:=b3-b4+b5 | a4 | 0.00 | [-0.10, 0.11] | -0.03 | .941 |
| a5:=b3-b5 | a5 | -0.05 | [-0.13, 0.04] | -0.13 | .272 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Agreeableness and Adversity
tab <- getPar(h4a.rsa.a.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Adv(r)", "State A<sup>2</sup>", "State A x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state agreeableness and adversity predicting positive affect."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 2.81 | [2.29, 3.34] | 2.70 | < .001 |
| State A | b1 | 0.23 | [0.11, 0.35] | 0.35 | < .001 |
| Adv(r) | b2 | 0.05 | [-0.07, 0.17] | 0.08 | .420 |
| State A2 | b3 | 0.03 | [-0.01, 0.08] | 0.11 | .108 |
| State A x Adv(r) | b4 | 0.02 | [-0.04, 0.07] | 0.06 | .546 |
| Adv(r)2 | b5 | 0.03 | [-0.01, 0.08] | 0.11 | .187 |
| Behavior conditions | cv1 | 0.20 | [-0.02, 0.41] | 0.09 | .075 |
| Situation conditions | cv2 | -0.04 | [-0.25, 0.18] | -0.02 | .743 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.28 | [0.09, 0.47] | 0.43 | .004 |
| a2:=b3+b4+b5 | a2 | 0.08 | [0.01, 0.15] | 0.28 | .026 |
| a3:=b1-b2 | a3 | 0.18 | [0.04, 0.32] | 0.28 | .014 |
| a4:=b3-b4+b5 | a4 | 0.05 | [-0.04, 0.14] | 0.15 | .292 |
| a5:=b3-b5 | a5 | 0.00 | [-0.06, 0.07] | 0.00 | .916 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Agreeableness and Deception
tab <- getPar(h4a.rsa.a.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Dec(r)", "State A<sup>2</sup>", "State A x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state agreeableness and deception predicting positive affect."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.22 | [2.65, 3.80] | 3.09 | < .001 |
| State A | b1 | 0.26 | [0.16, 0.36] | 0.40 | < .001 |
| Dec(r) | b2 | 0.07 | [-0.05, 0.19] | 0.10 | .254 |
| State A2 | b3 | 0.04 | [-0.01, 0.08] | 0.12 | .088 |
| State A x Dec(r) | b4 | -0.02 | [-0.07, 0.03] | -0.07 | .372 |
| Dec(r)2 | b5 | 0.00 | [-0.05, 0.04] | -0.01 | .882 |
| Behavior conditions | cv1 | 0.17 | [-0.06, 0.40] | 0.08 | .152 |
| Situation conditions | cv2 | -0.08 | [-0.30, 0.15] | -0.04 | .504 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.33 | [0.18, 0.48] | 0.49 | < .001 |
| a2:=b3+b4+b5 | a2 | 0.01 | [-0.06, 0.08] | 0.03 | .749 |
| a3:=b1-b2 | a3 | 0.19 | [0.02, 0.36] | 0.30 | .026 |
| a4:=b3-b4+b5 | a4 | 0.06 | [-0.01, 0.12] | 0.18 | .092 |
| a5:=b3-b5 | a5 | 0.04 | [-0.03, 0.12] | 0.13 | .287 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Honesty and Adversity
tab <- getPar(h4a.rsa.h.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Adv(r)", "State HH<sup>2</sup>", "State HH x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state honesty-humility and adversity predicting positive affect."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.24 | [2.71, 3.77] | 3.12 | < .001 |
| State HH | b1 | 0.23 | [0.13, 0.33] | 0.42 | < .001 |
| Adv(r) | b2 | 0.08 | [-0.05, 0.21] | 0.12 | .248 |
| State HH2 | b3 | -0.02 | [-0.05, 0.02] | -0.05 | .417 |
| State HH x Adv(r) | b4 | 0.02 | [-0.03, 0.06] | 0.07 | .486 |
| Adv(r)2 | b5 | 0.04 | [-0.01, 0.09] | 0.15 | .095 |
| Behavior conditions | cv1 | 0.01 | [-0.22, 0.25] | 0.01 | .909 |
| Situation conditions | cv2 | -0.03 | [-0.25, 0.19] | -0.01 | .779 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.31 | [0.11, 0.50] | 0.54 | .002 |
| a2:=b3+b4+b5 | a2 | 0.04 | [-0.03, 0.11] | 0.17 | .222 |
| a3:=b1-b2 | a3 | 0.15 | [0.02, 0.28] | 0.30 | .025 |
| a4:=b3-b4+b5 | a4 | 0.01 | [-0.05, 0.08] | 0.03 | .725 |
| a5:=b3-b5 | a5 | -0.06 | [-0.13, 0.01] | -0.20 | .113 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Honesty and Deception
tab <- getPar(h4a.rsa.h.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Dec(r)", "State HH<sup>2</sup>", "State HH x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state honesty-humility and deception predicting positive affect."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.64 | [3.10, 4.19] | 3.49 | < .001 |
| State HH | b1 | 0.29 | [0.18, 0.39] | 0.53 | < .001 |
| Dec(r) | b2 | 0.07 | [-0.03, 0.17] | 0.10 | .146 |
| State HH2 | b3 | 0.00 | [-0.04, 0.03] | -0.01 | .894 |
| State HH x Dec(r) | b4 | 0.01 | [-0.04, 0.05] | 0.03 | .767 |
| Dec(r)2 | b5 | 0.03 | [-0.02, 0.07] | 0.08 | .262 |
| Behavior conditions | cv1 | 0.00 | [-0.25, 0.25] | 0.00 | .990 |
| Situation conditions | cv2 | -0.10 | [-0.33, 0.12] | -0.05 | .358 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.36 | [0.24, 0.48] | 0.63 | < .001 |
| a2:=b3+b4+b5 | a2 | 0.03 | [-0.04, 0.10] | 0.10 | .376 |
| a3:=b1-b2 | a3 | 0.21 | [0.05, 0.38] | 0.43 | .012 |
| a4:=b3-b4+b5 | a4 | 0.02 | [-0.05, 0.09] | 0.05 | .658 |
| a5:=b3-b5 | a5 | -0.03 | [-0.09, 0.04] | -0.09 | .400 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
Visualization
ANOVA Interaction Plots
p1 <- interactions::cat_plot(lm.a, pred=condition.beh, modx=trait.a.ms,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Positive Affect",
main.title = "Trait Agreeableness x Behavior Conditions",
legend.main = "Trait Agreeableness (median-split)",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p2 <- interactions::cat_plot(lm.h, pred=condition.beh, modx=trait.h.ms,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Positive Affect",
main.title = "Trait Honesty x Behavior Conditions",
legend.main = "Trait Honesty (median-split)",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p3 <- interactions::cat_plot(lm.sit, pred=condition.beh, modx=condition.sit,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Positive Affect",
main.title = "Situation x Behavior Conditions",
legend.main = "Situation Conditions",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p1 / plot_spacer() / p2 / plot_spacer() / p3 + plot_layout(heights=c(1,0.1, 1, 0.1,1)) + plot_annotation(tag_levels = "A")
Figure 13: Interaction plots of interactions between median-split personality traits, behavior conditions, and situation conditions predicting positive affect.
Response Surface Plots
a <- plot(h3a.rsa.a, xlab="State Agreeableness", ylab="Trait Agreeableness", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Agreeableness",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F, pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
b <- plot(h3a.rsa.h, xlab="State Honesty", ylab="Trait Honesty", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait--State Congruence: Honesty-humility",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
c <- plot(h4a.rsa.a.a, xlab="State Agreeableness", ylab="Adversity(r)", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Adversity",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
d <- plot(h4a.rsa.h.a, xlab="State Honesty", ylab="Adversity(r)", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Adversity",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
e <- plot(h4a.rsa.a.d, xlab="State Agreeableness", ylab="Deception(r)", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Deception",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
f <- plot(h4a.rsa.h.d, xlab="State Honesty", ylab="Deception(r)", zlab="Positive affect",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Deception",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
## combine plots
cowplot::plot_grid(a,b,c,d,e,f, ncol = 2, labels="AUTO", label_size = 12)
Figure 14: Response surface plots of the association between trait–state congruence, state–situation congruence, and positive affect. Adversity and deception were reverse-coded (indicated by (r)) such that higher values indicate less adversity and decpetion, respectively. The response surface parameters are listed below the titles. The blue lines represent the line of congruence (LOC; i.e., X = Y) and the line of incongruence (LOIC; i.e., X = -Y). The black lines represent the range of observed values as a twodimensional boxplot.
Conclusion
Overall, the data did not support the congruence hypotheses with regard to positive affect:
Analyses of variance did not yield any significant interaction effects (all Fs < 2.28, all ps > .132)
Only the behavior conditions were significantly associated with positive affect such that participants in the high agreeableness and honesty-condition (M = 3.83, SD = 0.87) reported significantly more positive affect than participants in the low agreeableness and honesty-condition (M = 3.36, SD = 1.14, d = 0.47, 95% CI [0.21, 0.72])
Polynomial regressions also showed that only higher levels of state agreeableness and state honesty-humility (but not interactions of fit patterns) were significantly associated with more positive affect across all models (bs ranging from 0.21 to 0.29)
In summary, across both analysis of variance and response surface analysis, neither interactions nor fit patterns were significantly associated with positive affect
Congruence and Tiredness
Hypothesis & Analytic Strategy
Hypothesis
H3a: Congruence between personality trait and personality state is associated with tiredness such that trait-congruent personality states are related to less tiredness than trait-incongruent personality states.
H4a: Congruence between personality state and situation characteristic is associated with tiredness such that situation-congruent personality states are related to less tiredness than situation-incongruent personality states.
Analytic Strategy
Step 1: Analysis of Variance
We conducted a median split of trait Agreeableness / trait Honesty-Humility and conduct variance analyses with reverse-coded tiredness as DV and the variables representing behavior conditions and median-split personality traits and their interaction as IVs. This replicates analyses performed in Zelenski et al. (2012).
Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant’s and the computer’s points) and the DV by including these variables as covariates in the models changed the results.
Step 2: Response Surface Analysis
We conducted response surface analyses with midpoint-centered personality state and midpoint-centered personality trait as IVs and reverse-coded tiredness as DV. We included the experimental conditions as covariates in the polynomial regressions to control for the hierarchical data structure with participants nested in experimental conditions.
Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant’s and the computer’s points) and the DV by including these variables as covariates in the models changed the results.
Analysis of Variance
Trait–State Congruence: Agreeableness
Model without Covariates
aov.a <- aov_car(mood.at ~ trait.a.ms * condition.beh + condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.a, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions",
"Trait x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 250 | 6.25 | .024 | .013 |
| Behavior conditions | 1, 250 | 5.84 | .023 | .016 |
| Situation conditions | 1, 250 | 0.38 | .002 | .537 |
| Trait x Behavior conditions | 1, 250 | 1.82 | .007 | .179 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Model with Covariates
data$points.c <- scale(data$points, scale=FALSE)
lm.a <- lm(mood.at ~ trait.a.ms * condition.beh + points.c + points.diff + condition.sit, data=data,
contrasts = list(condition.sit=contr.sum, trait.a.ms=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.a, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned",
"Difference in points", "Situation conditions", "Trait x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 248 | 6.15 | .024 | .014 |
| Behavior conditions | 1, 248 | 4.54 | .018 | .034 |
| Points earned | 1, 248 | 0.54 | .002 | .463 |
| Difference in points | 1, 248 | 0.38 | .002 | .538 |
| Situation conditions | 1, 248 | 0.06 | .000 | .810 |
| Trait x Behavior conditions | 1, 248 | 1.77 | .007 | .184 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Trait–State Congruence: Honesty
Model without Covariates
aov.h <- aov_car(mood.at ~ trait.h.ms * condition.beh + condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.h, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions",
"Trait x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 250 | 0.01 | <.001 | .908 |
| Behavior conditions | 1, 250 | 6.87 | .027 | .009 |
| Situation conditions | 1, 250 | 0.18 | <.001 | .669 |
| Trait x Behavior conditions | 1, 250 | 0.80 | .003 | .372 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
Model with Covariates
data$points.c <- scale(data$points, scale=FALSE)
lm.h <- lm(mood.at ~ trait.h.ms * condition.beh + points.c + points.diff + condition.sit, data=data,
contrasts = list(condition.sit=contr.sum, trait.h.ms=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.h, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned",
"Difference in points", "Situation conditions", "Trait x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Trait (median-split) | 1, 248 | 0.02 | .000 | .879 |
| Behavior conditions | 1, 248 | 5.23 | .021 | .023 |
| Points earned | 1, 248 | 0.68 | .003 | .411 |
| Difference in points | 1, 248 | 0.50 | .002 | .479 |
| Situation conditions | 1, 248 | 0.17 | .001 | .684 |
| Trait x Behavior conditions | 1, 248 | 0.83 | .003 | .364 |
| Note: | ||||
| We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded. |
State–Situation Congruence
Model without Covariates
aov.sit <- aov_car(mood.at ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov.sit, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
tab$p.value[tab$p.value=="<.001"] <- "< .001"
tab$pes[tab$pes=="<.001"] <- "<.001"
tab$Effect <- c("Behavior conditions", "Situation conditions",
"Situation x Behavior conditions")
kable(tab,
escape=F,
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between the situation conditions and the behavior manipulation and tiredness.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "15em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Behavior conditions | 1, 251 | 6.64 | .026 | .011 |
| Situation conditions | 1, 251 | 0.23 | <.001 | .633 |
| Situation x Behavior conditions | 1, 251 | 2.20 | .009 | .139 |
| Note: | ||||
| Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded. |
Model with Covariates
lm.sit <- lm(mood.at ~ condition.sit * condition.beh + points.c + points.diff, data=data,
contrasts = list(condition.sit=contr.sum, condition.beh=contr.sum))
tab <- apa_print(car::Anova(lm.sit, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Situation conditions", "Behavior conditions", "Points earned",
"Difference in points", "Situation x Behavior conditions")
kable(tab[,c(1,8,3,2,7)],
escape=F,
row.names = F,
align=c("l", "r", "r", "r", "r"),
col.names=c("Effect", "df", "F", "η<sub>p</sub><sup>2</sup>", "<i>p</i>"),
caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness with additional covariates controlling for the results of the game.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = F, position = "left", fixed_thead = T) %>%
column_spec(c(1), width = "20em") %>%
column_spec(c(2:4), width = "8em") %>%
footnote(general="Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded.")| Effect | df | F | ηp2 | p |
|---|---|---|---|---|
| Situation conditions | 1, 249 | 0.05 | .000 | .830 |
| Behavior conditions | 1, 249 | 5.01 | .020 | .026 |
| Points earned | 1, 249 | 0.41 | .002 | .523 |
| Difference in points | 1, 249 | 0.34 | .001 | .562 |
| Situation x Behavior conditions | 1, 249 | 1.94 | .008 | .165 |
| Note: | ||||
| Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded. |
Response Surface Analysis
contrasts(data$condition.sit) <- contr.sum(2)
contrasts(data$condition.beh) <- contr.sum(2)
h3b.rsa.a <- RSA(mood.at ~ state.a.mc*trait.a.long.mc, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h3b.rsa.h <- RSA(mood.at ~ state.h.mc*trait.h.long.mc, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.a.a <- RSA(mood.at ~ state.a.mc*adv.mc.r, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.a.d <- RSA(mood.at ~ state.a.mc*dec.mc.r, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.h.a <- RSA(mood.at ~ state.h.mc*adv.mc.r, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.h.d <- RSA(mood.at ~ state.h.mc*dec.mc.r, data=data, model="full", verbose=F,
control.variables = c("condition.beh", "condition.sit"))Trait–State Congruence: Agreeableness
tab <- getPar(h3b.rsa.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Trait A", "State A<sup>2</sup>", "State A x Trait A", "Trait A<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of trait agreeableness and state agreeableness predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.03 | [2.54, 3.52] | 3.31 | < .001 |
| State A | b1 | 0.19 | [0.11, 0.28] | 0.34 | < .001 |
| Trait A | b2 | 0.12 | [0.00, 0.24] | 0.11 | .059 |
| State A2 | b3 | 0.02 | [-0.01, 0.06] | 0.09 | .206 |
| State A x Trait A | b4 | -0.05 | [-0.12, 0.02] | -0.09 | .134 |
| Trait A2 | b5 | 0.06 | [-0.01, 0.13] | 0.08 | .093 |
| Behavior conditions | cv1 | 0.07 | [-0.14, 0.28] | 0.04 | .494 |
| Situation conditions | cv2 | 0.00 | [-0.21, 0.21] | 0.00 | .982 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.31 | [0.18, 0.44] | 0.45 | < .001 |
| a2:=b3+b4+b5 | a2 | 0.04 | [-0.04, 0.11] | 0.08 | .372 |
| a3:=b1-b2 | a3 | 0.08 | [-0.08, 0.23] | 0.23 | .332 |
| a4:=b3-b4+b5 | a4 | 0.14 | [0.02, 0.25] | 0.26 | .021 |
| a5:=b3-b5 | a5 | -0.04 | [-0.12, 0.05] | 0.00 | .417 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
Trait–State Congruence: Honesty
tab <- getPar(h3b.rsa.h, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Trait HH", "State HH<sup>2</sup>", "State HH x Trait HH", "Trait HH<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of trait honesty-humility and state honesty-humility predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.27 | [2.76, 3.77] | 3.56 | < .001 |
| State HH | b1 | 0.11 | [-0.01, 0.22] | 0.23 | .067 |
| Trait HH | b2 | -0.13 | [-0.29, 0.03] | -0.17 | .105 |
| State HH2 | b3 | -0.01 | [-0.04, 0.03] | -0.03 | .628 |
| State HH x Trait HH | b4 | 0.04 | [-0.01, 0.09] | 0.16 | .156 |
| Trait HH2 | b5 | 0.06 | [-0.01, 0.12] | 0.19 | .079 |
| Behavior conditions | cv1 | 0.02 | [-0.21, 0.24] | 0.01 | .874 |
| Situation conditions | cv2 | 0.04 | [-0.18, 0.25] | 0.02 | .732 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | -0.02 | [-0.26, 0.21] | 0.06 | .836 |
| a2:=b3+b4+b5 | a2 | 0.09 | [0.00, 0.17] | 0.32 | .042 |
| a3:=b1-b2 | a3 | 0.24 | [0.08, 0.40] | 0.39 | .003 |
| a4:=b3-b4+b5 | a4 | 0.01 | [-0.07, 0.10] | -0.01 | .800 |
| a5:=b3-b5 | a5 | -0.07 | [-0.15, 0.01] | -0.22 | .103 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Agreeableness and Adversity
tab <- getPar(h4b.rsa.a.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Adv(r)", "State A<sup>2</sup>", "State A x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state agreeableness and adversity predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 2.81 | [2.32, 3.31] | 3.07 | < .001 |
| State A | b1 | 0.13 | [0.02, 0.24] | 0.23 | .018 |
| Adv(r) | b2 | 0.00 | [-0.12, 0.11] | -0.01 | .957 |
| State A2 | b3 | 0.01 | [-0.02, 0.05] | 0.05 | .457 |
| State A x Adv(r) | b4 | 0.04 | [-0.01, 0.09] | 0.16 | .157 |
| Adv(r)2 | b5 | 0.03 | [-0.02, 0.07] | 0.11 | .221 |
| Behavior conditions | cv1 | 0.09 | [-0.12, 0.29] | 0.05 | .404 |
| Situation conditions | cv2 | 0.08 | [-0.13, 0.29] | 0.04 | .479 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.13 | [-0.06, 0.32] | 0.22 | .187 |
| a2:=b3+b4+b5 | a2 | 0.08 | [0.01, 0.15] | 0.32 | .031 |
| a3:=b1-b2 | a3 | 0.14 | [0.02, 0.25] | 0.24 | .018 |
| a4:=b3-b4+b5 | a4 | 0.00 | [-0.08, 0.09] | 0.00 | .936 |
| a5:=b3-b5 | a5 | -0.01 | [-0.07, 0.05] | -0.05 | .703 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Agreeableness and Deception
tab <- getPar(h4b.rsa.a.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Dec(r)", "State A<sup>2</sup>", "State A x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state agreeableness and deception predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 2.99 | [2.47, 3.52] | 3.26 | < .001 |
| State A | b1 | 0.23 | [0.14, 0.32] | 0.40 | < .001 |
| Dec(r) | b2 | 0.09 | [-0.01, 0.20] | 0.15 | .084 |
| State A2 | b3 | 0.01 | [-0.03, 0.05] | 0.05 | .508 |
| State A x Dec(r) | b4 | 0.01 | [-0.04, 0.06] | 0.04 | .692 |
| Dec(r)2 | b5 | 0.04 | [0.00, 0.08] | 0.15 | .060 |
| Behavior conditions | cv1 | 0.08 | [-0.14, 0.29] | 0.04 | .474 |
| Situation conditions | cv2 | 0.04 | [-0.17, 0.24] | 0.02 | .736 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.33 | [0.19, 0.46] | 0.55 | < .001 |
| a2:=b3+b4+b5 | a2 | 0.06 | [-0.01, 0.13] | 0.23 | .084 |
| a3:=b1-b2 | a3 | 0.14 | [-0.01, 0.28] | 0.26 | .067 |
| a4:=b3-b4+b5 | a4 | 0.04 | [-0.02, 0.11] | 0.16 | .166 |
| a5:=b3-b5 | a5 | -0.03 | [-0.09, 0.04] | -0.10 | .443 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Honesty and Adversity
tab <- getPar(h4b.rsa.h.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Adv(r)", "State HH<sup>2</sup>", "State HH x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state honesty-humility and adversity predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.06 | [2.57, 3.56] | 3.34 | < .001 |
| State HH | b1 | 0.11 | [0.00, 0.22] | 0.23 | .046 |
| Adv(r) | b2 | 0.01 | [-0.11, 0.13] | 0.02 | .843 |
| State HH2 | b3 | -0.02 | [-0.05, 0.02] | -0.07 | .295 |
| State HH x Adv(r) | b4 | 0.03 | [-0.01, 0.08] | 0.17 | .136 |
| Adv(r)2 | b5 | 0.04 | [0.00, 0.09] | 0.18 | .064 |
| Behavior conditions | cv1 | -0.01 | [-0.23, 0.21] | 0.00 | .937 |
| Situation conditions | cv2 | 0.08 | [-0.13, 0.29] | 0.04 | .477 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.12 | [-0.08, 0.32] | 0.25 | .232 |
| a2:=b3+b4+b5 | a2 | 0.06 | [-0.02, 0.13] | 0.27 | .121 |
| a3:=b1-b2 | a3 | 0.10 | [-0.01, 0.20] | 0.21 | .068 |
| a4:=b3-b4+b5 | a4 | -0.01 | [-0.07, 0.05] | -0.06 | .806 |
| a5:=b3-b5 | a5 | -0.06 | [-0.13, 0.00] | -0.25 | .059 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
State–Situation Congruence: Honesty and Deception
tab <- getPar(h4b.rsa.h.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Dec(r)", "State HH<sup>2</sup>", "State HH x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")
kable(tab[,c(1,2,11,9,5)]
, digits=c(2,2,2,2,2)
, align=c("l", "r", "r", "r", "r", "r")
, caption = "Parameters of the response surface analysis of state honesty-humility and deception predicting tiredness."
, escape = FALSE
, col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "β", "<i>p</i> value")
) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left") %>%
column_spec(1, width = "18em") %>%
pack_rows("Regression Parameters", 1, 8) %>%
pack_rows("Response Surface Parameters", 9, 13) %>%
footnote(general="b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)| Label | b | 95% Confidence Interval | β | p value | |
|---|---|---|---|---|---|
| Regression Parameters | |||||
| (Intercept) | b0 | 3.31 | [2.80, 3.82] | 3.61 | < .001 |
| State HH | b1 | 0.23 | [0.14, 0.32] | 0.48 | < .001 |
| Dec(r) | b2 | 0.10 | [0.00, 0.19] | 0.15 | .040 |
| State HH2 | b3 | -0.01 | [-0.05, 0.02] | -0.05 | .423 |
| State HH x Dec(r) | b4 | 0.02 | [-0.02, 0.07] | 0.10 | .303 |
| Dec(r)2 | b5 | 0.06 | [0.02, 0.10] | 0.21 | .005 |
| Behavior conditions | cv1 | -0.04 | [-0.27, 0.20] | -0.02 | .765 |
| Situation conditions | cv2 | 0.01 | [-0.19, 0.22] | 0.01 | .895 |
| Response Surface Parameters | |||||
| a1:=b1+b2 | a1 | 0.33 | [0.21, 0.45] | 0.63 | < .001 |
| a2:=b3+b4+b5 | a2 | 0.07 | [0.00, 0.14] | 0.26 | .063 |
| a3:=b1-b2 | a3 | 0.13 | [0.00, 0.27] | 0.33 | .053 |
| a4:=b3-b4+b5 | a4 | 0.02 | [-0.04, 0.08] | 0.05 | .546 |
| a5:=b3-b5 | a5 | -0.07 | [-0.13, -0.01] | -0.26 | .015 |
| Note: | |||||
| b represents unstandardized regression coefficients, β represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output. | |||||
Visualization
ANOVA Interaction Plots
p1 <- interactions::cat_plot(lm.a, pred=condition.beh, modx=trait.a.ms,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Tiredness (r)",
main.title = "Trait Agreeableness x Behavior Conditions",
legend.main = "Trait Agreeableness (median-split)",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p2 <- interactions::cat_plot(lm.h, pred=condition.beh, modx=trait.h.ms,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Tiredness (r)",
main.title = "Trait Honesty x Behavior Conditions",
legend.main = "Trait Honesty (median-split)",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p3 <- interactions::cat_plot(lm.sit, pred=condition.beh, modx=condition.sit,
plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
geom="line", interval=T, interval.geom = "linerange",
pred.point.size = 5, dodge.width = 0.3,
x.label="Behavior Conditions", y.label="Tiredness (r)",
main.title = "Situation x Behavior Conditions",
legend.main = "Situation Conditions",
colors=cols.beh) +
theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
scale_x_discrete(expand=c(0.2, 0.2))
p1 / plot_spacer() / p2 / plot_spacer() / p3 + plot_layout(heights=c(1,0.1, 1, 0.1,1)) + plot_annotation(tag_levels = "A")
Figure 15: Interaction plots of interactions between median-split personality traits, behavior conditions, and situation conditions predicting reverse-coded tiredness (i.e., higher levels indicate less tiredness).
Response Surface Plots
## print RSA plots
a <- plot(h3b.rsa.a, xlab="State Agreeableness", ylab="Trait Agreeableness", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Agreeableness",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
b <- plot(h3b.rsa.h, xlab="State Honesty", ylab="Trait Honesty", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Honesty-humility",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
c <- plot(h4b.rsa.a.a, xlab="State Agreeableness", ylab="Adversity(r)", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Adversity",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
d <- plot(h4b.rsa.h.a, xlab="State Honesty", ylab="Adversity(r)", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Adversity",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
e <- plot(h4b.rsa.a.d, xlab="State Agreeableness", ylab="Deception(r)", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Deception",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
f <- plot(h4b.rsa.h.d, xlab="State Honesty", ylab="Deception(r)", zlab="Tiredness(r)",
legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Deception",
project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))
## combine plots
cowplot::plot_grid(a,b,c,d,e,f, ncol = 2, labels="AUTO", label_size = 12)
Figure 16: Response surface plots of the association between trait–state congruence, state–situation congruence, and tiredness. Adversity, deception, and tiredness were reverse-coded (indicated by (r)) such that higher values indicate less adversity, decpetion, and tiredness, respectively. The response surface parameters are listed below the titles. The blue lines represent the line of congruence (LOC; i.e., X = Y) and the line of incongruence (LOIC; i.e., X = -Y). The black lines represent the range of observed values as a twodimensional boxplot.
Conclusion
Overall, the data did not support the congruence hypotheses with regard to tiredness:
Analyses of variance did not yield any significant interaction effects (all Fs < 2.20, all ps > .139)
Only the behavior conditions were significantly associated with reverse-coded tiredness such that participants in the high agreeableness and honesty condition (M = 3.54, SD = 0.78) reported significantly more active mood—that is, less tiredness—than participants in the low agreeableness and honesty condition (M = 3.25, SD = 1.02, d =0.33, 95% CI [0.08, 0.58])
Polynomial regressions also showed that only higher levels of state agreeableness and state honesty-humility (but not interactions of fit patterns) were significantly associated with more active mood across all models (bs ranging from 0.11 to 0.24)
In summary, across both analysis of variance and response surface analysis, neither interactions nor fit patterns were significantly associated with positive affect
Congruence and Stroop Performance
Hypothesis
H3c: Congruence between personality trait and personality state is associated with with Stroop performance such that trait-congruent personality states are related to faster reaction times or less errors than trait-incongruent personality states.
H4c: Congruence between personality state and situation characteristic is associated with Stroop performance such that situation-congruent personality states are related are related to faster reaction times or less errors than situation-incongruent personality states.
Analytic Strategy
Statistical data analysis inevitably offers so-called researcher degrees of freedom. Many decisions must be made for which there are several equally valid options. Therefore, any decision is often both defensible and arbitrary (Simonsohn, Simmons, & Nelson, 2019). Handling of reaction times (RTs) in psychological experiments seems to offer a particularly great amount of researcher degrees of freedom. Therefore, we decided to conduct a mini specification-curve analysis (Simonsohn, Simmons, & Nelson, 2019) for all hypotheses that involve RTs. We call it mini specification-curve analysis because it does not involve all analyses or all steps of data handling, but merely focuses on identifying and handling outliers and calculating summary statistics in RT data. Additionally, we want to emphasize that we most certainly did not include all conceivable options for these decisions, but surely selected a fair and representative amount.
Step 1: Set of Reasonable Specifications
Here, we list the specifications we will include for four different decisions: (1) How to identify outliers in RTs, (2) how to treat these outliers, (3) which trials to use for calculation, and (4) which summary statistic to calculate. Numbers in brackets represent the number of specifications in the branch.
Reaction Times
Total Number of Specifications
Total Number of Specifications
46 methods for the identification of outliers x 3 methods for the treatment of outliers x 8 options for selecting trials to be included x 2 methods for the summary statistic x 4 options for covariates + no identification/treatment of outliers x 8 options for selecting trials to be included x 2 methods for the summary statistic x 4 options for covariates = 8832 + 64 = 8896 analyses
Identification of Outliers
Fixed Cutoffs (4)
All RTs below or above a certain, prespecified cutoff are identified as outliers
- < 100ms, > 1500ms
- < 350ms, > 2000ms
- < 300ms, > 4000ms
- < 200ms, > 2000ms
Relative cutoffs: Global (14)
Applied to all RTs from all participants and all conditions at the same time
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs
- Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Relative cutoffs: Per cell (14)
Applied separately to the RTs from each experimental cell, i.e., condition (but across participants)
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs
- Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Relative cutoffs: Per participant (14)
Applied separately to RTs from each participant (but across cells)
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs
- Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Treatment of Outliers
- Trimming (i.e. outliers are removed)
- Winsorizing (i.e., outliers are replaced by the cutoff)
- Interpolating (i.e., outliers are replaced by the mean/median)
- None (i.e., outliers are kept in the data)
Selection of the Trials to be Considered
Use all trials (4)
- Overall (i.e., across all conditions)
- Congruent trials only
- Incongruent trials only
- Neutral trials only
Use only correctly answered trials (4)
- Overall (i.e., across all conditions)
- Congruent trials only
- Incongruent trials only
- Neutral trials only
Summary Statistic
- Mean
- Median
Inclusion of Covariates
- Experimental conditions only
- Experimental conditions & points won in the game
- Experimental conditions & point difference in the game
- Experimental conditions, points won in the game, & point difference in the game
Error Rates
Total Number of Specifications
Total Number of Specifications
46 methods for the identification of outliers x 3 methods for the treatment of outliers x 4 options for selecting trials to be included x 1 methods for the calculation of summary statistics x 4 options for covariates + no identification/treatment of outliers x 4 options for selecting trials to be included x 1 methods for the calculation of summary statistics x 4 options for covariates = 2224 analyses
Identification of Outliers
Fixed cutoffs (4)
All RTs below or above a certain, pre-specified cutoff are identified as outliers
- < 100ms, > 1500ms
- < 350ms, > 2000ms
- < 300ms, > 4000ms
- < 200ms, > 2000ms
Relative cutoffs: Global (14)
Applied to all RTs from all participants and all conditions at the same time
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs
- Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Relative cutoffs: Per cell (14)
Applied separately to the RTs from each experimental cell, i.e., condition (but across participants)
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs
- Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Relative cutoffs: Per participant (14)
Applied separately to RTs from each participant
Mean RT +- SDs (5)
- Mean RT ± 2 SDs
- Mean RT ± 2.5 SDs
- Mean RT ± 3 SDs
- Mean RT ± 3.5 SDs *Mean RT ± 4 SDs
Mean RT +- IQR (2)
- Mean RT ± 3 IQR
- Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR
Median +- MADs (3)
- median plus or minus 2 times the MAD
- median plus or minus 2.5 times the MAD
- median plus or minus 3 times the MAD
Percentages (4)
- 2% most extreme values
- 5% most extreme values
- 10% most extreme values
- 15% most extreme values
Treatment of Outliers
- Trimming (i.e. outliers are removed)
- None (i.e., outliers are kept in the data)
- Error (i.e., outliers are considered errors)
- Correct (i.e., outliers are considered correct)
Selection of the Trials to be Considered
- Overall (i.e., across all conditions)
- Congruent trials only
- Incongruent trials only
- Neutral trials only
Inclusion of Covariates
- Experimental conditions
- Experimental conditions & points won in the game
- Experimental conditions & point difference in the game
- Experimental conditions, points won in the game, & point difference in the game
Step 2: Descriptive Specification Curves
We conducted all analyses that include Stroop performance (RTs or error rates) separately for each specification. The results from the different specifications were then be graphically displayed in descriptive specification curves. These curves displayed the effects of interest (i.e., interaction coefficients or presence of fit patterns) from all specifications ranked by size of the effect. Additionally, these plots indicate which analytic decisions lead to which effects. Descriptive specification curves were used to examine the range of the effects across all specifications, the proportion of significant effects, and how the analytic decisions impacted the estimated effects.
Step 3: Permutation Test
Finally, we applied a permutation technique to test how inconsistent the results are with the null hypothesis of no effect across the specification curve. We created 500 data sets by shuffling the dependent variable (RT or error rate). In these datasets, because of the random distribution of RTs/error rates, the null hypothesis is per definition true.
We then needed to derive a test statistic from the specification curves under the null hypothesis (obtained with the 500 shuffled datasets). Because the congruence hypotheses are compound hypotheses for which several conditions must be fulfilled (see Humberg, Nestler, & Back, 2019), we used the percentage of specifications supporting the alternative hypothesis (i.e., the congruence effect) as the test statistic.
For each shuffled dataset, we calculated the percentage of specifications supporting the congruence hypothesis and compared the distribution of this percentage across the 500 samples to the observed percentage in the real data.
This comparison allowed us to determine whether we could reject the null hypothesis of no congruence effect across the specification curve. Specifically, the relative frequency of shuffled samples with at least as many specifications supporting the congruence effect as the unshuffled data represented the p value of the permutation test. This p value reflects the probability of observing this many or even more significant specifications under the assumption of no congruence effect.
Additionally, we graphically compared the observed and the expected-under-the-null specification curves.
Stroop Effect
sub <- data.clean %>%
ungroup() %>%
select("ResponseId", contains("Stroop"), "condition.bs") %>%
rename(id=ResponseId) %>%
filter(numStroopTrial=="passed")
stroop.long <- data.frame(id=NA, answer=NA, correct=NA, stimulus=NA, condition=NA, rt=NA, exp.cell=NA)
for(i in 1:nrow(sub)) {
if(length(unlist(str_split(sub$numStroopAnswer[i],",")))==75) {
answer=unlist(str_split(sub$numStroopAnswer[i],","))
} else {
answer=rep("-999",75)
}
new <- data.frame(id=rep(as.character(sub[i,1]),75), answer=answer, correct=unlist(str_split(sub$numStroopCorrectAnswer[i],",")),
stimulus=unlist(str_split(sub$numStroopStimulus[i],",")), condition=unlist(str_split(sub$numStroopCondition[i],",")),
rt=unlist(str_split(sub$numStroopReactionTime[i],",")), exp.cell=sub$condition.bs[i])
stroop.long <- rbind(stroop.long, new)
}
stroop.long <- stroop.long[2:nrow(stroop.long),]
stroop.long$rt <- as.numeric(stroop.long$rt)
stroop.long$rt[which(stroop.long$rt==-999)] <- NA
stroop.long$rt[which(is.na(stroop.long$answer))] <- NA
stroop.long$answer[which(is.na(stroop.long$rt))] <- NA
stroop.long$condition <- factor(stroop.long$condition, levels=c(0, -1, 1), labels=c("neutral", "congruent", "incongruent"))
stroop.long$correct <- factor(stroop.long$correct, levels=c(0,1), labels=c("right", "left"))
stroop.long$answer[stroop.long$answer!="ArrowLeft" & stroop.long$answer!="ArrowRight"] <- NA
stroop.long$answer <- factor(stroop.long$answer, levels=c("ArrowLeft", "ArrowRight"), labels=c("left", "right"))
stroop.long$error <- ifelse(stroop.long$answer==stroop.long$correct, FALSE, TRUE) # TRUE means that there is an error as in error? true
stroop.short <- stroop.long %>%
filter(!is.na(condition)) %>%
filter(!is.na(rt)) %>%
filter(!is.na(error)) %>%
group_by(id, condition) %>%
summarize(error.rate = sum(error)*1/75,
error.perc = sum(error)*100/75,
mean.rt = mean(rt)) %>%
ungroup() %>%
left_join(., subset(data, select=c("ResponseId", "condition.beh", "condition.sit", "condition.bs")), by=c("id" = "ResponseId"))Exemplary Specification
Descriptives
Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect.
tab <- bind_rows(describeBy(stroop.short$mean.rt, group=stroop.short$condition, mat = T, digits=2)[,c(2, 5:15, 4)],
describeBy(stroop.short$error.rate, group=stroop.short$condition, mat = T, digits=2)[,c(2, 5:15, 4)])
kable(tab,
row.names = FALSE,
col.names = c("Stroop trial type", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis", "se", "n"),
caption="Descriptive statistics of average reaction times and error rates in the different Stroop trial types aggregated within persons.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
pack_rows("DV: Reaction Time", 1, 3) %>%
pack_rows("DV: Error Rate", 4, 6)| Stroop trial type | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | n |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DV: Reaction Time | ||||||||||||
| neutral | 816.47 | 187.30 | 777.33 | 798.01 | 130.61 | 477.73 | 1694.80 | 1217.07 | 1.47 | 3.65 | 14.76 | 161 |
| congruent | 763.98 | 180.06 | 735.33 | 744.50 | 146.04 | 418.70 | 1695.77 | 1277.07 | 1.60 | 4.88 | 14.19 | 161 |
| incongruent | 867.10 | 192.24 | 834.30 | 850.34 | 142.38 | 492.63 | 1836.07 | 1343.43 | 1.36 | 3.85 | 15.15 | 161 |
| DV: Error Rate | ||||||||||||
| neutral | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.03 | 3.32 | 10.98 | 0.00 | 161 |
| congruent | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.07 | 0.07 | 4.55 | 27.26 | 0.00 | 161 |
| incongruent | 0.02 | 0.02 | 0.01 | 0.01 | 0.02 | 0.00 | 0.13 | 0.13 | 2.22 | 7.42 | 0.00 | 161 |
Regression Models
Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect.
stroop.short$condition <- relevel(stroop.short$condition, ref="congruent")
# model
m.rt <- lmer(mean.rt ~ condition + (1|id), data=stroop.short)
m.er <- lmer(error.perc ~ condition + (1|id), data=stroop.short)
tab <- bind_rows(as.data.frame(summary(m.rt)$coefficients),as.data.frame(summary(m.er)$coefficients))
tab$df <- floor(tab$df)
tab$`Pr(>|t|)` <- printp(tab$`Pr(>|t|)`)
row.names(tab) <- c("Intercept (i.e., mean of congruent trials)",
"Difference (in ms) between congruent and neutral trials",
"Difference (in ms) between congruent and incongruent trials",
"Intercept (i.e., error rate of congruent trials)",
"Difference (in %) between congruent and neutral trials",
"Difference (in %) between congruent and incongruent trials")
kable(tab,
row.names = TRUE,
escape = FALSE,
digits = 3,
align = "r",
col.names = c("Coefficient", "SE", "df", "<i>t</i>", "<i>p</i>"),
caption="Multilevel regression model predicting average reaction times and error rates from the different Stroop trial types aggregated within persons.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
pack_rows("DV: Reaction Time", 1, 3) %>%
pack_rows("DV: Error Rate", 4, 6) %>%
column_spec(1, width = "20em") %>%
column_spec(2:5, width = "8em")| Coefficient | SE | df | t | p | |
|---|---|---|---|---|---|
| DV: Reaction Time | |||||
| Intercept (i.e., mean of congruent trials) | 763.981 | 14.706 | 179 | 51.950 | < .001 |
| Difference (in ms) between congruent and neutral trials | 52.485 | 6.027 | 320 | 8.709 | < .001 |
| Difference (in ms) between congruent and incongruent trials | 103.114 | 6.027 | 320 | 17.110 | < .001 |
| DV: Error Rate | |||||
| Intercept (i.e., error rate of congruent trials) | 0.265 | 0.104 | 452 | 2.558 | .011 |
| Difference (in %) between congruent and neutral trials | -0.108 | 0.133 | 319 | -0.808 | .420 |
| Difference (in %) between congruent and incongruent trials | 1.342 | 0.133 | 319 | 10.069 | < .001 |
Visualization
Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect.
rt <- ggplot(stroop.short, aes(x = condition, y = mean.rt, fill = condition, colour = condition)) +
theme_pub() +
geom_point(position = position_jitter(width = .25), size = 1, alpha = .5) +
geom_boxplot(outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
guides(fill = "none", colour = "none") +
ylab("Mean reaction time") +
theme_pub() +
theme(axis.title.x = element_blank()) +
ggtitle("Mean Reaction Time")
er <- ggplot(stroop.short, aes(x = condition, y = error.perc, fill = condition, colour = condition)) +
theme_pub() +
geom_point(position = position_jitter(width = .25, height = .1), size = 1, alpha = .5) +
geom_boxplot(outlier.shape = NA, alpha = 0.5, width = .15, colour = "BLACK") +
guides(fill = "none", colour = "none") +
ylab("Error rate (%)") +
theme_pub() +
theme(axis.title.x = element_blank()) +
ggtitle("Error Rate")
rt | er
Descriptive Specification Curves
p1a <- plot_curve_aov(results.stroop.rt,
"estimate_conditionincongruent",
desc = FALSE,
ci = FALSE,
ribbon = TRUE,
legend = FALSE,
null = 0) +
theme_pub() + ylab("numerical Stroop effect (in ms)") + ggtitle("Reaction Time") +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
p2a <- plot_curve_aov(results.stroop.er,
"estimate_conditionincongruent",
desc = FALSE,
ci = FALSE,
ribbon = TRUE,
legend = FALSE,
null = 0) +
theme_pub() + ylab("numerical Stroop effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) + ggtitle("Error Rate") +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
p1b <- plot_choices_aov(results.stroop.rt,
"estimate_conditionincongruent",
choices = c("identification", "treatment", "summary", "subset"),
desc = FALSE,
null = 0) + theme_pub() +
theme(strip.text.y = element_text(size=9,face="bold"),
panel.spacing = unit(.6, "lines"),
axis.text.y = element_text(size=10, hjust=1)) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
p2b <- plot_choices_aov(results.stroop.er,
"estimate_conditionincongruent",
choices = c("identification", "treatment"),
desc = FALSE,
null = 0) + theme_pub() +
theme(strip.text.y = element_text(size=9,face="bold", hjust=1, margin=margin(l=10, b=10, r=10)),
axis.text.y = element_text(size=10, hjust=1)) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
#p1/p2
cowplot::plot_grid(p1a, p2a, p1b, p2b, ncol=2, labels=c("A","B","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.3))
Permutation Test
## create table
tab <- bind_cols(data.frame(dv=c("Reaction time", "Error rate")), res.stroop)
tab$predictor <- c("Stroop effect: difference in reaction time (in ms) between incongruent and congruent trials",
"Stroop effect: difference in error rate (in %) between incongruent and congruent trials")
tab$med.eff[2] <- tab$med.eff[2]*100
tab$p.val <- printp(tab$p.val)
tab$n.sig <- round(tab$n.sig*100/tab$n.specs,0)
## print table
kable(tab,
align = c("l", "l", "r", "r", "r", "r", "r"),
escape = F,
col.names = c("DV", "Relevant predictor", "Number of specifications", "Median effect size",
"Significant specifications (%)", "Number of shuffled samples with more significant
specifications than for the original sample", "<i>p</i> value of permutation test")) %>%
add_header_above(c(" " = 2,
"Description of specification curve\n(for original sample)" = 3,
"Permutation test with\n500 shuffled samples" = 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | DV | Relevant predictor | Number of specifications | Median effect size | Significant specifications (%) | Number of shuffled samples with more significant specifications than for the original sample | p value of permutation test |
|---|---|---|---|---|---|---|
| Reaction time | Stroop effect: difference in reaction time (in ms) between incongruent and congruent trials | 556 | 106.71 | 100 | 0 | < .001 |
| Error rate | Stroop effect: difference in error rate (in %) between incongruent and congruent trials | 139 | 3.00 | 100 | 7 | .014 |
Inferential Specification Curves
p1 <- plot_curve_aov(results.stroop.rt, "estimate_conditionincongruent", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "numerical Stroop effect (in ms)") +
theme_pub() +
geom_ribbon(data=conf.curves[["conf.curve.stroop.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
scale_x_continuous(breaks = seq(0,600,100)) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Reaction time")
p2 <- plot_curve_aov(results.stroop.er, "estimate_conditionincongruent", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "numerical Stroop effect (in % of errors)") +
theme_pub() +
geom_ribbon(data=conf.curves[["conf.curve.stroop.er"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
scale_y_continuous(labels = function(x) x * 100) +
scale_x_continuous(breaks = seq(0,150,25)) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Error rates")
cowplot::plot_grid(p1, p2, ncol=2, labels=c("A","B"), label_size = 10, align = "v", axis = "rbl")
Conclusion
As expected, we found significant Stroop effects in the numerical Stroop task:
- Participants had significantly longer reaction times in incongruent trials than in congruent trials (median difference across all specifications: 106.71ms)
- Participants made significantly more errors in incongruent trials than in congruent trials (median difference across all specifications: 3%)
- Permutation tests demonstrated that the null hypothesis of no Stroop effect had to be rejected across the whole specification curve, p < .001 for reaction times and p = .014 for error rates
Analysis of Variance
Descriptive Specification Curves
Reaction Time
labels.pre = c("condition.beh + condition.sit",
"condition.beh + condition.sit + points.c",
"condition.beh + condition.sit + points.c + points.diff",
"condition.beh + condition.sit + points.diff")
labels.post = c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")
res.aov.rt.a <- res.aov.rt.a %>%
mutate(controls = factor(controls,
levels=c("condition.beh + trait.a.ms + condition.sit",
"condition.beh + trait.a.ms + condition.sit + points.c",
"condition.beh + trait.a.ms + condition.sit + points.c + points.diff",
"condition.beh + trait.a.ms + condition.sit + points.diff"),
labels=c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")))
res.aov.rt.h <- res.aov.rt.h %>%
mutate(controls = factor(controls,
levels=c("condition.beh + trait.h.ms + condition.sit",
"condition.beh + trait.h.ms + condition.sit + points.c",
"condition.beh + trait.h.ms + condition.sit + points.c + points.diff",
"condition.beh + trait.h.ms + condition.sit + points.diff"),
labels=c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")))
res.aov.rt.sit <- res.aov.rt.sit %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
a <- plot_curve_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("Trait Agreeableness x Behavior Conditions") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("Trait Honesty x Behavior Conditions") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_curve_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("Situation x Behavior Conditions") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
d <- plot_choices_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness",
choices = c("identification", "treatment", "conditions", "summary", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_choices_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility",
choices = c("identification", "treatment", "conditions", "summary", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
f <- plot_choices_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2",
choices = c("identification", "treatment", "conditions", "summary", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,d,e,c,NULL,f, ncol=2, labels=c("A","B", "","", "C","", ""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.5))
Figure 17: Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates.
d <- res.aov.rt.a %>%
select(contains("estimate")) %>%
summarize(intercept = median(`estimate_(Intercept)`),
cond.high = median(`estimate_condition.behhigh agreeableness and honesty`),
trait.high = median(`estimate_trait.a.mshigh trait agreeableness`),
interaction = median(`estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness`))
df <- data.frame(value=rep(NA,4), trait=rep(c("disagreeable", "agreeable"),2), behavior=rep(c("disagreeable", "agreeable"),each=2))
df$value <- c(d$intercept, d$intercept+d$trait.high, d$intercept+d$cond.high, d$intercept+d$cond.high+d$trait.high+d$interaction)
ggplot(df, aes(x=behavior, y=value, group=trait, linetype=trait)) + theme_pub() +
geom_line() + geom_point() +
scale_x_discrete(expand=c(0.1,0.1)) +
scale_y_continuous(limits=c(740,830)) +
theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
labs(x="Behavior conditions", y="Median predicted reaction time\nacross all specifications (in ms)", linetype="Trait Agreeableness") + ggtitle("Trait Agreeableness x Behavior Conditions")
Figure 18: Illustration of the significant interaction between median-split trait agreeableness and the behavior conditions predicting reaction times.
Error Rate
res.aov.er.a <- res.aov.er.a %>%
mutate(controls = factor(controls,
levels=c("condition.beh + trait.a.ms + condition.sit",
"condition.beh + trait.a.ms + condition.sit + points.c",
"condition.beh + trait.a.ms + condition.sit + points.c + points.diff",
"condition.beh + trait.a.ms + condition.sit + points.diff"),
labels=c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")))
res.aov.er.h <- res.aov.er.h %>%
mutate(controls = factor(controls,
levels=c("condition.beh + trait.h.ms + condition.sit",
"condition.beh + trait.h.ms + condition.sit + points.c",
"condition.beh + trait.h.ms + condition.sit + points.c + points.diff",
"condition.beh + trait.h.ms + condition.sit + points.diff"),
labels=c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")))
res.aov.er.sit <- res.aov.er.sit %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
a <- plot_curve_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("Trait-State Agreeableness") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,2400,500)) +
scale_y_continuous(labels = function(x) x * 100) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("Trait-State Honesty") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,2400,500)) +
scale_y_continuous(labels = function(x) x * 100) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_curve_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("Situation x Behavior Conditions") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_x_continuous(breaks=seq(0,2400,500)) +
scale_y_continuous(labels = function(x) x * 100) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
d <- plot_choices_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness",
choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,2400,500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_choices_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility",
choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,2400,500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
f <- plot_choices_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2",
choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,2400,500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,d,e,c,NULL,f, ncol=2, labels=c("A","B", "","", "C","", ""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.5))
Figure 19: Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates.
Permutation Test
Reaction Time
## create table
tab <- data.frame(congruence.op=rep("Interaction",3),
predictor=c("Median-split Trait A x Behavior Conditions",
"Median-split Trait H x Behavior Conditions",
"Situation x Behavior Conditions"),
n.specs = NA,
med.effect = NA,
perc.sig = NA,
n.shuff = NA,
p.val = NA)
## fill table
tab[c(1:3),3:7] <- res.aov.rt[,2:6]
## format table
tab$p.val <- printp(tab$p.val)
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))
## print table
kable(tab,
align = c("l", "l", "r", "r", "r", "r", "r"),
escape=F,
col.names = c("Operationalization",
"Relevant predictor", "Number of specifications",
"Median effect size", "Significant specifications (%)",
"Number of shuffled samples\nwith more significant
specifications\nthan for the original sample", "<i>p</i> value of permutation test")) %>%
add_header_above(c(" " = 2,
"Description of specification curve (for original sample)" = 3,
"Permutation test with 500 shuffled samples" = 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | Operationalization | Relevant predictor | Number of specifications | Median effect size | Significant specifications (%) | Number of shuffled samples with more significant specifications than for the original sample | p value of permutation test |
|---|---|---|---|---|---|---|
| Interaction | Median-split Trait A x Behavior Conditions | 8896 | 114.00 | 66% | 9 | .018 |
| Interaction | Median-split Trait H x Behavior Conditions | 8896 | -11.40 | 0% | 500 | > .999 |
| Interaction | Situation x Behavior Conditions | 8896 | 48.61 | 0% | 500 | > .999 |
Error Rate
## create table
tab <- data.frame(congruence.op=rep("Interaction",3),
predictor=c("Median-split Trait A x Behavior Conditions",
"Median-split Trait H x Behavior Conditions",
"Situation x Behavior Conditions"),
n.specs = NA,
med.effect = NA,
perc.sig = NA,
n.shuff = NA,
p.val = NA)
## fill table
tab[c(1:3),3:7] <- res.aov.error[,2:6]
## format table
tab$p.val <- printp(tab$p.val)
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))
## print table
kable(tab,
align = c("l", "l", "r", "r", "r", "r", "r"),
escape=F,
col.names = c("Operationalization",
"Relevant predictor", "Number of specifications",
"Median effect size", "Significant specifications (%)",
"Number of shuffled samples with more significant
specifications than for the original sample", "<i>p</i> value of permutation test")) %>%
add_header_above(c(" " = 2,
"Description of specification curve (for original sample)" = 3,
"Permutation test with 500 shuffled samples" = 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | Operationalization | Relevant predictor | Number of specifications | Median effect size | Significant specifications (%) | Number of shuffled samples with more significant specifications than for the original sample | p value of permutation test |
|---|---|---|---|---|---|---|
| Interaction | Median-split Trait A x Behavior Conditions | 2224 | -0.01 | 5% | 127 | .254 |
| Interaction | Median-split Trait H x Behavior Conditions | 2224 | 0.00 | 0% | 376 | .752 |
| Interaction | Situation x Behavior Conditions | 2224 | 0.00 | 0% | 370 | .740 |
Inferential Specification Curves
Reaction Time
a <- plot_curve_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.a"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Agreeableness x Behavior Conditions")
b <- plot_curve_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.h"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Honesty x Behavior Conditions")
c <- plot_curve_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.sit"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Situation x Behavior Conditions")
cowplot::plot_grid(a,b,c, ncol=2, labels=c("A","B","C"), label_size = 12, align = "v", axis = "rbl")
Figure 20: Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates).
Error Rate
a <- plot_curve_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.a"]],
mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
scale_y_continuous(labels = function(x) x * 100) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Agreeableness x Behavior Conditions")
b <- plot_curve_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.h"]],
mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
scale_y_continuous(labels = function(x) x * 100) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Honesty x Behavior Conditions")
c <- plot_curve_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.sit"]],
mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
scale_y_continuous(labels = function(x) x * 100) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Situation x Behavior Conditions")
cowplot::plot_grid(a,b,c, ncol=2, labels=c("A","B","C"), label_size = 12, align = "v", axis = "rbl")
Figure 21: Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates).
Response Surface Analysis
Descriptive Specification Curves
Reaction Time
labels.pre = c("condition.beh + condition.sit",
"condition.beh + condition.sit + points.c",
"condition.beh + condition.sit + points.c + points.diff",
"condition.beh + condition.sit + points.diff")
labels.post = c("exp. groups",
"exp. groups + points",
"exp. groups + points + diff",
"exp. groups + diff")
res.rsa.rt.a <- res.rsa.rt.a %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.rt.h <- res.rsa.rt.h %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.rt.a.adv <- res.rsa.rt.a.adv %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.rt.a.dec <- res.rsa.rt.a.dec %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.rt.h.adv <- res.rsa.rt.h.adv %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.rt.h.dec <- res.rsa.rt.h.dec %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
a <- plot_curve_rsa(res.rsa.rt.a, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("Trait-State Agreeableness") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_rsa(res.rsa.rt.h, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("Trait-State Honesty-Humility") +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_choices_rsa(res.rsa.rt.a, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
d <- plot_choices_rsa(res.rsa.rt.h, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_curve_rsa(res.rsa.rt.a.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("State Agreeableness-Adversity")+
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
f <- plot_curve_rsa(res.rsa.rt.h.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("State Honesty-Adversity")+
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
g <- plot_choices_rsa(res.rsa.rt.a.adv, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
h <- plot_choices_rsa(res.rsa.rt.h.adv, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
i <- plot_curve_rsa(res.rsa.rt.a.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("State Agreeableness-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
j <- plot_curve_rsa(res.rsa.rt.h.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect (in ms)") +
theme_pub() + ggtitle("State Honesty-Deception")+
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
k <- plot_choices_rsa(res.rsa.rt.a.dec, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
l <- plot_choices_rsa(res.rsa.rt.h.dec, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
scale_x_continuous(breaks=seq(0,9000,1500)) +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold"),
panel.spacing = unit(.75, "lines")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""),
label_size = 10, align = "v", axis = "rbl", rel_heights = c(1,2,1,2,1,2))
Figure 22: Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop reaction times.
a <- plot_curve_rsa(res.rsa.rt.a, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("Trait-State Agreeableness") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_rsa(res.rsa.rt.h, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("Trait-State Honesty-Humility") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_choices_rsa(res.rsa.rt.a, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
d <- plot_choices_rsa(res.rsa.rt.h, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_curve_rsa(res.rsa.rt.a.adv, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Agreeableness-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
f <- plot_curve_rsa(res.rsa.rt.h.adv, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Honesty-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
g <- plot_choices_rsa(res.rsa.rt.a.adv, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
h <- plot_choices_rsa(res.rsa.rt.h.adv, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
i <- plot_curve_rsa(res.rsa.rt.a.dec, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Agreeableness-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
j <- plot_curve_rsa(res.rsa.rt.h.dec, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Honesty-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
k <- plot_choices_rsa(res.rsa.rt.a.dec, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
l <- plot_choices_rsa(res.rsa.rt.h.dec, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.8,1,1.8,1,1.8))
Figure 23: Descriptive specification curves of trait-state congruence and state-situation congruence predicting Stroop reaction times.
Error Rate
res.rsa.er.a <- res.rsa.er.a %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.er.h <- res.rsa.er.h %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.er.a.adv <- res.rsa.er.a.adv %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.er.a.dec <- res.rsa.er.a.dec %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.er.h.adv <- res.rsa.er.h.adv %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
res.rsa.er.h.dec <- res.rsa.er.h.dec %>%
mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))
a <- plot_curve_rsa(res.rsa.er.a, "est_b4", ci = FALSE, ribbon = T) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("Trait-State Agreeableness") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
scale_y_continuous(labels = function(x) x * 100) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_rsa(res.rsa.er.h, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + ggtitle("Trait-State Honesty-Humility") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_choices_rsa(res.rsa.er.a, "est_b4", choices = c("identification", "treatment", "conditions","controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
d <- plot_choices_rsa(res.rsa.er.h, "est_b4", choices = c("identification", "treatment", "conditions","controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_curve_rsa(res.rsa.er.a.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + ggtitle("State Agreeableness-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
f <- plot_curve_rsa(res.rsa.er.h.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
scale_y_continuous(labels = function(x) x * 100) +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("State Honesty-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
g <- plot_choices_rsa(res.rsa.er.a.adv, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
h <- plot_choices_rsa(res.rsa.er.h.adv, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
i <- plot_curve_rsa(res.rsa.er.a.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
scale_y_continuous(labels = function(x) x * 100) +
labs(x = "", y = "interaction effect\n(in % of errors)") +
theme_pub() + ggtitle("State Agreeableness-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
j <- plot_curve_rsa(res.rsa.er.h.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + ggtitle("State Honesty-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
k <- plot_choices_rsa(res.rsa.er.a.dec, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
l <- plot_choices_rsa(res.rsa.er.h.dec, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10, hjust=1),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.6,1,1.6,1,1.6))
Figure 24: Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates.
a <- plot_curve_rsa(res.rsa.er.a, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("Trait-State Agreeableness") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
b <- plot_curve_rsa(res.rsa.er.h, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("Trait-State Honesty-Humility") +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
c <- plot_choices_rsa(res.rsa.er.a, "fit", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
d <- plot_choices_rsa(res.rsa.er.h, "fit", choices = c("identification", "treatment","conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
e <- plot_curve_rsa(res.rsa.er.a.adv, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Agreeableness-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
f <- plot_curve_rsa(res.rsa.er.h.adv, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Honesty-Adversity")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
g <- plot_choices_rsa(res.rsa.er.a.adv, "fit", choices = c("identification", "treatment","conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
h <- plot_choices_rsa(res.rsa.er.h.adv, "fit", choices = c("identification", "treatment","conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
i <- plot_curve_rsa(res.rsa.er.a.dec, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Agreeableness-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
j <- plot_curve_rsa(res.rsa.er.h.dec, "fit", ci = FALSE, ribbon = FALSE) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(x = "", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + ggtitle("State Honesty-Deception")+
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left
k <- plot_choices_rsa(res.rsa.er.a.dec, "fit", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
l <- plot_choices_rsa(res.rsa.er.h.dec, "fit", choices = c("identification", "treatment", "conditions", "controls")) +
labs(x = "specifications (ranked)") + theme_pub() +
theme(strip.text.x = element_blank()) +
theme(axis.text.y = element_text(size=10),
axis.text.x = element_text(size=10),
strip.text.y = element_text(size=9),
axis.title=element_text(size=10,face="bold")) +
theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left
## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.6,1,1.6,1,1.6))
Figure 25: Descriptive specification curves of trait-state congruence and state-situation congruence predicting Stroop error rates.
Permutation Test
Reaction Time
## create table
tab <- data.frame(congruence.op=c(rep("Interaction",6), rep("Fit pattern",6)),
predictor=rep(c("Trait A x State A", "Trait HH x State HH",
"State A x Adversity", "State A x Deception",
"State HH x Adversity", "State HH x Deception"),2),
n.specs = NA,
med.effect = NA,
perc.sig = NA,
n.shuff = NA,
p.val = NA)
## fill table
tab[,3:7] <- res.rsa.rt[,2:6]
## format table
tab$p.val <- printp(tab$p.val)
tab$med.effect[c(7:12)] <- ifelse(tab$med.effect[c(7:12)]==0, "no", "yes")
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))
## print table
kable(tab,
align = c("l", "l", "r", "r", "r", "r", "r"),
col.names = c("Operationalization",
"Relevant predictor", "Number of specifications",
"Median effect size", "Significant specifications (%)",
"Number of shuffled samples with more significant
specifications than for the original sample", "<i>p</i> value of permutation test")) %>%
add_header_above(c(" " = 2,
"Description of specification curve (for original sample)" = 3,
"Permutation test with 500 shuffled samples" = 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | Operationalization | Relevant predictor | Number of specifications | Median effect size | Significant specifications (%) | Number of shuffled samples with more significant specifications than for the original sample | <i>p</i> value of permutation test |
|---|---|---|---|---|---|---|
| Interaction | Trait A x State A | 8896 | -1.15 | 0% | 400 | > .999 |
| Interaction | Trait HH x State HH | 8896 | -5.02 | 3% | 105 | .210 |
| Interaction | State A x Adversity | 8896 | 3.07 | 0% | 192 | .384 |
| Interaction | State A x Deception | 8896 | -4.25 | 0% | 500 | > .999 |
| Interaction | State HH x Adversity | 8896 | -2.36 | 0% | 500 | > .999 |
| Interaction | State HH x Deception | 8896 | -0.58 | 0% | 500 | > .999 |
| Fit pattern | Trait A x State A | 8896 | no | 0% | 161 | .402 |
| Fit pattern | Trait HH x State HH | 8896 | no | 1% | 89 | .178 |
| Fit pattern | State A x Adversity | 8896 | no | 0% | 500 | > .999 |
| Fit pattern | State A x Deception | 8896 | no | 0% | 500 | > .999 |
| Fit pattern | State HH x Adversity | 8896 | no | 0% | 500 | > .999 |
| Fit pattern | State HH x Deception | 8896 | no | 0% | 500 | > .999 |
Error Rate
## create table
tab <- data.frame(congruence.op=c(rep("Interaction",6), rep("Fit pattern",6)),
predictor=rep(c("Trait A x State A", "Trait HH x State HH",
"State A x Adversity", "State A x Deception",
"State HH x Adversity", "State HH x Deception"),2),
n.specs = NA,
med.effect = NA,
perc.sig = NA,
n.shuff = NA,
p.val = NA)
## fill table
tab[,3:7] <- res.rsa.error[,2:6]
## format table
tab$p.val <- printp(tab$p.val)
tab$med.effect[c(7:12)] <- ifelse(tab$med.effect[c(7:12)]==0, "no", "yes")
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))
## print table
kable(tab,
align = c("l", "l", "r", "r", "r", "r", "r"),
col.names = c("Operationalization",
"Relevant predictor", "Number of specifications", "Median effect size",
"Significant specifications (%)", "Number of shuffled samples with more significant
specifications than for the original sample", "<i>p</i> value of permutation test")) %>%
add_header_above(c(" " = 2,
"Description of specification curve (for original sample)" = 3,
"Permutation test with 500 shuffled samples" = 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) | Operationalization | Relevant predictor | Number of specifications | Median effect size | Significant specifications (%) | Number of shuffled samples with more significant specifications than for the original sample | <i>p</i> value of permutation test |
|---|---|---|---|---|---|---|
| Interaction | Trait A x State A | 2224 | 0 | 1% | 274 | .548 |
| Interaction | Trait HH x State HH | 2224 | 0 | 0% | 500 | > .999 |
| Interaction | State A x Adversity | 2224 | 0 | 0% | 500 | > .999 |
| Interaction | State A x Deception | 2224 | 0 | 0% | 420 | .840 |
| Interaction | State HH x Adversity | 2224 | 0 | 0% | 441 | .882 |
| Interaction | State HH x Deception | 2224 | 0 | 0% | 396 | .792 |
| Fit pattern | Trait A x State A | 2224 | no | 2% | 217 | .434 |
| Fit pattern | Trait HH x State HH | 2224 | no | 7% | 89 | .178 |
| Fit pattern | State A x Adversity | 2224 | no | 0% | 500 | > .999 |
| Fit pattern | State A x Deception | 2224 | no | 20% | 30 | .060 |
| Fit pattern | State HH x Adversity | 2224 | no | 0% | 416 | .832 |
| Fit pattern | State HH x Deception | 2224 | no | 25% | 15 | .030 |
Inferential Specification Curves
Reaction Time
a <- plot_curve_rsa(res.rsa.rt.a, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.trait.a.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")
b <- plot_curve_rsa(res.rsa.rt.h, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.trait.h.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility")
c <- plot_curve_rsa(res.rsa.rt.a.adv, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.a.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity")
d <- plot_curve_rsa(res.rsa.rt.h.adv, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.h.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")
e <- plot_curve_rsa(res.rsa.rt.a.dec, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.a.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")
f <- plot_curve_rsa(res.rsa.rt.h.dec, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.h.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")
cowplot::plot_grid(a,b,c,NULL,NULL,NULL, ncol=2, labels=c("A","B","C","D","E","F"),
label_size = 12, align = "v", axis = "rbl")
Figure 26: Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates).
a <- plot_curve_rsa(res.rsa.rt.a, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.trait.a.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")
b <- plot_curve_rsa(res.rsa.rt.h, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.trait.h.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility")
c <- plot_curve_rsa(res.rsa.rt.a.adv, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.a.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity")
d <- plot_curve_rsa(res.rsa.rt.h.adv, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.h.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")
e <- plot_curve_rsa(res.rsa.rt.a.dec, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.a.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")
f <- plot_curve_rsa(res.rsa.rt.h.dec, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.h.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")
cowplot::plot_grid(a,b,c,d,e,f, ncol=2, labels=c("A","B","C","D","E","F"),
label_size = 12, align = "v", axis = "rbl")Error Rate
a <- plot_curve_rsa(res.rsa.er.a, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.trait.a.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")
b <- plot_curve_rsa(res.rsa.er.h, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.trait.h.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility")
c <- plot_curve_rsa(res.rsa.er.a.adv, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.a.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity")
d <- plot_curve_rsa(res.rsa.er.h.adv, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.h.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")
e <- plot_curve_rsa(res.rsa.er.a.dec, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.a.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")
f <- plot_curve_rsa(res.rsa.er.h.dec, "est_b4", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
scale_y_continuous(labels = function(x) x * 100) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.h.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")
## combine plots
(a|b)/
(c|d)/
(e|f) + plot_annotation(tag_levels = 'A') 
Figure 27: Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates).
a <- plot_curve_rsa(res.rsa.er.a, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.trait.a.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")
b <- plot_curve_rsa(res.rsa.er.h, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.trait.h.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility")
c <- plot_curve_rsa(res.rsa.er.a.adv, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.a.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity")
d <- plot_curve_rsa(res.rsa.er.h.adv, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.h.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")
e <- plot_curve_rsa(res.rsa.er.a.dec, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.a.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")
f <- plot_curve_rsa(res.rsa.er.h.dec, "fit", ci = FALSE, ribbon = F) +
geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
labs(x = "specifications (ranked)", y = "fit pattern") +
scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.h.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
geom_point(aes(color = "black"), size = 1) +
theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")
(a|b)/
(c|d)/
(e|f) + plot_annotation(tag_levels = 'A') Exploratory Analyses
Difference Scores for Congruence
Descriptive Statistics
We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study.
data$diff.a <- data$state.a-data$trait.a.long
data$diff.h <- data$state.h-data$trait.h.long
data$diff.a.a <- data$state.a-8-data$adv
data$diff.d.a <- data$state.a-8-data$dec
data$diff.a.h <- data$state.h-8-data$adv
data$diff.d.h <- data$state.h-8-data$dec
tab <- bind_rows(as.data.frame(describe(data$diff.a))[,2:13],
as.data.frame(describe(data$diff.h))[,2:13],
as.data.frame(describe(data$diff.a.a))[,2:13],
as.data.frame(describe(data$diff.d.a))[,2:13],
as.data.frame(describe(data$diff.a.h))[,2:13],
as.data.frame(describe(data$diff.d.h))[,2:13]
)
row.names(tab) <- c("State A - Trait A", "State HH - Trait HH",
"State A - Adversity(r)", "State A - Deception(r)",
"State HH - Adversity(r)", "State HH - Deception(r)")
kable(tab,
digits=2,
row.names = TRUE,
col.names = c("n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis", "se"),
caption = "Descriptive statistics of the descriptive difference scores representing congruence in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
footnote(general="Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. ")| n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| State A - Trait A | 257 | 0.75 | 1.62 | 0.88 | 0.81 | 1.67 | -4.88 | 4.5 | 9.38 | -0.32 | 0.06 | 0.10 |
| State HH - Trait HH | 257 | -1.14 | 2.28 | -1.00 | -1.15 | 2.59 | -6.00 | 4.5 | 10.50 | 0.06 | -0.52 | 0.14 |
| State A - Adversity(r) | 254 | -5.70 | 2.44 | -5.50 | -5.61 | 2.84 | -11.83 | -2.0 | 9.83 | -0.28 | -0.76 | 0.15 |
| State A - Deception(r) | 254 | -8.60 | 2.13 | -8.54 | -8.60 | 2.16 | -14.00 | -2.0 | 12.00 | 0.07 | -0.05 | 0.13 |
| State HH - Adversity(r) | 254 | -6.05 | 2.58 | -6.33 | -6.01 | 2.84 | -12.00 | -2.0 | 10.00 | -0.04 | -0.90 | 0.16 |
| State HH - Deception(r) | 254 | -8.93 | 2.49 | -8.54 | -8.91 | 2.53 | -14.00 | -3.0 | 11.00 | -0.12 | -0.68 | 0.16 |
| Note: | ||||||||||||
| Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. |
Histograms
We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study.
a <- ggplot(data=data, aes(x=trait.h-state.h)) + theme_pub() +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="Trait-State Congruence:\nHonesty", x="Deviation of state honesty from trait honesty")
b <- ggplot(data=data, aes(x=trait.a-state.a)) + theme_pub() +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="Trait-State Congruence:\nAgreeableness", x="Deviation of state agreeableness from trait agreeableness")
c <- ggplot(data=data, aes(x=8-adv-state.a)) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="State-Situation Congruence:\nAgreeableness and Adversity",
x="Deviation of state agreeableness from adversity (reverse coded)")
d <- ggplot(data=data, aes(x=8-dec-state.a)) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="State-Situation Congruence:\nAgreeableness and Deception",
x="Deviation of state agreeableness from deception (reverse coded)")
e <- ggplot(data=data, aes(x=8-adv-state.h)) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="State-Situation Congruence:\nHonesty and Adversity",
x="Deviation of state honesty from adversity (reverse coded)")
f <- ggplot(data=data, aes(x=8-dec-state.h)) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) +
scale_y_continuous(expand = c(0, 0.01)) +
labs(title="State-Situation Congruence:\nHonesty and Deception",
x="Deviation of state honesty from deception (reverse coded)")
(a + b)/
plot_spacer()/
(c + d)/
plot_spacer()/
(e + f) + plot_annotation(tag_levels="A") + plot_layout(heights=c(1,0.2,1,0.2,1))
Figure 28: Histograms of difference scores between between personality traits and personality states and between situation characteristics and personality states as a descriptive indicator of congruence.
Difference Scores in the Experimental Conditions
Descriptive Statistics
We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study.
data$diff.a <- data$state.a-data$trait.a.long
data$diff.h <- data$state.h-data$trait.h.long
data$diff.a.a <- 8-data$adv-data$state.a
data$diff.d.a <- 8-data$dec-data$state.a
data$diff.a.h <- 8-data$adv-data$state.h
data$diff.d.h <- 8-data$dec-data$state.h
tab <- bind_rows(describeBy(data$diff.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$diff.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$diff.a.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$diff.d.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$diff.a.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
describeBy(data$diff.d.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15]
)
names(tab)[1] <- "Experimental conditions"
kable(tab,
row.names = FALSE,
col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median",
"trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
caption = "Descriptive statistics of the descriptive difference scores representing congruence in the different experimental groups.") %>%
kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>%
pack_rows("DV: Difference between trait agreeableness and state agreeableness", 1, 4) %>%
pack_rows("DV: Difference between trait honesty-humility and state honesty-humility", 5, 8) %>%
pack_rows("DV: Difference between aversity(r) and state agreeableness", 9, 12) %>%
pack_rows("DV: Difference between deception(r) and state agreeableness", 13, 16) %>%
pack_rows("DV: Difference between adversity(r) and state honesty-humility", 17, 20) %>%
pack_rows("DV: Difference between deception(r) and state honesty-humility", 21, 24) %>%
footnote(general="Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. ")| behavior condition | situation condition | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DV: Difference between trait agreeableness and state agreeableness | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | 0.38 | 1.58 | 0.12 | 0.37 | 1.30 | -3.00 | 4.50 | 7.50 | 0.16 | -0.14 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | 1.46 | 1.27 | 1.50 | 1.50 | 1.11 | -2.62 | 4.38 | 7.00 | -0.46 | 0.73 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 0.38 | 1.67 | 0.38 | 0.36 | 1.67 | -3.00 | 3.75 | 6.75 | 0.13 | -0.41 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 0.80 | 1.73 | 1.38 | 0.93 | 1.48 | -4.88 | 4.50 | 9.38 | -0.78 | 0.78 |
| DV: Difference between trait honesty-humility and state honesty-humility | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 70 | -1.87 | 2.28 | -2.00 | -2.01 | 2.69 | -5.38 | 4.50 | 9.88 | 0.57 | -0.19 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | -0.21 | 1.54 | 0.12 | -0.19 | 1.48 | -3.62 | 4.12 | 7.75 | -0.05 | 0.02 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | -2.02 | 2.48 | -2.62 | -2.12 | 2.41 | -6.00 | 4.50 | 10.50 | 0.46 | -0.74 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | -0.40 | 2.18 | -0.25 | -0.42 | 1.67 | -5.25 | 4.50 | 9.75 | 0.02 | -0.10 |
| DV: Difference between aversity(r) and state agreeableness | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 69 | 1.16 | 2.17 | 1.17 | 1.24 | 1.73 | -6.00 | 6.00 | 12.00 | -0.49 | 1.10 |
| high agreeableness and honesty | low adversity and deception | 1 | 65 | 0.48 | 1.34 | 0.50 | 0.50 | 0.86 | -3.33 | 4.25 | 7.58 | -0.23 | 0.90 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 1.17 | 2.08 | 1.00 | 1.24 | 1.98 | -6.00 | 5.25 | 11.25 | -0.51 | 0.82 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 0.17 | 2.19 | 0.50 | 0.25 | 1.73 | -6.00 | 4.67 | 10.67 | -0.47 | 0.21 |
| DV: Difference between deception(r) and state agreeableness | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 68 | -1.81 | 1.97 | -1.75 | -1.87 | 1.85 | -6.00 | 4.00 | 10.00 | 0.46 | 0.71 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | -2.66 | 2.00 | -2.75 | -2.75 | 1.85 | -6.00 | 3.58 | 9.58 | 0.49 | 0.24 |
| low agreeableness and honesty | high adversity and deception | 1 | 60 | -1.79 | 1.90 | -1.54 | -1.78 | 1.67 | -6.00 | 4.00 | 10.00 | 0.13 | 0.79 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | -2.40 | 2.05 | -2.08 | -2.47 | 1.98 | -6.00 | 6.00 | 12.00 | 0.93 | 2.83 |
| DV: Difference between adversity(r) and state honesty-humility | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 69 | 1.80 | 2.44 | 1.92 | 1.88 | 2.84 | -6.00 | 6.00 | 12.00 | -0.37 | 0.13 |
| high agreeableness and honesty | low adversity and deception | 1 | 65 | 0.46 | 1.57 | 0.25 | 0.47 | 1.24 | -3.33 | 4.42 | 7.75 | -0.03 | -0.02 |
| low agreeableness and honesty | high adversity and deception | 1 | 61 | 2.03 | 2.47 | 2.17 | 2.11 | 3.09 | -6.00 | 6.00 | 12.00 | -0.47 | 0.13 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | 0.04 | 2.42 | 0.17 | 0.19 | 1.98 | -6.00 | 5.75 | 11.75 | -0.56 | 0.60 |
| DV: Difference between deception(r) and state honesty-humility | |||||||||||||
| low agreeableness and honesty | low adversity and deception | 1 | 68 | -1.16 | 2.00 | -1.21 | -1.20 | 1.54 | -6.00 | 4.00 | 10.00 | 0.10 | 0.72 |
| high agreeableness and honesty | low adversity and deception | 1 | 67 | -2.68 | 2.13 | -3.00 | -2.78 | 2.22 | -6.00 | 3.00 | 9.00 | 0.44 | -0.17 |
| low agreeableness and honesty | high adversity and deception | 1 | 60 | -0.96 | 1.95 | -0.92 | -0.88 | 1.61 | -6.00 | 4.00 | 10.00 | -0.37 | 0.42 |
| high agreeableness and honesty | high adversity and deception | 1 | 59 | -2.52 | 2.01 | -2.50 | -2.60 | 2.22 | -6.00 | 5.25 | 11.25 | 0.82 | 1.94 |
| Note: | |||||||||||||
| Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. | |||||||||||||
Histograms
We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study.
p1 <- ggplot(subset(data, condition.beh=="high agreeableness and honesty"), aes(x=diff.a)) +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
labs(title="high agreeableness and honesty-condition",
x="Deviation of state agreeableness from trait agreeableness") + xlim(-6,6) + theme_pub()
p2 <- ggplot(subset(data, condition.beh=="low agreeableness and honesty"), aes(x=diff.a)) +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
labs(title="low agreeableness and honesty-condition",
x="Deviation of state agreeableness from trait agreeableness") + xlim(-6,6) + theme_pub()
## combine plots
(p1+p2) + plot_annotation(
title = 'Trait-State Congruence',
subtitle = 'Difference scores between trait agreeableness and state agreeableness in the two behavior conditions'
) 
Figure 29: Histograms of difference scores between between personality traits and personality states as a descriptive indicator of congruence in the different experimental groups.
p1 <- ggplot(subset(data, condition.beh=="high agreeableness and honesty"), aes(x=diff.h)) +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
labs(title="high agreeableness and honesty-condition",
x="Deviation of state honesty-humility from trait honesty-humility") + xlim(-6,6) + theme_pub()
p2 <- ggplot(subset(data, condition.beh=="low agreeableness and honesty"), aes(x=diff.h)) +
geom_histogram(color="black", fill=cols.beh[1], binwidth=1) +
labs(title="low agreeableness and honesty-condition",
x="Deviation of state honesty-humility from trait honesty-humility") + xlim(-6,6) + theme_pub()
## combine plots
(p1+p2) + plot_annotation(
title = 'Trait-State Congruence',
subtitle = 'Difference scores between trait honesty and state honesty in the two behavior conditions'
) 
Figure 30: Histograms of difference scores between between personality traits and personality states as a descriptive indicator of congruence in the different experimental groups.
p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behehavior: high, situation: high)",
x="Deviation of state agreeableness from adversity (reverse coded)")
p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behavior: low, situation: low)",
x="Deviation of state agreeableness from adversity (reverse coded)")
p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: high, situation: low)",
x="Deviation of state agreeableness from adversity (reverse coded)")
p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: low, situation: high)",
x="Deviation of state agreeableness from adversity (reverse coded)")
## combine plots
(p1+p2)/
plot_spacer()/
(p3+p4) + plot_layout(heights=c(1,0.2,1)) +
plot_annotation(
title = 'State-Situation Congruence',
subtitle = 'Difference scores between reverse-coded adversity and state agreeableness in the four experimental groups'
) 
Figure 31: Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups.
p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behehavior: high, situation: high)",
x="Deviation of state agreeableness from deception (reverse coded)")
p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behavior: low, situation: low)",
x="Deviation of state agreeableness from deception (reverse coded)")
p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: high, situation: low)",
x="Deviation of state agreeableness from deception (reverse coded)")
p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: low, situation: high)",
x="Deviation of state agreeableness from deception (reverse coded)")
## combine plots
(p1+p2)/
plot_spacer()/
(p3+p4) + plot_layout(heights=c(1,0.2,1)) +
plot_annotation(
title = 'State-Situation Congruence',
subtitle = 'Difference scores between reverse-coded deception and state agreeableness in the four experimental groups'
) 
Figure 32: Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups.
p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behehavior: high, situation: high)",
x="Deviation of state honesty from adversity (reverse coded)")
p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behavior: low, situation: low)",
x="Deviation of state honesty from adversity (reverse coded)")
p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: high, situation: low)",
x="Deviation of state honesty from adversity (reverse coded)")
p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: low, situation: high)",
x="Deviation of state honesty from adversity (reverse coded)")
## combine plots
(p1+p2)/
plot_spacer()/
(p3+p4) + plot_layout(heights=c(1,0.2,1)) +
plot_annotation(
title = 'State-Situation Congruence',
subtitle = 'Difference scores between reverse-coded adversity and state honesty in the four experimental groups'
)
Figure 33: Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups.
p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behehavior: high, situation: high)",
x="Deviation of state honesty from deception (reverse coded)")
p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="congruent", subtitle="(behavior: low, situation: low)",
x="Deviation of state honesty from deception (reverse coded)")
p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: high, situation: low)",
x="Deviation of state honesty from deception (reverse coded)")
p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
geom_histogram(color="black", fill=cols.sit[1], binwidth=1) +
labs(title="incongruent", subtitle="(behavior: low, situation: high)",
x="Deviation of state honesty from deception (reverse coded)")
## combine plots
(p1+p2)/
plot_spacer()/
(p3+p4) + plot_layout(heights=c(1,0.2,1)) +
plot_annotation(
title = 'State-Situation Congruence',
subtitle = 'Difference scores between reverse-coded deception and state honesty in the four experimental groups'
) 
Figure 34: Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups.
---
title: "Online Supplementary Material"
author: "Be Yourself and Behave Appropriately: Exploring Associations between Incongruent Personality States and Positive Affect, Tiredness, and Cognitive Performance"
output:
  bookdown::html_document2:
    theme: paper
    code_folding: hide
    code_download: true
    toc: true
    toc_depth: 1
    toc_float:
      collapsed: false
      smooth_scroll: false
---

```{css ccs-styling, echo=FALSE}

# this just some .css modifications to the template to make a nicer display

.list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus {
   z-index: 2;
   color: #ffffff;
      background-color: #808080;
      border-color: #808080;
}

body.main-container {
   max-width: 95% ;
   width: 95%;
}

body {
   max-width: 95%;
   font-size: 14px;
}

div.main-container {
   width: 95%;
   max-width: 95%;
}

.col-lg-3 {
   width: 25%;
}

a {
   color: #808080;
      text-decoration: none;
}
a {
   background-color: transparent;
}

a:hover {
   color: #808080;
      background-color: #DDDDDD;
}

.nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus {
   color: #ffffff;
      background-color: #808080;
}

div.tocify {
   width: 20%;
   max-width: 260px;
   max-height: 85%;
}

.list-group-item {
   position: relative;
   display: block;
   padding: 20px 0px 20px 0px;
   margin: 20px 0px 20px 0px;
   color: #808080;
      background-color: #ffffff;
      border: 1px solid #dddddd;
}

.tocify ul, .tocify li {
   list-style: none;
   margin: 10px 0px 10px 0px;
   padding: 0;
   border: none;
   line-height: 20px;
}

.list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus {
   z-index: 2;
   color: #ffffff;
      background-color: #808080;
      border-color: #808080;
}

h1 {
   font-size: 24px !important;
   font-weight: bold; 
   margin-top: 5em !important;
}

h1.title {
   font-size: 30px !important;
}

h2 {
   font-size: 24px !important;
   margin-top: 3em !important;
}

h3 {
   font-size: 20px;
   margin-top: 3em !important;
}

h3.subtitle {
   font-size: 24px !important;
   font-style: italic;
}

h4 {
   font-size: 20px !important;
   margin-top: 2em !important;
}

h5 {
   font-size: 18px !important;
   margin-top: 1em !important;
}

h6 {
   font-size: 16px !important;
   margin-top: 1em !important;
}

```

```{r setup, include = FALSE, echo = FALSE, message=FALSE, warning=FALSE}

knitr::opts_chunk$set(echo = TRUE)
options(knitr.kable.NA = '')

#### load packages

library(report)     # reporting packages
library(ggplot2)    # visualizations
library(papaja)     # reporting results
library(patchwork)  # plot layouts
library(cowplot)    # plot layouts
library(tidyverse)  # data preparation
library(psych)      # descriptives
library(kableExtra) # table layouts
library(DT)         # table layouts
library(RSA)        # response surface analysis
library(specr)      # specification curve analysis
library(afex)       # analysis of variance
library(corx)       # correlation tables
library(readxl)     # import excel file
library(Gmisc)      # flowchart
library(glue)       # flowchart helper
library(grid)       # flowchart helper



#### load custom functions

source("Scripts/functions.R")
source("Scripts/functions_specification curve analysis_new.R")



#### load data

load("Data/Pretest/data_pretest.RData")
load("Data/Main study/data_raw.RData") # raw data
load("Data/Main study/data_clean.RData") # raw data
load("Data/Main study/data_complete.RData") # final data
load("Data/Main Study/results_specification curve analysis.RData") # results from all specification curve analyses
codebook <- read_xlsx("Data/Main study/codebook_data_complete.xlsx") # codebook final data
codebook.raw <- read_xlsx("Data/Main study/codebook_raw_data.xlsx")

### transform personality traits to a 7-point scale
data$trait.a.long <- 6*(data$trait.a-1)/4+1
data$trait.h.long <- 6*(data$trait.h-1)/4+1
data$trait.a.long.mc <- data$trait.a.long-4
data$trait.h.long.mc <- data$trait.h.long-4

### change names of experimental conditions
levels(data$condition.beh) <- c("low agreeableness and honesty", "high agreeableness and honesty")
levels(data$condition.sit) <- c("low adversity and deception", "high adversity and deception ")

### center
data$points.c <- as.numeric(scale(data$points, scale=FALSE))



#### set colors

cols.beh = inlmisc::GetColors(11, scheme = "sunset")[c(11,10)]
cols.sit = inlmisc::GetColors(11, scheme = "sunset")[1:2]



#### analysis preferences

knitr::opts_chunk$set(cache.extra = knitr::rand_seed) # Discard cache when random seed changes
knitr::opts_chunk$set(cache.path = "Data/cache/OSM/")
knitr::opts_chunk$set(fig.path = "Output/OSM/")
knitr::opts_chunk$set(cache.comments = FALSE) # Ignore changes to comments
knitr::opts_chunk$set(cache.extra = list(R.version, sessionInfo())) #Discard cache if the R environment changes
knitr::opts_chunk$set(dev = c("jpeg", "svg", "pdf"))

```

# ReadMe {.tabset .tabset-pills -}

This document includes an overview of all analyses reported in the paper as well as additional tables and figures for the paper "*Be Yourself and Behave Appropriately: Exploring Associations between Incongruent Personality States and Positive Affect, Tiredness, and Cognitive Performance*". 

**Tabs**  
To make the document easier to read, we use tabs. You will find tabs in gray in many places, where you can choose between different information (e.g., tables or graphics on a topic). *Note*: Sometimes there are several levels of tabs, so for example, on the first level you decide whether you want to see tables or graphics and on the next level you choose a sport.

**Analysis Code**  
The document includes all R code used to create the graphs and tables or to perform the analyses. However, the code is hidden by default to enhance readability. If you want to see the code, simply click on the respective **CODE** button in the right hand side just above the figure or table---you will then see the associated codechunk. If you want to show all codechunks by default, click **CODE** > **Show all Code** at the very top of the document. Moreover, you can also download the RMarkdown source document there.


# Methodological Aspects {.tabset .tabset-pills -}

## Participants {-}

```{r participant-flow, fig.asp=1, fig.width=11, out.width="90%", fig.keep='last', warning=F, message=F, fig.cap="Flow of participants through the study including exclusion criteria, dropouts, and final sample sizes."}

data.raw <- data.raw %>% 
   mutate(screening = factor(ifelse(screener.1==1 & screener.2==4 & screener.3==2 & screener.4==1 & screener.5==1,"passed", "failed")),
          requirements = factor(ifelse(consent==1 & old.enough==1 & device%in%c(1,2),"passed", "failed")),
          careful.responding = factor(ifelse(bhi_25==3 & sit_13==6,"passed", "failed")),
          exclude = ifelse(screening=="passed" & requirements=="passed" & careful.responding=="passed", FALSE, TRUE))


start <- boxGrob(glue("N = {pop}",
                      "participants started the study",
                      pop = txtInt(nrow(data.raw)),
                      .sep = "\n"),
                 txt_gp = gpar(cex = 0.8),
                 box_gp = gpar(fill = "#d6d6d6"),
                 width = 0.25)
screening <- boxGrob(glue("Language comprehension and\nattention screening",
                          "N = {pop}",
                          pop = nrow(subset(data.raw, requirements!="failed")),
                          .sep = "\n"),
                     txt_gp = gpar(cex = 0.8),
                     box_gp = gpar(fill = "#d6d6d6"),
                     width = 0.25)
traits <- boxGrob(glue("Personality trait assessment &\ndemographics",
                       "N = {incl}",
                       incl = nrow(subset(data.raw, screening=="passed" & requirements=="passed")),
                       .sep = "\n"),
                  txt_gp = gpar(cex = 0.8),
                  box_gp = gpar(fill = "#d6d6d6"),
                  width = 0.25)

manip <- boxGrob(glue("Introduction and instruction for the game",
                      "(incl. manipulation of situation and behavior)",
                      "N = {incl}",
                      incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[3:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
                      .sep = "\n"),
                 txt_gp = gpar(cex = 0.8),
                 box_gp = gpar(fill = "#d6d6d6"),
                 width = 0.25)

game <- boxGrob(glue("Prisoner's Dilemma game",
                     "N = {incl}",
                     incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[5:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
                     .sep = "\n"),
                txt_gp = gpar(cex = 0.8),
                box_gp = gpar(fill = "#d6d6d6"),
                width = 0.25)

states <- boxGrob(glue("Assessment of situation characteristics,",
                       " personality states, positive affect, and",
                       " tiredness during the game",
                       "N = {incl}",
                       incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[14:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
                       .sep = "\n"),
                  txt_gp = gpar(cex = 0.8),
                  box_gp = gpar(fill = "#d6d6d6"),
                  width = 0.25)

stroop <- boxGrob(glue("Numerical Stroop task",
                       "N = {incl}",
                       incl = sum(table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[15:length(unique(subset(data.raw, screening=="passed" & requirements=="passed")$pb))]),
                       .sep = "\n"),
                  txt_gp = gpar(cex = 0.8),
                  box_gp = gpar(fill = "#d6d6d6"),
                  width = 0.25)

comments <- boxGrob(glue("Comments and personalized code",
                         "N = {incl}",
                         incl = table(as.numeric(subset(data.raw, screening=="passed" & requirements=="passed")$pb))[18],
                         .sep = "\n"),
                    txt_gp = gpar(cex = 0.8),
                    box_gp = gpar(fill = "#d6d6d6"),
                    width = 0.25)

excluded <- boxGrob(glue("Exclusion Criteria:",
                         " - No consent (N = {uninterested})",
                         " - Younger than 18 (N = {tooyoung})",
                         " - No desktop or laptop (N = {device})",
                         uninterested = nrow(subset(data.raw, consent==2 | is.na(consent))),
                         tooyoung = nrow(subset(data.raw, old.enough==2 | is.na(old.enough))) - nrow(subset(data.raw, consent==2 | is.na(consent))),
                         device = nrow(subset(data.raw, !(device%in%c(1,2)))) - nrow(subset(data.raw, old.enough==2 | is.na(old.enough))),
                         .sep = "\n"),
                    just = "left",
                    txt_gp = gpar(cex = 0.8),
                    box_gp = gpar(fill = "#ffafaf"),
                    width = 0.2)

excluded2 <- boxGrob(glue("Exclusion Criteria:",
                          " - Failed screening (N = {screening})",
                          screening = nrow(subset(data.raw, requirements=="passed" & screening=="failed")),
                          .sep = "\n"),
                     just = "left",
                     txt_gp = gpar(cex = 0.8),
                     box_gp = gpar(fill = "#ffafaf"),
                     width = 0.2)

final1 <- boxGrob(glue("Final sample for analyses\nwith positive affect and\ntiredness as DV:",
                       "N = {final1}*",
                       final1 = nrow(data),
                       .sep = "\n"),
                  just = "center",
                  txt_gp = gpar(cex = 0.8),
                  box_gp = gpar(fill = "#add8a4"),
                  width = 0.2)

final2 <- boxGrob(glue("Final sample for analyses\nwith Stroop performance as DV:",
                       "N = {final2}*",
                       final2 = nrow(subset(data, progress==96)),
                       .sep = "\n"),
                  txt_gp = gpar(cex = 0.8),
                  box_gp = gpar(fill = "#add8a4"),
                  width = 0.2,
                  just = "center")

note <- boxGrob(glue("* N = {careless} participants were exluded because of careless responding",
                     careless = nrow(subset(data.raw, pb>65)) - nrow(subset(data, progress>65)),
                     .sep = "\n"),
                txt_gp = gpar(cex = 0.8),
                box_gp = gpar(fill = "white", col="white"),
                just = "left")



grid.newpage()
vert <- spreadVertical(start = start,
                       screening = screening,
                       traits = traits,
                       manip = manip,
                       game = game,
                       states = states,
                       stroop = stroop,
                       comments = comments,
                       final2 = final2)

excluded <- moveBox(excluded, x = .8, y = coords(vert$screening)$top + distance(vert$start, vert$screening, half = TRUE))
excluded2 <- moveBox(excluded2, x = .8, y = coords(vert$traits)$top + distance(vert$screening, vert$traits, half = TRUE))
final1 <- moveBox(final1, x = .2, y = coords(vert$stroop)$top + distance(vert$states, vert$stroop, half = TRUE))
vert$final2 <- moveBox(vert$final2, x = .2)
note <- moveBox(note, x=.8, y=.01)

# print arrows
for (i in 1:(length(vert) - 2)) {
   connectGrob(vert[[i]], vert[[i + 1]], type = "vert", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches")))) %>%
      print
}

connectGrob(vert$start, excluded, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$traits, excluded2, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$states, final1, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))
connectGrob(vert$comments, vert$final2, type = "L", arrow_obj = getOption("connectGrobArrow", default = arrow(ends = "last", type = "closed", length = unit(0.1, "inches"))))

# Print boxes
vert
excluded
excluded2
final1
note

```

## Manipulations {.tabset .tabset-pills -}

### Situation Manipulations {-}

Further information on the game and the manipulations can also be found in the "Documentation of the Game"-file that was upload with the preregistration.

The situation was manipulated in two ways. First, during the explanation of the game, the other partner was framed either as a partner or an opponent and was said the behave accordingly. Second, also during the explanation of the game, the payoffs were altered such that they either signal that stealing is not worthwhile or that stealing does pay off:

#### High Adversity and Deception-Condition {-}

>**Let's play a game!**
> 
>You were randomly assigned **an opponent**. You and your opponent will **play a game**: In every round, there is a jackpot of coins to win. Both of you have the **choice to either share the jackpot** with your opponent **or to steal the jackpot** from your opponent. 
>
>If you both choose to share, each of you will receive 15 coins.  
>If both steal, each will receive 10 coins.  
>If you share, but your opponent steals, your opponent will receive 40 coins and you none.  
The other way around, if you steal and your opponent shares, you will receive 40 coins and your opponent will receive none.   
>
>You will both decide simultaneously without knowing what the other will choose. However, in each round, you will have the **chance to communicate with your opponent which decision you plan to make**; but you are both allowed to lie. Unfortunately, **your opponent is a rather dishonest person and will often play dirty.** You will play multiple rounds. 

```{r}

t <- data.frame(a=c("", 
                    "<b style='color:red;display:inline;'>you</b> share", 
                    "<b style='color:red;display:inline;'>you</b> steal"), 
                b=c("<b style='color:blue;display:inline;'>your opponent</b> shares", 
                    "<b style='color:red;display:inline;'>15</b> | <b style='color:blue;display:inline;'>15</b>", 
                    "<b style='color:red;display:inline;'>40</b> | <b style='color:blue;display:inline;'>0</b>"), 
                c=c("<b style='color:blue;display:inline;'>your opponent</b> steals", 
                    "<b style='color:red;display:inline;'>0</b> | <b style='color:blue;display:inline;'>40</b>", 
                    "<b style='color:red;display:inline;'>10</b> | <b style='color:blue;display:inline;'>10</b>"))

kable(t, 
      col.names=c("", "", ""),
      align=c("r", "c", "c"),
      escape = F) %>% 
   kable_paper(bootstrap_options = "bordered", full_width = F, position = "left") %>% 
   column_spec(1, bold = T) %>%
   row_spec(1, bold = T)

```

#### Low Adversity and Deception-Condition {-}

>**Let's play a game!**
> 
>You were randomly assigned **a partner**. You and your partner will **play a game**: In every round, there is a jackpot of coins to win. Both of you have the **choice to either share** the jackpot with your partner **or to steal the jackpot** from your partner. 
>
> you both choose to share, each of you will receive 20 coins. 
>If both steal, the jackpot is lost and none of you receives anything.  
>If you share, but your partner steals, your partner will receive 30 coins and you will receive 5 coins. 
>The other way around, if you steal and your partner cooperates, you will receive 30 coins and your partner will receive 5 coins.
>
>You will both decide simultaneously without knowing what the other will choose. However, in each round, you will have the **chance to communicate with your partner which decision you plan to make**; but you are both allowed to lie. Luckily, **your partner is a pretty honest person and is most of the time true to their word**. You will play multiple rounds.   

```{r}
t <- data.frame(a=c("", 
                    "<b style='color:red;display:inline;'>you</b> share", 
                    "<b style='color:red;display:inline;'>you</b> steal"), 
                b=c("<b style='color:blue;display:inline;'>your partner</b> shares", 
                    "<b style='color:red;display:inline;'>20</b> | <b style='color:blue;display:inline;'>20</b>", 
                    "<b style='color:red;display:inline;'>30</b> | <b style='color:blue;display:inline;'>5</b>"), 
                c=c("<b style='color:blue;display:inline;'>your partner</b> steals", 
                    "<b style='color:red;display:inline;'>5</b> | <b style='color:blue;display:inline;'>30</b>", 
                    "<b style='color:red;display:inline;'>0</b> | <b style='color:blue;display:inline;'>0</b>"))

kable(t, 
      col.names=c("", "", ""),
      align=c("r", "c", "c"),
      escape = F) %>% 
   kable_paper(bootstrap_options = "bordered", full_width = F, position = "left") %>% 
   column_spec(1, bold = T) %>%
   row_spec(1, bold = T)

```

### Behavior Manipulations {-}

Further information on the game and the manipulations can also be found in the "Documentation of the Game"-file that was upload with the preregistration.

Participants' behaviors and personality states were manipulated during the instructions for the Prisoner's Dilemma game. Following the general explanation of the game, participants were informed about their task:

#### High Agreeableness and Honesty-Condition {-}

> **Your task**
>
> Your task is to **behave honestly and cooperatively** with your [partner/opponent]. **Please try to be true to your word most of the time.** Of course, you do not always have to do as you proposed, but please try to be honest and cooperative as much as you can."

#### Low Agreeableness and Honesty-Condition {-}

>**Your task**  
>
>Your task is to behave **dishonestly and uncooperatively** with your [partner/opponent]. **Please try to play dirty most of the time.** Of course, you do not always have to deceive your [partner/opponent], but please try to be dishonest and uncooperative as much as you can.  

## Measures {.tabset .tabset-pills -}

### Personality Traits {-}

HEXACO model personality traits were measured with the Brief Hexaco Inventory (de Vries, 2019). The original article including the items is:

De Vries, R. E. (2013). The 24-item brief hexaco inventory (bhi). Journal of Research in Personality, 47(6), 871–880. https://doi.org/10.1016/j.jrp.2013.09.003 

```{r}

tab <- codebook.raw %>% 
   filter(stringr::str_detect(`Variable Name`, 'bhi')) %>% 
   filter(`Variable Name` != "bhi_25") %>% 
   arrange(Description) %>% 
   mutate(`Reverse Coding` = ifelse(is.na(`Reverse Coding`),"No", `Reverse Coding`)) 

kable(tab[c(1:24),c(2,4,6,5)]
      , col.names = c("Item text", "Scale", "Facet", "Reverse coding necessary")
      , caption="Items of the BHI used for personality trait assessment"
      , escape=F
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)

```

### Personality States {-}

HEXACO model personality states were measured with bipolar items adopted from Sherman et al. (2015) and Churchyard (2013). All personality states were measured with one bipolar item each adopted from Sherman et al. (2015). State honesty-humility and state agreeableness were additionally measured with three items from Churchyard (2013), and all four items were combined into one scale score for these states. The original articles using these items are:

Sherman, R. A., Rauthmann, J. F., Brown, N. A., Serfass, D. G., & Jones, A. B. (2015). The independent effects of personality and situations on real-time expressions of behavior and emotion. Journal of Personality and Social Psychology, 109(5), 872–888. https://doi.org/10.1037/pspp0000036

Churchyard, J. S. (2013). Within-person variation in personality and psychological well-being [Dissertation]. University of Hertfordshire, Hertfordshire. https://doi.org/10.18745/th.15432


```{r}

tab <- codebook.raw %>% 
   filter(Description=="behavior") 

kable(tab[,c(2,6,5)]
      , col.names = c("Item text (bipolar)", "Scale", "Reverse coding necessary")
      , caption="Items of the MDMQ used for the assessment of tiredness"
      , escape=F
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general=paste0("The instruction was '", tab$Instruction[1], "'."), escape = F)

```

### Situation Characteristics {-}

Perceived situation characteristics from the Situational Eight DIAMONDS taxonomy (Rauthmann et al., 2014) were measured with the S8* (Rauthmann & Sherman, 2016a) for adversity and deception and with the shorter S8-I Rauthmann & Sherman, 2016b) for the remaining situation characteristics. The original articles including the items are: 

Rauthmann, J. F., & Sherman, R. A. (2016). Measuring the situational eight diamonds characteristics of situations. European Journal of Psychological Assessment, 32(2), 155–164. https://doi.org/10.1027/1015-5759/a000246

Rauthmann, J. F., & Sherman, R. A. (2016). Ultra-brief measures for the situational eight diamonds domains. European Journal of Psychological Assessment, 32(2), 165–174. https://doi.org/10.1027/1015-5759/a000245


```{r}

tab <- codebook.raw %>% 
   filter(stringr::str_detect(`Variable Name`, 'sit_')) %>% 
   filter(`Variable Name` != "sit_13") %>% 
   mutate(Description = ifelse(Subscale=="Adversity" | Subscale=="Deception", "S8* (Rauthmann & Sherman, 2016a)", "S8-I (Rauthmann & Sherman, 2016b)"),
          `Reverse Coding` = ifelse(is.na(`Reverse Coding`),"No", `Reverse Coding`)) 

kable(tab[,c(2,6,4,5)]
      , col.names = c("Item text", "Scale", "Questionnaire", "Reverse coding necessary")
      , caption="Items of the S8* and S8-I used for the assessment of perceived situation characteristics"
      , escape=F
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)

```

### Positive Affect {-}

Positive affect was measured with a short form of the English version of the multidimensional mood state questionnaire (Steyer et al., 1994). The original version can be found [here](https://www.metheval.uni-jena.de/mdbf.php). 

```{r}

tab <- codebook.raw %>% 
   filter(stringr::str_detect(Subscale, 'GB')) %>% 
   mutate(Subscale = "positive affect (good-bad mood)")

kable(tab[,c(2,4,6,5)]
      , col.names = c("Item text", "Scale", "Subscale", "Reverse coding necessary")
      , caption="Items of the MDMQ used for the assessment of positive affect"
      , escape=F
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)

```

### Tiredness {-}

Tiredness was measured with a short form of the English version of the multidimensional mood state questionnaire (Steyer et al., 1994). The original version can be found [here](https://www.metheval.uni-jena.de/mdbf.php). Tiredness was reverse-coded such that higher levels indicate less tiredness, or more active mood. 

```{r}

tab <- codebook.raw %>% 
   filter(stringr::str_detect(Subscale, 'AT')) %>% 
   mutate(Subscale = "tiredness (active-tired mood)")

kable(tab[,c(2,4,6,5)]
      , col.names = c("Item text", "Scale", "Subscale", "Reverse coding necessary")
      , caption="Items of the MDMQ used for the assessment of tiredness"
      , escape=F
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general=paste0("The instruction was '", tab$Instruction[1], "' and the response scale was ", tab$`Value Labels`[1], "."), escape = F)

```

## Analytical Strategy {.tabset .tabset-pills -}

### R Version and Packages {-}

This is a list of the packages used to prepare and analyze the data and to present the results. We explicitly thank the authors of all packages for the work they have put and are putting into the development and maintenance of the packages.   

```{r, results='asis'}

report_packages(include_R=FALSE, prefix="\n * ")

report_system()

```

### Planned Analyses {-}

The planned analyses were preregistered along with the study design. This preregistration,  including a detailed account of the analytic strategy, can be found on OSF: https://osf.io/hnu4b/

### Codebook of the Final Data {-}

This is the codebook for the final data (without the reaction time and error-rate specifications) to give an overview of the data.

```{r codebook, echo = TRUE, message=FALSE, warning=FALSE, error=FALSE}

# print table
datatable(codebook, 
          caption="Codebook of the final dataset.") 

```

## Pretest {.tabset .tabset-pills -}

We tested an earlier version of the experimental paradigm in a preliminary study to determine if the manipulation works. Here, we briefly summarize the design of the game and the manipulations and the findings from this pretest.

### Game {-}

These are the original instructions for the prisoner's dilemma game in the pretest:

> "You were randomly assigned a partner[opponent]. You and your partner[opponent] will play a
game: You are standing in front of a machine that multiplies and distributes
coins. In every round, each of you will receive a coin. You have the choice
to either put your coin into the machine (i.e., cooperate with your
partner[opponent]) or keep your coin (i.e., cheat on your partner[opponent]). Your partner[opponent] will
have the same choice.  
If you both cooperate, that is, both put their coin into the machine, each of
you will receive 19[10] coins out of the machine. If both cheat, each will receive
1[8] coin. If you cooperate, but your partner[opponent] cheats, your partner[opponent] will receive 20
coins and you none. The other way around, if you cheat and your partner[opponent]
cooperates, you will receive 20 coins and your partner[opponent] will receive none.
You will both decide simultaneously without knowing what the other will
choose. You will play multiple rounds."

In this version, we aimed to covertly manipulate the participants' behaviors by varying the goal of the game: 

> "Your task is to play with your partner/opponent such that you will
together gain as many coins as possible. In the end, your result will be
number of coins that you have gained on average, that is, the mean of
your and your partner/opponent's coins.
If you gained, for example, 300 coins and your partner/opponent gained
100, you will receive (300+100)/2, that is, 200 tickets for the lottery."
>
> --- high agreeableness and honesty-condition

> "Your task is to play against your partner/opponent such that you will
gain more points than your partner/opponent. In the end, your result
will be your advantage over your partner/opponent, that is, the
difference between your and your partner/opponent's coins.
If you gained, for example, 300 coins and your partner/opponent gained
100, you will receive 300-100, that is, 200 tickets for the lottery."
>
> --- low agreeableness and honesty-condition

The situation was manipulated by the description of the computer (i.e., partner or opponent) and by changing the payoffs of the game.

### Results {-}

```{r pretest-anovas-1, warning=FALSE, message=FALSE, error=FALSE}

aov.coop  <- aov_car(coopSum ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.agree <- aov_car(state.agreeableness ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.hon   <- aov_car(state.honesty.humility ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.adv   <- aov_car(adversity ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)
aov.dec   <- aov_car(deception ~ behavior_factor * situation_factor + Error(ResponseId), data=pretest)

tab <- bind_rows(as.data.frame(nice(aov.coop, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.agree, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.hon, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.adv, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.dec, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
)

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and the targeted personality states and situation characteristics in the pretest.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   pack_rows("DV: Sum of cooperative decisions", 1, 3) %>%
   pack_rows("DV: State agreeableness", 4, 6) %>%
   pack_rows("DV: State honesty-humility", 7, 9) %>%
   pack_rows("DV: Adversity", 10, 12) %>%
   pack_rows("DV: Deception", 13, 15) 

```

```{r pretest-plot, fig.width=11, fig.asp=1.2, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Associations between the experimental conditions and the targeted personality states and situation characteristics in the pretest."}

# plot interaction diagram
r1 <- ggplot(pretest %>% 
                group_by(situation_factor, behavior_factor) %>% 
                summarise(groups = mean(coopSum, na.rm=TRUE)), 
             aes(x = situation_factor, y = groups, color = behavior_factor)) +
   theme_pub() + xlab("Situation conditions") + ylab("Sum of cooperative decisions") + 
   scale_y_continuous(limits=c(1,15), labels=c(1:15), breaks=c(1:15)) +
   ggtitle("Cooperative Decisions") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") +
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = behavior_factor), size=1.5) +
   geom_point(size=2.5) + theme(legend.title = element_text(size=9))

r2 <- ggplot(pretest %>% 
                group_by(situation_factor, behavior_factor) %>% 
                summarise(groups = mean(state.agreeableness, na.rm=TRUE)), 
             aes(x = situation_factor, y = groups, color = behavior_factor)) +
   theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("State Agreeableness") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") +
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = behavior_factor), size=1.5) +
   geom_point(size=2.5) + theme(legend.title = element_text(size=9))

r3 <- ggplot(pretest %>% 
                group_by(situation_factor, behavior_factor) %>% 
                summarise(groups = mean(state.honesty.humility, na.rm=TRUE)), 
             aes(x = situation_factor, y = groups, color = behavior_factor)) +
   theme_pub() + xlab("Situation conditions") + ylab("State honesty-humility score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("State Honesty-Humility") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Behavior conditions") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = behavior_factor), size=1.5) +
   geom_point(size=2.5) + theme(legend.title = element_text(size=9))

r4 <- ggplot(pretest %>% 
                group_by(situation_factor, behavior_factor) %>% 
                summarise(groups = mean(adversity, na.rm=TRUE)), 
             aes(x = behavior_factor, y = groups, color = situation_factor)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Adversity score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Adversity") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Situation conditions") +
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = situation_factor), size=1.5) +
   geom_point(size=2.5) + theme(legend.title = element_text(size=9))

r5 <- ggplot(pretest %>% 
                group_by(situation_factor, behavior_factor) %>% 
                summarise(groups = mean(deception, na.rm=TRUE)), 
             aes(x = behavior_factor, y = groups, color = situation_factor)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Deception score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Deception") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color="Situation conditions") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = situation_factor), size=1.5) +
   geom_point(size=2.5) + theme(legend.title = element_text(size=9))


### combine plots
r1 + r2 + r3 + r4 + r5 + plot_layout(ncol=2) + plot_annotation(tag_levels = "A") 

```

### Conclusion {-}

The game in the pretest successfully manipulated participant's levels of state agreeableness and state honesty-humility, but it had no effect on the perceptions of adversity and deception. In sum, this pretest thus revealed that the manipulation of the situation was unsuccessful and that aiming to covertly manipulate behavior via goal instructions may have unwanted effect on the perception of the situations. We therefore adapted the experimental paradigm for the main study described in the methods below. 

# Descriptives {.tabset .tabset-pills -}

## Descriptive Statistics {-}

```{r big-five-desc, echo = FALSE, message=FALSE, warning=FALSE, cache=F, dependson="create-master-dataframes"}

## extract relevant variables
rel <- data %>% 
   ungroup() %>% 
   select(# Traits
      "trait.h", "trait.e", "trait.x", "trait.a", "trait.c", "trait.o",
      # Behaviors
      "state.h", "state.e", "state.x", "state.a", "state.c", "state.o", "nCoop", "nHon",
      # Situation
      "dut", "int", "adv", "mat", "pos", "neg", "dec", "soc",
      # Outcomes
      "mood.gb", "mood.at")


## arrange descriptives in table
tab <- round(as.data.frame(describe(rel))[,2:13],2)
row.names(tab) <- c(# Traits
   "Trait Honesty-Humility", "Trait Emotionality", "Trait Extraversion", "Trait Agreeableness", "Trait Conscientiousness", "Trait Openness",
   # Behaviors
   "State Honesty-Humility", "State Emotionality", "State Extraversion", "State Agreeableness", "State Conscientiousness", "State Openness", "Sharing Behaviors", "Honest Behaviors",
   # Situation
   "Duty", "Intellect", "Adversity", "Mating", "pOsitivity", "Negativity", "Deception", "Sociality",
   # Outcomes
   "Positive Affect", "Tiredness(r)")


# add reliability
raw <- data.raw %>% 
   mutate(screening = factor(ifelse(screener.1==1 & screener.2==4 & screener.3==2 & 
                                       screener.4==1 & screener.5==1,"passed", "failed")),
          requirements = factor(ifelse(consent==1 & old.enough==1 & device%in%c(1,2),"passed", "failed")),
          careful.responding = factor(ifelse(bhi_25==3 & sit_13==6,"passed", "failed")),
          exclude = ifelse(screening=="passed" & requirements=="passed" & careful.responding=="passed", FALSE, TRUE)) %>% 
   filter(exclude==FALSE)

tab$`Internal consistency (&alpha;)` <- round(c(psych::alpha(subset(raw, select=c("bhi_6", "bhi_12", "bhi_18", "bhi_24")), 
                                                             keys=c("bhi_12", "bhi_18", "bhi_24"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("bhi_5", "bhi_11", "bhi_17", "bhi_23")), 
                                                             keys=c("bhi_11", "bhi_17"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("bhi_4", "bhi_10", "bhi_16", "bhi_22")), 
                                                             keys=c("bhi_4", "bhi_22"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("bhi_3", "bhi_9", "bhi_15", "bhi_21")), 
                                                             keys=c("bhi_3", "bhi_9"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("bhi_2", "bhi_8", "bhi_14", "bhi_20")), 
                                                             keys=c("bhi_8", "bhi_20"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("bhi_1", "bhi_7", "bhi_13", "bhi_19")), 
                                                             keys=c("bhi_7"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("hexaco.state_1", "hexaco.state_2", "hexaco.state_3", 
                                                                                  "hexaco.state_4")))$total$std.alpha, NA, NA, 
                                                psych::alpha(subset(raw, select=c("hexaco.state_7", "hexaco.state_8", "hexaco.state_9", 
                                                                                  "hexaco.state_10")))$total$std.alpha,NA,NA,NA,NA,
                                                NA,NA,psych::alpha(subset(raw, select=c("sit_7", "sit_8", "sit_9")))$total$std.alpha,
                                                NA, NA, NA,psych::alpha(subset(raw, select=c("sit_10", "sit_11", "sit_12")))$total$std.alpha,NA,
                                                psych::alpha(subset(raw, select=c("mdbf_1", "mdbf_4", "mdbf_8", "mdbf_11")), 
                                                             keys=c("mdbf_4", "mdbf_11"))$total$std.alpha,
                                                psych::alpha(subset(raw, select=c("mdbf_2", "mdbf_5", "mdbf_7", "mdbf_10")), 
                                                             keys=c("mdbf_5", "mdbf_7"))$total$std.alpha),2)


## print table
kable(tab
      , caption="Descriptive statistics of the relevant variables."
      , escape=FALSE
) %>%
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   pack_rows("Personality Traits", 1, 6) %>%
   pack_rows("Personality States", 7, 12) %>%
   pack_rows("Concrete Behaviors", 13, 14) %>%
   pack_rows("Situation Characteristics", 15, 22) %>%
   pack_rows("Outcomes", 23, 24) %>% 
   footnote(general="Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Internal consistencies are reported for all measured that included more than one item.")

```

## Intercorrelations {-}

```{r cor, message=FALSE, warning=FALSE, error=FALSE}

rel <- data %>% 
   ungroup() %>% 
   select(# Traits
      "trait.h", "trait.e", "trait.x", "trait.a", "trait.c", "trait.o",
      # Behaviors
      "state.h", "state.e", "state.x", "state.a", "state.c", "state.o", "nCoop", "nHon",
      # Situation
      "dut", "int", "adv", "mat", "pos", "neg", "dec", "soc",
      # Outcomes
      "mood.gb", "mood.at")

x <- corx::corx(rel,
                triangle = "lower",
                stars = c(0.05))

row.names(x$apa) <- c(# Traits
   "1 Trait Honesty-Humility", "2 Trait Emotionality", "3 Trait Extraversion", "4 Trait Agreeableness", "5 Trait Conscientiousness", "6 Trait Openness",
   # Behaviors
   "7 State Honesty-Humility", "8 State Emotionality", "9 State Extraversion", "10 State Agreeableness", "11 State Conscientiousness", "12 State Openness", "13 Sharing Behaviors", "14 Honest Behaviors",
   # Situation
   "15 Duty", "16 Intellect", "17 Adversity", "18 Mating", "19 pOsitivity", "20 Negativity", "21 Deception", "22 Sociality",
   # Outcomes
   "23 Positive Affect", "24 Tiredness(r)")

x$apa[which(x$apa==' - ', arr.ind=T)] <- "--"


kable(x$apa
      , caption="Intercorrelations of the relevant variables."
      , escape=FALSE
) %>%
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), fixed_thead = T) %>% 
   column_spec(c(1), width = "16em")
```

## Histograms {-}

```{r, fig.asp=1.3, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of the distributions of personality traits, personality states, and situation characteristics in the sample."}

th <- ggplot(data=data, aes(x=trait.h)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1)  +
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait H-H") + xlab("") + theme_pub()

te <- ggplot(data=data, aes(x=trait.e)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait E") + xlab("") + theme_pub()

tx <- ggplot(data=data, aes(x=trait.x)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait X") + xlab("") + theme_pub()

ta <- ggplot(data=data, aes(x=trait.a)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait A") + xlab("") + theme_pub()

tc <- ggplot(data=data, aes(x=trait.c)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait C") + xlab("") + theme_pub()

to <- ggplot(data=data, aes(x=trait.o)) + geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,5.5), breaks=c(1:5), labels=c(1:5)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Trait O") + xlab("") + theme_pub()

sh <- ggplot(data=data, aes(x=state.h)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State H-H") + xlab("") + theme_pub()

se <- ggplot(data=data, aes(x=state.e)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State E") + xlab("") + theme_pub()

sx <- ggplot(data=data, aes(x=state.x)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State X") + xlab("") + theme_pub()

sa <- ggplot(data=data, aes(x=state.a)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State A") + xlab("") + theme_pub()

sc <- ggplot(data=data, aes(x=state.c)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State C") + xlab("") + theme_pub()

so <- ggplot(data=data, aes(x=state.o)) + geom_histogram(color="black", fill=cols.beh[2], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("State O") + xlab("") + theme_pub()

d <- ggplot(data=data, aes(x=dut)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Duty") + xlab("") + theme_pub()

i <- ggplot(data=data, aes(x=int)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Intellect") + xlab("") + theme_pub()

a <- ggplot(data=data, aes(x=adv)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Adversity") + xlab("") + theme_pub()

m <- ggplot(data=data, aes(x=mat)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Mating") + xlab("") + theme_pub()

o <- ggplot(data=data, aes(x=pos)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("pOsitivity") + xlab("") + theme_pub()

n <- ggplot(data=data, aes(x=neg)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Negativity") + xlab("") + theme_pub()

de <- ggplot(data=data, aes(x=dec)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Deception") + xlab("") + theme_pub()

s <- ggplot(data=data, aes(x=soc)) + geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   scale_x_continuous(limits=c(0.5,7.5), breaks=c(1:7), labels=c(1:7)) + scale_y_continuous(expand = c(0, 0.01)) +
   ggtitle("Sociality") + xlab("") + theme_pub()


# (th | te | tx | ta | tc | to) /
#    (sh | se | sx | sa | sc | so) /
#    (d | i | a | m) /
#    (o | n | de | s)


traits <- cowplot::plot_grid(th, te, tx, ta, tc, to, ncol=4)
states <- cowplot::plot_grid(sh, se, sx, sa, sc, so, ncol=4)
sits <- cowplot::plot_grid(d, i, a, m, o, n, de, s, ncol=4)

cowplot::plot_grid(traits, NULL, states, NULL, sits, ncol=1, rel_heights = c(1, 0.05, 1, 0.05, 1))

```

# Manipulation Check {.tabset .tabset-pills -}

## Share Decisions {-}

### Hypothesis {-}

The behavior manipulation is related to participants' actual behavior (i.e., number of share decisions and/or number of honest trials) such that participants in the 'act honestly and agreeably' condition make more share decisions and/or have more honest trials than participants in the 'act dishonestly and disagreeably' condition. 

### Descriptives {-}

```{r h1a-descriptives-1, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$nCoop, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      row.names = FALSE,
      caption="Descriptive statistics of the number of share decisions in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}


```{r h1a-anovas-1, warning=FALSE, message=FALSE, error=FALSE}

aov <- aov_car(nCoop ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and the number of share decisions.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1a-plot, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and the number of share decisions."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=nCoop)) + 
   theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") + 
   ggtitle("Means and distributions") + ylim(c(0,15)) +
   geom_violin(aes(x = condition.sit, y = nCoop), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(m = mean(nCoop, na.rm=TRUE), 
                         sd = sd(nCoop, na.rm=T)), 
            aes(x = condition.sit, y = m, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") + 
   ggtitle("Main effects and interactions") + ylim(c(0,15)) +
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   scale_fill_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## Honest Decisions {-}

### Hypothesis {-}

The behavior manipulation is related to participants' actual behavior (i.e., number of share decisions and/or number of honest trials) such that participants in the 'act honestly and agreeably' condition make more share decisions and/or have more honest trials than participants in the 'act dishonestly and disagreeably' condition. 

### Descriptives {-}

```{r h1a-descriptives-2, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$nHon, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      row.names = FALSE,
      caption="Descriptive statistics of the number of honest decisions in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}

```{r h1a-anovas-2, warning=FALSE, message=FALSE, error=FALSE}

hon.aov <- aov_car(nHon ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and the number of honest decisions.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1a-plot-2, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and the number of honest decisions."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=nHon)) + 
   theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") + 
   ggtitle("Means and distributions") + ylim(c(0,15)) +
   geom_violin(aes(x = condition.sit, y = nHon), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(groups = mean(nHon, na.rm=TRUE)), 
            aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("Number of decisions") + 
   ggtitle("Main effects and interactions") + ylim(c(0,15)) +
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## State Agreeableness {-}

### Hypothesis {-}

The behavior manipulation is also related to participants' self-reported behavior (i.e., state Agreeableness and/or state Honesty-Humility) such that participants in the 'act honestly and agreeably' condition report higher levels of state Agreeableness and/or state Honesty-Humility than participants in the 'act dishonestly and disagreeably' condition.

### Descriptives {-}

```{r h1b-descriptives-1, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$state.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      row.names = FALSE,
      caption="Descriptive statistics of state agreeableness in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}

```{r h1b-anovas-1, warning=FALSE, message=FALSE, error=FALSE}

aov <- aov_car(state.a ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and state agreeableness.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1b-plot-1, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and state agreeableness."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=state.a)) + 
   theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") + 
   ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.a), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(groups = mean(state.a, na.rm=TRUE)), 
            aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State agreeableness score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## State Honesty-Humility {-}

### Hypothesis {-}

The behavior manipulation is also related to participants' self-reported behavior (i.e., state Agreeableness and/or state Honesty-Humility) such that participants in the 'act honestly and agreeably' condition report higher levels of state Agreeableness and/or state Honesty-Humility than participants in the 'act dishonestly and disagreeably' condition.

### Descriptives {-}

```{r h1b-descriptives-2, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$state.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      row.names = FALSE,
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      caption="Descriptive statistics of state honesty-humility in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}

```{r h1b-anovas-2, warning=FALSE, message=FALSE, error=FALSE}

aov <- aov_car(state.h ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and state honesty-humility.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1b-plot-2, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and state honesty-humility."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.sit, y=state.h)) + 
   theme_pub() + xlab("Situation conditions")  + ylab("State honesty-humility score") + 
   ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.h), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(groups = mean(state.h, na.rm=TRUE)), 
            aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State honesty-humility score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## Adversity {-}

### Hypothesis {-}

The situation manipulation is related to participants' self-reported situation perceptions (i.e., Deception and/or Adversity of the situation) such that participants in the 'trustworthy partner' condition report lower levels of perceived Deception and/or Adversity than participants in the 'untrustworthy opponent' condition.

### Descriptives {-}

```{r h1c-descriptives-1, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$adv, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      row.names = FALSE,
      caption="Descriptive statistics of adversity in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}

```{r h1c-anovas-1, warning=FALSE, message=FALSE, error=FALSE}

aov <- aov_car(adv ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and adversity.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(1, width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1c-plot-1, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and adversity."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.beh, y=adv)) + 
   theme_pub() + xlab("Behavior conditions")  + ylab("Adversity score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = adv), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(groups = mean(adv, na.rm=TRUE)), 
            aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Adversity score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## Deception {-}

### Hypothesis {-}

The situation manipulation is related to participants' self-reported situation perceptions (i.e., Deception and/or Adversity of the situation) such that participants in the 'trustworthy partner' condition report lower levels of perceived Deception and/or Adversity than participants in the 'untrustworthy opponent' condition.

### Descriptives {-}

```{r h1c-descriptives-2, warning=FALSE, message=FALSE, error=FALSE}

kable(describeBy(data$dec, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      row.names = FALSE,
      caption="Descriptive statistics of deception in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### ANOVA {-}

```{r h1c-anovas-2, warning=FALSE, message=FALSE, error=FALSE}

aov <- aov_car(dec ~ condition.beh * condition.sit + Error(ResponseId), data=data)
tab <- as.data.frame(nice(aov, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analysis of variance examining associations between experimental conditions and deception.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1c-plot-2, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=0.5, fig.cap="Associations between the experimental conditions and deception."}

# plot means and distributions
l <- ggplot(data, aes(x=condition.beh, y=dec)) + 
   theme_pub() + xlab("Behavior conditions")  + ylab("Deception score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = dec), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r <- ggplot(data %>% 
               group_by(condition.sit, condition.beh) %>% 
               summarise(groups = mean(dec, na.rm=TRUE)), 
            aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Deception score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 


# combine plots
(l | plot_spacer() | r) + plot_annotation(tag_levels = "A") + plot_layout(width=c(1,0.1,1))

```

## Not Targeted Personality States and Situation Characteristics {-}

### Hypothesis {-}

The manipulations are neither related to the reported levels of state openness, state conscientiousness, state extraversion, and state emotional stability nor to the reported levels of Duty, Intellect, Mating, pOsitivity, Negativity, and Sociality.

### Descriptives {-}

```{r h1d-descriptives, warning=FALSE, message=FALSE, error=FALSE}

tab <- bind_rows(describeBy(data$state.e, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$state.x, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$state.c, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$state.o, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$dut, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$int, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$mat, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$pos, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$neg, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$soc, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],)

kable(tab,
      row.names = FALSE,
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      caption="Descriptive statistics of the remaining personality states and situation characteristics in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T)  %>% 
   pack_rows("State Emotionality", 1, 4) %>%
   pack_rows("State Extraversion", 5, 8) %>%
   pack_rows("State Conscientiousness", 9, 12) %>%
   pack_rows("State Openness", 13, 16) %>%
   pack_rows("Duty", 17, 20) %>%
   pack_rows("Intellect", 21, 24) %>%
   pack_rows("Mating", 25, 28) %>%
   pack_rows("pOsitivity", 29, 32) %>%
   pack_rows("Negativity", 33, 36) %>%
   pack_rows("Sociality", 37, 40)

```

### ANOVA {-}

```{r h1d-anovas, warning=FALSE, message=FALSE, error=FALSE}

aov.e <- aov_car(state.e ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.x <- aov_car(state.x ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.c <- aov_car(state.c ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.o <- aov_car(state.o ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.dut <- aov_car(dut ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.int <- aov_car(int ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.mat <- aov_car(mat ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.pos <- aov_car(pos ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.neg <- aov_car(neg ~ condition.beh * condition.sit + Error(ResponseId), data=data)
aov.soc <- aov_car(dec ~ condition.beh * condition.sit + Error(ResponseId), data=data)


tab <- bind_rows(as.data.frame(nice(aov.e, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.x, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.c, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.o, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.dut, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.int, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.mat, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.pos, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.neg, es="pes", sig_symbols = rep("", 4), MSE=FALSE)),
                 as.data.frame(nice(aov.soc, es="pes", sig_symbols = rep("", 4), MSE=FALSE))
)

tab$p.value[tab$p.value=="<.001"] <- "&lt;.001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- rep(c("Behavior conditions", "Situation conditions", "Behavior x Situation conditions"),10)

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i> value"),
      caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics.") %>% 
   pack_rows("DV: State Emotionality", 1, 3) %>%
   pack_rows("DV: State Extraversion", 4, 6) %>%
   pack_rows("DV: State Conscientiousness", 7, 9) %>%
   pack_rows("DV: State Openness", 10, 12) %>%
   pack_rows("DV: Duty", 13, 15) %>%
   pack_rows("DV: Intellect", 16, 18) %>%
   pack_rows("DV: Mating", 19, 21) %>%
   pack_rows("DV: pOsitivity", 22, 24) %>%
   pack_rows("DV: Negativity", 25, 27) %>%
   pack_rows("DV: Sociality", 28, 30) %>%  
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")

```

### Visualization {-}

```{r h1d-plot-1, fig.width=11, fig.asp=2, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Associations between the experimental conditions and the remaining personality states."}

#### Personality States

### State Emotionality

# plot means and distributions
l1 <- ggplot(data, aes(x=condition.sit, y=state.e)) + 
   theme_pub() + xlab("Situation conditions")  + ylab("State emotionality score") + 
   ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.e), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r1 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(state.e, na.rm=TRUE)), 
             aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State emotionality score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 

row1 <- (l1 | plot_spacer() | r1) + plot_layout(width=c(1,0.1,1))



### State Extraversion

# plot means and distributions
l2 <- ggplot(data, aes(x=condition.sit, y=state.x)) + 
   theme_pub() + xlab("Situation conditions")  + ylab("State extraversion score") + 
   ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.x), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r2 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(state.x, na.rm=TRUE)), 
             aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State extraversion score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 

row2 <- (l2 | plot_spacer() | r2) + plot_layout(width=c(1,0.1,1))



### State Conscientiousness

# plot means and distributions
l3 <- ggplot(data, aes(x=condition.sit, y=state.c)) + 
   theme_pub() + xlab("Situation conditions") + ylab("State conscientiousness score") + 
   ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.c), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r3 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(state.c, na.rm=TRUE)), 
             aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State conscientiousness score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 

row3 <- (l3 | plot_spacer() | r3) + plot_layout(width=c(1,0.1,1))



### State Openness

# plot means and distributions
l4 <- ggplot(data, aes(x=condition.sit, y=state.o)) + 
   theme_pub() + xlab("Situation conditions") + ylab("State openness score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.sit, y = state.o), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.beh) +
   facet_wrap(~condition.beh) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r4 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(state.o, na.rm=TRUE)), 
             aes(x = condition.sit, y = groups, color = condition.beh)) +
   theme_pub() + xlab("Situation conditions") + ylab("State openness score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.3,0.2), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.beh) +
   geom_line(aes(group = condition.beh), size=1.5) +
   geom_point(size=2.5) 

row4 <- (l4 | plot_spacer() | r4) + plot_layout(width=c(1,0.1,1))



### titles

e <- ggdraw() + draw_label("State Emotionality", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
x <- ggdraw() + draw_label("State Extraversion", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
c <- ggdraw() + draw_label("State Conscientiousness", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
o <- ggdraw() + draw_label("State Openness", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 




### combine plots
e/
   row1 /
   x/
   row2/
   c/
   row3/
   o/
   row4 + plot_layout(heights=rep(c(0.3,1),4)) #+ plot_annotation(tag_levels = c("A", "", "", 
#                                                                              "B", "", "", 
#                                                                              "C", "", "",
#                                                                              "D", "", "")) 

```

```{r h1d-plot-2, fig.width=11, fig.asp=3, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Associations between the experimental conditions and the remaining situation characteristics."}

#### Situation characteristics

### Duty

# plot means and distributions
l1 <- ggplot(data, aes(x=condition.beh, y=dut)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("Duty score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = dut), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r1 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(dut, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Duty score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row1 <- (l1 | plot_spacer() | r1) + plot_layout(widths=c(1,0.1,1))



### Intellect

# plot means and distributions
l2 <- ggplot(data, aes(x=condition.beh, y=int)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("Intellect score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = int), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r2 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(int, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Intellect score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row2 <- (l2 | plot_spacer() | r2) + plot_layout(widths=c(1,0.1,1))



### Mating

# plot means and distributions
l3 <- ggplot(data, aes(x=condition.beh, y=mat)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("Mating score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = mat), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r3 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(mat, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Mating score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row3 <- (l3 | plot_spacer() | r3) + plot_layout(widths=c(1,0.1,1))



### pOsitivity

# plot means and distributions
l4 <- ggplot(data, aes(x=condition.beh, y=pos)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("pOsitivity score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = pos), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r4 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(pos, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("pOsitivity score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row4 <- (l4 | plot_spacer() | r4) + plot_layout(widths=c(1,0.1,1))



### Negativity

# plot means and distributions
l5 <- ggplot(data, aes(x=condition.beh, y=neg)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("Negativity score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = neg), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r5 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(neg, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Negativity score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row5 <- (l5 | plot_spacer() | r5) + plot_layout(widths=c(1,0.1,1))



### Sociality

# plot means and distributions
l6 <- ggplot(data, aes(x=condition.beh, y=soc)) + 
   theme_pub() + xlab("Behavior conditions") + ylab("Sociality score") + ggtitle("Means and distributions") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   geom_violin(aes(x = condition.beh, y = soc), width=1, fill='#EEEEEE', color="#EEEEEE", trim=FALSE) +
   stat_summary(fun = mean, geom = "crossbar", width = 0.75, 
                position = position_dodge(width = .75), colour="#808080") +
   geom_jitter(aes(colour=condition.beh), shape = 16, width = .1, alpha=.5, size=2.5) + 
   scale_colour_manual(values=cols.sit) +
   facet_wrap(~condition.sit) + 
   theme(axis.text.x=element_text(angle=20, hjust=1, vjust=1)) +
   theme(legend.position="", strip.text.x = element_text(size=9,face="bold")) 


# plot interaction diagram
r6 <- ggplot(data %>% 
                group_by(condition.sit, condition.beh) %>% 
                summarise(groups = mean(soc, na.rm=TRUE)), 
             aes(x = condition.beh, y = groups, color = condition.sit)) +
   theme_pub() + xlab("Behavior conditions") + ylab("Sociality score") + 
   scale_y_continuous(limits=c(1,7), labels=c(1:7), breaks=c(1:7)) +
   ggtitle("Main effects and interactions") + 
   theme(legend.position=c(0.25,0.85), legend.direction="vertical") + labs(color = "") + 
   scale_colour_manual(values=cols.sit) +
   geom_line(aes(group = condition.sit), size=1.5) +
   geom_point(size=2.5) 

row6 <- (l6 | plot_spacer() | r6) + plot_layout(widths=c(1,0.1,1))



### titles

dut <- ggdraw() + draw_label("Duty", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
int <- ggdraw() + draw_label("Intellect", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
mat <- ggdraw() + draw_label("Mating", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
pos <- ggdraw() + draw_label("pOsitivity", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
neg <- ggdraw() + draw_label("Negativity", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 
soc <- ggdraw() + draw_label("Sociality", fontface = c('bold'), size=14, hjust = 0.5, lineheight = 1) 



### combine plots
dut/
   row1 /
   int/
   row2/
   mat/
   row3/
   pos/
   row4/
   neg/
   row5/
   soc/
   row6 + plot_layout(heights=rep(c(0.3,1),6)) #+ plot_annotation(tag_levels = c("A", "", "", 
#                                                                              "B", "", "", 
#                                                                              "C", "", "",
#                                                                              "D", "", "", 
#                                                                              "E", "", "", 
#                                                                              "F", "", "")) 

```


# Replication of Main Effects {.tabset .tabset-pills -}

## Hypothesis and Analystic Strategy {-}

**Hypothesis**  
H2a: Higher levels of reported state Agreeableness and state Honesty-Humility are associated with more positive mood.  

H2b: Higher levels of perceived Deception and Adversity are associated with more negative mood.

**Analytic Strategy**  
We estimated a multiple regression model with the positive affect dimension as the DV and grand-mean centered state Agreeableness, state Honesty-Humility, perceived Adversity, and perceived Deception as IVs. We will add trait Agreeableness and trait Honesty-Humility and their interactions with their corresponding personality states as predictors in a next step to examine between-person differences in the associations between personality states and mood. We included the experimental conditions as covariates in the regressions to control for the hierarchical data structure with participants nested in experimental conditions.

Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant's and the computer's points) and the DV by including these variables as covariates in the models changed the results.

## Regression Models {-}

```{r h2a}

## estimate models
h2 <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c + 
             condition.beh + condition.sit, data=data, 
          contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) + 
               condition.beh + condition.sit, data=data, 
            contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))

## estimate models
h2a <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c + 
             points.c + points.diff + condition.beh + condition.sit, data=data, 
          contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2a.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) + 
               scale(points.c) + scale(points.diff) + condition.beh + condition.sit, data=data, 
            contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
#summary(h2a)

h2a.2 <- lm(mood.gb ~ state.a.c + state.h.c + adv.c + dec.c + 
               points.c + points.diff + condition.beh + condition.sit +
               state.a.c*trait.a.c + state.h.c*trait.h.c, data=data, 
            contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
h2a.2.z <- lm(scale(mood.gb) ~ scale(state.a) + scale(state.h) + scale(adv) + scale(dec) + 
                 scale(points.c) + scale(points.diff) + condition.beh + condition.sit +
                 scale(state.a)*scale(trait.a) + scale(state.h)*scale(trait.h), data=data, 
              contrasts = list(condition.beh = contr.sum, condition.sit = contr.sum))
#summary(h2a.2)



## build table

tab <- data.frame(predictor=rep(NA, nrow(summary(h2a.2)$coefficients)+1), Model1=NA, Model2=NA, Model3=NA)
tab[,1] <- c("(Intercept)", "State Agreeableness", "State Honesty-Humility", "Adversity", "Deception",
             "Points earned", "Difference in points", "Behavior conditions", "Situation conditions",
             "Trait Agreeableness", "Trait Honesty", "Trait A x State A", "Trait HH x State HH", "")
tab[c(1,2,3,4,5,8,9),2] <-  c(paste0(printnum(summary(h2.z)$coefficients[,1]), " ",
                                     stars(summary(h2.z)$coefficients[,4]),
                                     "<br>[",printnum(confint(h2.z)[,1]), ", ",
                                     printnum(confint(h2.z)[,2]), "]"))
tab[1:nrow(summary(h2a)$coefficients),3] <- c(paste0(printnum(summary(h2a.z)$coefficients[,1]), " ",
                                                     stars(summary(h2a.z)$coefficients[,4]),
                                                     "<br>[",printnum(confint(h2a.z)[,1]), ", ",
                                                     printnum(confint(h2a.z)[,2]), "]"))
tab[1:nrow(summary(h2a.2)$coefficients),4] <- c(paste0(printnum(summary(h2a.2.z)$coefficients[,1]), " ",
                                                       stars(summary(h2a.2.z)$coefficients[,4]),
                                                       "<br>[",printnum(confint(h2a.2.z)[,1]), ", ",
                                                       printnum(confint(h2a.2.z)[,2]), "]"))
tab[nrow(summary(h2a.2)$coefficients)+1,2:4] <- c(print.clean(apa_print(h2.z)$estimate$modelfit$r2),
                                                  paste0(print.clean(apa_print(h2a.z)$estimate$modelfit$r2),
                                                         "<br>&Delta; *R^2^* = ",
                                                         printnum(summary(h2a.z)$r.squared - 
                                                                     summary(h2.z)$r.squared), 
                                                         ", *F*(", anova(h2.z, h2a.z)[2,3], ", ", 
                                                         anova(h2.z, h2a.z)[2,1], ") = ", 
                                                         printnum(anova(h2.z, h2a.z)[2,5]), ", *p* ", 
                                                         printp(anova(h2.z, h2a.z)[2,6], 
                                                                add_equals = T)),
                                                  paste0(print.clean(apa_print(h2a.2.z)$estimate$modelfit$r2),
                                                         "<br>&Delta; *R^2^* = ",
                                                         printnum(summary(h2a.2.z)$r.squared - 
                                                                     summary(h2a.z)$r.squared), 
                                                         ", *F*(", anova(h2a.z, h2a.2.z)[2,3], ", ", 
                                                         anova(h2a.z, h2a.2.z)[2,1], ") = ", 
                                                         printnum(anova(h2a.z, h2a.2.z)[2,5]), ", *p* ", 
                                                         printp(anova(h2a.z, h2a.2.z)[2,6], 
                                                                add_equals = T)))

## print table
kable(
   tab
   , align = c("l", "c", "c", "c")
   , col.names = c("Predictor", "Model 1", "Model 2", "Model 3")
   , caption = "Multiple regression models of associations between positive affect and state agreeableness, state honesty-humility, deception, and adversity controlling for the outcome of the game and the experimental groups."
   , escape = FALSE
   , row.names = FALSE
) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T)  %>% 
   column_spec(1, width = "15em") %>%
   pack_rows("Standardized Coefficients", 1, nrow(summary(h2a.2)$coefficients)) %>%
   pack_rows("Model Fit", nrow(summary(h2a.2)$coefficients)+1, nrow(summary(h2a.2)$coefficients)+1) %>%
   footnote(general="Values standardized regression coefficients, values in brackets represent 95% confidence intervals of the regression coefficients")

```

## Visualization {-}

```{r, fig.asp=0.8, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Associations between positive affect and state agreeableness (A), state honesty-humility (B), adversity (C), and deception (D)."}

a <- ggplot(data, aes(x=state.a, y=mood.gb)) + 
   theme_pub() + xlab("State Agreeableness") + ylab("Positive affect") +
   scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
   geom_jitter(size=2.5, alpha=.5, color=cols.beh[1]) + 
   geom_smooth(method="lm", color="black", size=1.2)

b <- ggplot(data, aes(x=state.h, y=mood.gb)) + 
   theme_pub() + xlab("State Honesty-Humility") + ylab("Positive affect") +
   scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
   geom_jitter(size=2.5, alpha=.5, color=cols.beh[2]) + 
   geom_smooth(method="lm", color="black", size=1.2)

c <- ggplot(data, aes(x=adv, y=mood.gb)) + 
   theme_pub() + xlab("Adversity") + ylab("Positive affect") +
   scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
   geom_jitter(size=2.5, alpha=.5, color=cols.sit[1]) + 
   geom_smooth(method="lm", color="black", size=1.2)

d <- ggplot(data, aes(x=dec, y=mood.gb)) + 
   theme_pub() + xlab("Deception") + ylab("Positive affect") +
   scale_x_continuous(limits=c(1,7), breaks=c(1:7), labels=c(1:7)) +
   geom_jitter(size=2.5, alpha=.5, color=cols.sit[2]) + 
   geom_smooth(method="lm", color="black", size=1.2)

r1 <- (a + plot_spacer() + b) + plot_layout(widths=c(1,0.1,1))
r2 <- (c + plot_spacer() + d) + plot_layout(widths=c(1,0.1,1))

r1/
   plot_spacer() /
   r2 + plot_annotation(tag_level="A") + plot_layout(heights=c(1,0.1,1))

```




## Conclusion {-}

Overall, the data mostly supported Hypothesis 2:

* Higher levels of state agreeableness, `r print.clean(apa_print(h2a.2.z)$estimate$scalestate_a, standardized=T)`, and state honesty-humility, `r print.clean(apa_print(h2a.2.z)$estimate$scalestate_h, standardized=T)`, were significantly associated with more positive affect within groups

* Higher levels of perceived adversity, `r print.clean(apa_print(h2a.2.z)$estimate$scaleadv, standardized=T)`, but not deception, `r print.clean(apa_print(h2a.2.z)$estimate$scaledec, standardized=T)`, were significantly associated with less positive affect within groups

* Trait agreeableness and trait adversity were not significantly associated with positive affect and neither were their interactions with the respective personality traits


# Congruence and Positive Affect {.tabset .tabset-pills -}

## Hypothesis & Analytic Strategy {-}

### Hypothesis {-}

H3a: Congruence between personality trait and personality state is associated with positive affect such that trait-congruent personality states are related to more positive mood than trait-incongruent personality states.

H4a: Congruence between personality state and situation characteristic is associated with positive affect such that situation-congruent personality states are related to more positive mood than situation-incongruent personality states.

### Analytic Strategy {-} 

**Step 1: Analysis of Variance**  
We conducted a median split of trait Agreeableness / trait Honesty-Humility and conduct variance analyses with positive affect as DV and the variables representing behavior conditions and median-split personality traits and their interaction as IVs. This replicates analyses performed in Zelenski et al. (2012).

Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant's and the computer's points) and the DV by including these variables as covariates in the models changed the results.

**Step 2: Response Surface Analysis**  
We conducted response surface analyses with midpoint-centered personality state and midpoint-centered personality trait as IVs and positive affect as DV. We included the experimental conditions as covariates in the polynomial regressions to control for the hierarchical data structure with participants nested in experimental conditions. 

Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant's and the computer's points) and the DV by including these variables as covariates in the models changed the results.


## Analysis of Variance {.tabset .tabset-pills -}

### Trait--State Congruence: Agreeableness {-}

#### Model without Covariates {-}

```{r aov-gb-a, warning=F, message=F}

aov.a <- aov_car(mood.gb ~ trait.a.ms * condition.beh + condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.a, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions", 
                "Trait x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-gb-a-cov, warning=F, message=F}

lm.a <- lm(mood.gb ~ trait.a.ms * condition.beh + points.c + points.diff + condition.sit, data=data, 
           contrasts = list(condition.sit=contr.sum, trait.a.ms=contr.sum, condition.beh=contr.sum))


tab <- apa_print(car::Anova(lm.a, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation conditions", "Trait x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

### Trait--State Congruence: Honesty {-}

#### Model without Covariates {-}

```{r aov-gb-h, warning=F, message=F}

aov.h <- aov_car(mood.gb ~ trait.h.ms * condition.beh + condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.h, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions", 
                "Trait x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-gb-h-cov, warning=F, message=F}

lm.h <- lm(mood.gb ~ trait.h.ms * condition.beh + points.c + points.diff + condition.sit, data=data, 
           contrasts = list(condition.sit=contr.sum, trait.h.ms=contr.sum, condition.beh=contr.sum))


tab <- apa_print(car::Anova(lm.h, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation conditions", "Trait x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

### State--Situation Congruence {-}

#### Model without Covariates {-}

```{r aov-gb-sit, warning=F, message=F}

aov.sit <- aov_car(mood.gb ~ condition.beh * condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.sit, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", 
                "Situation x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between the situation conditions and the behavior manipulation and positive affect.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em")  %>% 
   footnote(general="Situation conditions and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-gb-sit-cov, warning=F, message=F}

data$points.c <- scale(data$points, scale=FALSE)

lm.sit <- lm(mood.gb ~ condition.sit * condition.beh + points.c + points.diff, data=data, 
             contrasts = list(condition.sit=contr.sum, condition.beh=contr.sum))



tab <- apa_print(car::Anova(lm.sit, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Situation conditions", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and positive affect with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="Situation conditions and behavior conditions were effect-coded.")

```

## Response Surface Analysis {.tabset .tabset-pills -}

```{r pa-rsa, warning=FALSE, message=FALSE, error=FALSE, results="hide"}

contrasts(data$condition.sit) <- contr.sum(2)
contrasts(data$condition.beh) <- contr.sum(2)

data$trait.a.long <- 6*(data$trait.a-1)/4+1
data$trait.h.long <- 6*(data$trait.h-1)/4+1
data$trait.a.long.mc <- data$trait.a.long-4
data$trait.h.long.mc <- data$trait.h.long-4

h3a.rsa.a <- RSA(mood.gb ~ state.a.mc*trait.a.long.mc, data=data, 
                 model="full", verbose=F,
                 control.variables = c("condition.beh", "condition.sit"))

h3a.rsa.h <- RSA(mood.gb ~ state.h.mc*trait.h.long.mc, data=data, 
                 model="full", verbose=F,
                 control.variables = c("condition.beh", "condition.sit"))

h4a.rsa.a.a <- RSA(mood.gb ~ state.a.mc*adv.mc.r, data=data, 
                   model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.a.d <- RSA(mood.gb ~ state.a.mc*dec.mc.r, data=data, 
                   model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.h.a <- RSA(mood.gb ~ state.h.mc*adv.mc.r, data=data, 
                   model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4a.rsa.h.d <- RSA(mood.gb ~ state.h.mc*dec.mc.r, data=data, 
                   model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))

```

### Trait--State Congruence: Agreeableness {-}

```{r pa-rsa-tables-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h3a.rsa.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Trait A", "State A<sup>2</sup>", "State A x Trait A", "Trait A<sup>2</sup>", "Behavior conditions", "Situation conditions")


## print table
kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align = c("l", "r", "r", "r", "r", "r")
      , col.names = c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
      , caption = "Parameters of the response surface analysis of trait agreeableness and state agreeableness predicting positive affect."
      , escape = FALSE
) %>% 
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "15em") 

```

### Trait--State Congruence: Honesty {-}

```{r pa-rsa-tables-h, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h3a.rsa.h, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Trait HH", "State HH<sup>2</sup>", "State HH x Trait HH", "Trait HH<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of trait honesty-humility and state honesty-humility predicting positive affect."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Agreeableness and Adversity {-}

```{r pa-rsa-tables-a-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4a.rsa.a.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Adv(r)", "State A<sup>2</sup>", "State A x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state agreeableness and adversity predicting positive affect."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Agreeableness and Deception {-}

```{r pa-rsa-tables-a-d, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4a.rsa.a.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Dec(r)", "State A<sup>2</sup>", "State A x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state agreeableness and deception predicting positive affect."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Honesty and Adversity {-}

```{r pa-rsa-tables-h-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4a.rsa.h.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Adv(r)", "State HH<sup>2</sup>", "State HH x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state honesty-humility and adversity predicting positive affect."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Honesty and Deception {-}

```{r pa-rsa-tables-h-d, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4a.rsa.h.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Dec(r)", "State HH<sup>2</sup>", "State HH x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state honesty-humility and deception predicting positive affect."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

## Visualization {.tabset .tabset-pills -}

### ANOVA Interaction Plots {-}

```{r pa-aov-plot, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=1.2, out.width='90%', fig.cap="Interaction plots of interactions between median-split personality traits, behavior conditions, and situation conditions predicting positive affect."}

p1 <- interactions::cat_plot(lm.a, pred=condition.beh, modx=trait.a.ms, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Positive Affect",
                             main.title = "Trait Agreeableness x Behavior Conditions",
                             legend.main = "Trait Agreeableness (median-split)",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p2 <- interactions::cat_plot(lm.h, pred=condition.beh, modx=trait.h.ms, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Positive Affect",
                             main.title = "Trait Honesty x Behavior Conditions",
                             legend.main = "Trait Honesty (median-split)",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p3 <- interactions::cat_plot(lm.sit, pred=condition.beh, modx=condition.sit, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Positive Affect",
                             main.title = "Situation x Behavior Conditions",
                             legend.main = "Situation Conditions",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p1 / plot_spacer() / p2 / plot_spacer() / p3 + plot_layout(heights=c(1,0.1, 1, 0.1,1)) + plot_annotation(tag_levels = "A")

```


### Response Surface Plots {-}

```{r pa-rsa-plot, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=1.5, fig.cap="Response surface plots of the association between trait--state congruence, state--situation congruence, and positive affect. Adversity and deception were reverse-coded (indicated by *(r)*) such that higher values indicate less adversity and decpetion, respectively. The response surface parameters are listed below the titles. The blue lines represent the line of congruence (LOC; i.e., X = Y) and the line of incongruence (LOIC; i.e., X = -Y). The black lines represent the range of observed values as a twodimensional boxplot."}

a <- plot(h3a.rsa.a, xlab="State Agreeableness", ylab="Trait Agreeableness", zlab="Positive affect",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Agreeableness",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F, pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

b <- plot(h3a.rsa.h, xlab="State Honesty", ylab="Trait Honesty", zlab="Positive affect", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait--State Congruence: Honesty-humility",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

c <- plot(h4a.rsa.a.a, xlab="State Agreeableness", ylab="Adversity(r)", zlab="Positive affect",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Adversity",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

d <- plot(h4a.rsa.h.a, xlab="State Honesty", ylab="Adversity(r)", zlab="Positive affect", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Adversity",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

e <- plot(h4a.rsa.a.d, xlab="State Agreeableness", ylab="Deception(r)", zlab="Positive affect",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Deception",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

f <- plot(h4a.rsa.h.d, xlab="State Honesty", ylab="Deception(r)", zlab="Positive affect", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Deception",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,pad=2,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

## combine plots
cowplot::plot_grid(a,b,c,d,e,f, ncol = 2, labels="AUTO", label_size = 12)

```

## Conclusion {-}

Overall, the data did not support the congruence hypotheses with regard to positive affect:

* Analyses of variance did not yield any significant interaction effects (all *F*s < `r printnum(max(summary(aov.a)[4,4], summary(aov.h)[4,4], summary(aov.sit)[3,4]))`, all *p*s > `r printp(min(summary(aov.a)[4,6], summary(aov.h)[4,6], summary(aov.sit)[3,6]))`)

* Only the behavior conditions were significantly associated with positive affect such that participants in the high agreeableness and honesty-condition (*M* = `r describeBy(data$mood.gb, group=data$condition.beh, digits=2, mat=T)$mean[2]`, *SD* = `r describeBy(data$mood.gb, group=data$condition.beh, digits=2, mat=T)$sd[2]`) reported significantly more positive affect than participants in the low agreeableness and honesty-condition (*M* = `r describeBy(data$mood.gb, group=data$condition.beh, digits=2, mat=T)$mean[1]`, *SD* = `r describeBy(data$mood.gb, group=data$condition.beh, digits=2, mat=T)$sd[1]`, *d* = `r paste0(printnum(cohen.d(data$mood.gb, data$condition.beh)$cohen.d[2]), ", 95% CI [",printnum(cohen.d(data$mood.gb, data$condition.beh)$cohen.d[1]),", ", printnum(cohen.d(data$mood.gb, data$condition.beh)$cohen.d[3]) ,"]")`)

* Polynomial regressions also showed that only higher levels of state agreeableness and state honesty-humility (but not interactions of fit patterns) were significantly associated with more positive affect across all models (*b*s ranging from `r printnum(min(c(h3a.rsa.a$LM$coefficients[2,1], h3a.rsa.h$LM$coefficients[2,1], h4a.rsa.a.a$LM$coefficients[2,1], h4a.rsa.a.d$LM$coefficients[2,1], h4a.rsa.h.a$LM$coefficients[2,1], h4a.rsa.h.d$LM$coefficients[2,1])))`  to `r printnum(max(c(h3a.rsa.a$LM$coefficients[2,1], h3a.rsa.h$LM$coefficients[2,1], h4a.rsa.a.a$LM$coefficients[2,1], h4a.rsa.a.d$LM$coefficients[2,1], h4a.rsa.h.a$LM$coefficients[2,1], h4a.rsa.h.d$LM$coefficients[2,1])))`)

* In summary, across both analysis of variance and response surface analysis, neither interactions nor fit patterns were significantly associated with positive affect

# Congruence and Tiredness {.tabset .tabset-pills -}

## Hypothesis & Analytic Strategy {-}

### Hypothesis {-}

H3a: Congruence between personality trait and personality state is associated with tiredness such that trait-congruent personality states are related to less tiredness than trait-incongruent personality states.

H4a: Congruence between personality state and situation characteristic is associated with tiredness such that situation-congruent personality states are related to less tiredness than situation-incongruent personality states.

### Analytic Strategy {-} 

**Step 1: Analysis of Variance**  
We conducted a median split of trait Agreeableness / trait Honesty-Humility and conduct variance analyses with reverse-coded tiredness as DV and the variables representing behavior conditions and median-split personality traits and their interaction as IVs. This replicates analyses performed in Zelenski et al. (2012).

Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant's and the computer's points) and the DV by including these variables as covariates in the models changed the results.

**Step 2: Response Surface Analysis**  
We conducted response surface analyses with midpoint-centered personality state and midpoint-centered personality trait as IVs and reverse-coded tiredness as DV. We included the experimental conditions as covariates in the polynomial regressions to control for the hierarchical data structure with participants nested in experimental conditions. 

Additionally, we examined whether controlling for possible associations between the results of the game (points earned by the participant and difference between the participant's and the computer's points) and the DV by including these variables as covariates in the models changed the results.

## Analysis of Variance {.tabset .tabset-pills -}

### Trait--State Congruence: Agreeableness {-}

#### Model without Covariates {-}

```{r aov-at-a, warning=F, message=F}

aov.a <- aov_car(mood.at ~ trait.a.ms * condition.beh + condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.a, es="pes", sig_symbols = rep("", 4), MSE=FALSE))


tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions", 
                "Trait x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-at-a-cov, warning=F, message=F}

data$points.c <- scale(data$points, scale=FALSE)

lm.a <- lm(mood.at ~ trait.a.ms * condition.beh + points.c + points.diff + condition.sit, data=data, 
           contrasts = list(condition.sit=contr.sum, trait.a.ms=contr.sum, condition.beh=contr.sum))


tab <- apa_print(car::Anova(lm.a, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation conditions", "Trait x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

### Trait--State Congruence: Honesty {-}

#### Model without Covariates {-}

```{r aov-h-at, warning=F, message=F}

aov.h <- aov_car(mood.at ~ trait.h.ms * condition.beh + condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.h, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Trait (median-split)", "Behavior conditions", "Situation conditions", 
                "Trait x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-at-h-cov, warning=F, message=F}

data$points.c <- scale(data$points, scale=FALSE)

lm.h <- lm(mood.at ~ trait.h.ms * condition.beh + points.c + points.diff + condition.sit, data=data, 
           contrasts = list(condition.sit=contr.sum, trait.h.ms=contr.sum, condition.beh=contr.sum))


tab <- apa_print(car::Anova(lm.h, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Trait (median-split)", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation conditions", "Trait x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between experimental conditions and the remaining personality states and situation characteristics with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="We included the situation conditions as covariates in this model to control for dependencies among participants who experienced the same situation condition. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions, median-split personality traits, and behavior conditions were effect-coded.")

```

### State--Situation Congruence {-}

#### Model without Covariates {-}

```{r aov-at-sit, warning=F, message=F}

aov.sit <- aov_car(mood.at ~ condition.beh * condition.sit + Error(ResponseId), data=data)


tab <- as.data.frame(nice(aov.sit, es="pes", sig_symbols = rep("", 4), MSE=FALSE))

tab$p.value[tab$p.value=="<.001"] <- "&lt; .001"
tab$pes[tab$pes=="<.001"] <- "&lt;.001"
tab$Effect <- c("Behavior conditions", "Situation conditions", 
                "Situation x Behavior conditions")

kable(tab,
      escape=F,
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between the situation conditions and the behavior manipulation and tiredness.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "15em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded.")

```

#### Model with Covariates {-}

```{r aov-at-sit-cov, warning=F, message=F}

lm.sit <- lm(mood.at ~ condition.sit * condition.beh + points.c + points.diff, data=data, 
             contrasts = list(condition.sit=contr.sum, condition.beh=contr.sum))


tab <- apa_print(car::Anova(lm.sit, type=3))$table
tab$dfs <- paste0(tab$df, ", ", tab$df.residual)
tab$term <- c("Situation conditions", "Behavior conditions", "Points earned", 
              "Difference in points", "Situation x Behavior conditions")


kable(tab[,c(1,8,3,2,7)],
      escape=F,
      row.names = F,
      align=c("l", "r", "r", "r", "r"),
      col.names=c("Effect", "df", "F", "&#951;<sub>p</sub><sup>2</sup>", "<i>p</i>"),
      caption="Analyses of variance examining associations between median-split personality traits, the behavior manipulation and tiredness with additional covariates controlling for the results of the game.") %>% 
   kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                 full_width = F, position = "left", fixed_thead = T) %>% 
   column_spec(c(1), width = "20em") %>% 
   column_spec(c(2:4), width = "8em") %>% 
   footnote(general="Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Situation conditions and behavior conditions were effect-coded.")

```


## Response Surface Analysis {.tabset .tabset-pills -}

```{r at-rsa, results="hide", warning=FALSE, message=FALSE, error=FALSE}

contrasts(data$condition.sit) <- contr.sum(2)
contrasts(data$condition.beh) <- contr.sum(2)

h3b.rsa.a <- RSA(mood.at ~ state.a.mc*trait.a.long.mc, data=data, model="full", verbose=F,
                 control.variables = c("condition.beh", "condition.sit"))
h3b.rsa.h <- RSA(mood.at ~ state.h.mc*trait.h.long.mc, data=data, model="full", verbose=F,
                 control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.a.a <- RSA(mood.at ~ state.a.mc*adv.mc.r, data=data, model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.a.d <- RSA(mood.at ~ state.a.mc*dec.mc.r, data=data, model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.h.a <- RSA(mood.at ~ state.h.mc*adv.mc.r, data=data, model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))
h4b.rsa.h.d <- RSA(mood.at ~ state.h.mc*dec.mc.r, data=data, model="full", verbose=F,
                   control.variables = c("condition.beh", "condition.sit"))

```

### Trait--State Congruence: Agreeableness {-}

```{r at-rsa-tables-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h3b.rsa.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Trait A", "State A<sup>2</sup>", "State A x Trait A", "Trait A<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of trait agreeableness and state agreeableness predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Trait A represents trait agreeableness. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### Trait--State Congruence: Honesty {-}

```{r at-rsa-tables-h, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h3b.rsa.h, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Trait HH", "State HH<sup>2</sup>", "State HH x Trait HH", "Trait HH<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of trait honesty-humility and state honesty-humility predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Trait HH represents trait honesty-humility. Personality traits were transformed from a 5-point scale to a 7-point scale. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Agreeableness and Adversity {-}

```{r at-rsa-tables-a-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4b.rsa.a.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Adv(r)", "State A<sup>2</sup>", "State A x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state agreeableness and adversity predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Agreeableness and Deception {-}

```{r at-rsa-tables-a-d, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4b.rsa.a.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State A", "Dec(r)", "State A<sup>2</sup>", "State A x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state agreeableness and deception predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State A represents state agreeableness, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Honesty and Adversity {-}

```{r at-rsa-tables-h-a, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4b.rsa.h.a, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Adv(r)", "State HH<sup>2</sup>", "State HH x Adv(r)", "Adv(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state honesty-humility and adversity predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Adv(r) represents reverse-coded Adversity. Adversity was reverse-coded such that higher levels indicate less adversity. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

### State--Situation Congruence: Honesty and Deception {-}

```{r at-rsa-tables-h-d, warning=FALSE, message=FALSE, error=FALSE}

tab <- getPar(h4b.rsa.h.d, standardized=TRUE)[c(8, 1:7, 9:13),]
tab$ci <- paste0("[",printnum(tab$ci.lower), ", ", printnum(tab$ci.upper), "]")
tab$pvalue <- printp(tab$pvalue)
row.names(tab)[1:8] <- c("(Intercept)", "State HH", "Dec(r)", "State HH<sup>2</sup>", "State HH x Dec(r)", "Dec(r)<sup>2</sup>", "Behavior conditions", "Situation conditions")


kable(tab[,c(1,2,11,9,5)]
      , digits=c(2,2,2,2,2)
      , align=c("l", "r", "r", "r", "r", "r")
      , caption = "Parameters of the response surface analysis of state honesty-humility and deception predicting tiredness."
      , escape = FALSE
      , col.names=c("Label", "<i>b</i>", "95% Confidence Interval", "&beta;", "<i>p</i> value")
)  %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T, full_width = F, position = "left")  %>% 
   column_spec(1, width = "18em") %>%
   pack_rows("Regression Parameters", 1, 8) %>%
   pack_rows("Response Surface Parameters", 9, 13) %>% 
   footnote(general="b represents unstandardized regression coefficients, &beta; represents standardized regression coeficients. State HH represents state honesty-humility, Dec(r) represents reverse-coded Deception. Deception was reverse-coded such that higher levels indicate less deception. Tiredness was reverse-coded such that higher values indicate less tiredness or a more active mood. Control variables (i.e., points earned in the game, difference in points, and experimental groups) are not listed in the output.", escape=F)

```

## Visualization {.tabset .tabset-pills -}

### ANOVA Interaction Plots {-}

```{r at-aov-plot, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=1.2, out.width='90%', fig.cap="Interaction plots of interactions between median-split personality traits, behavior conditions, and situation conditions predicting reverse-coded tiredness (i.e., higher levels indicate less tiredness)."}

p1 <- interactions::cat_plot(lm.a, pred=condition.beh, modx=trait.a.ms, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Tiredness (r)",
                             main.title = "Trait Agreeableness x Behavior Conditions",
                             legend.main = "Trait Agreeableness (median-split)",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p2 <- interactions::cat_plot(lm.h, pred=condition.beh, modx=trait.h.ms, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Tiredness (r)",
                             main.title = "Trait Honesty x Behavior Conditions",
                             legend.main = "Trait Honesty (median-split)",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p3 <- interactions::cat_plot(lm.sit, pred=condition.beh, modx=condition.sit, 
                             plot.points = T, point.size=1, point.alpha=.3, jtter=.2,
                             geom="line", interval=T, interval.geom = "linerange",
                             pred.point.size = 5, dodge.width = 0.3,
                             x.label="Behavior Conditions", y.label="Tiredness (r)",
                             main.title = "Situation x Behavior Conditions",
                             legend.main = "Situation Conditions",
                             colors=cols.beh) + 
   theme_pub() + theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) +
   scale_x_discrete(expand=c(0.2, 0.2))


p1 / plot_spacer() / p2 / plot_spacer() / p3 + plot_layout(heights=c(1,0.1, 1, 0.1,1)) + plot_annotation(tag_levels = "A")

```

### Response Surface Plots {-}

```{r at-rsa-plot, warning=FALSE, message=FALSE, error=FALSE, fig.width=11, fig.asp=1.5, fig.cap="Response surface plots of the association between trait--state congruence, state--situation congruence, and tiredness. Adversity, deception, and tiredness were reverse-coded (indicated by *(r)*) such that higher values indicate less adversity, decpetion, and tiredness, respectively. The response surface parameters are listed below the titles. The blue lines represent the line of congruence (LOC; i.e., X = Y) and the line of incongruence (LOIC; i.e., X = -Y). The black lines represent the range of observed values as a twodimensional boxplot."}

## print RSA plots
a <- plot(h3b.rsa.a, xlab="State Agreeableness", ylab="Trait Agreeableness", zlab="Tiredness(r)",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Agreeableness",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

b <- plot(h3b.rsa.h, xlab="State Honesty", ylab="Trait Honesty", zlab="Tiredness(r)", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="Trait-State Congruence: Honesty-humility",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

c <- plot(h4b.rsa.a.a, xlab="State Agreeableness", ylab="Adversity(r)", zlab="Tiredness(r)",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Adversity",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

d <- plot(h4b.rsa.h.a, xlab="State Honesty", ylab="Adversity(r)", zlab="Tiredness(r)", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Adversity",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

e <- plot(h4b.rsa.a.d, xlab="State Agreeableness", ylab="Deception(r)", zlab="Tiredness(r)",
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nAgreeableness and Deception",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))

f <- plot(h4b.rsa.h.d, xlab="State Honesty", ylab="Deception(r)", zlab="Tiredness(r)", 
           legend=FALSE, distance = c(1.3, 1.3, 1.4), main="State-Situation Congruence:\nHonesty and Deception",
           project = c("contour"), axes = c("LOC", "LOIC"), hull=F,
           param=T, gridsize=7, points=list(show=FALSE), zlim=c(1, 5.5))


## combine plots
cowplot::plot_grid(a,b,c,d,e,f, ncol = 2, labels="AUTO", label_size = 12)

```

## Conclusion {-}

Overall, the data did not support the congruence hypotheses with regard to tiredness:

* Analyses of variance did not yield any significant interaction effects (all *F*s < `r printnum(max(summary(aov.a)[4,4], summary(aov.h)[4,4], summary(aov.sit)[3,4]))`, all *p*s > `r printp(min(summary(aov.a)[4,6], summary(aov.h)[4,6], summary(aov.sit)[3,6]))`)

* Only the behavior conditions were significantly associated with reverse-coded tiredness such that participants in the high agreeableness and honesty condition (*M* = `r describeBy(data$mood.at, group=data$condition.beh, digits=2, mat=T)$mean[2]`, *SD* = `r describeBy(data$mood.at, group=data$condition.beh, digits=2, mat=T)$sd[2]`) reported significantly more active mood---that is, less tiredness---than participants in the low agreeableness and honesty condition (*M* = `r describeBy(data$mood.at, group=data$condition.beh, digits=2, mat=T)$mean[1]`, *SD* = `r describeBy(data$mood.at, group=data$condition.beh, digits=2, mat=T)$sd[1]`, *d* =`r paste0(printnum(cohen.d(data$mood.at, data$condition.beh)$cohen.d[2]), ", 95% CI [",printnum(cohen.d(data$mood.at, data$condition.beh)$cohen.d[1]),", ", printnum(cohen.d(data$mood.at, data$condition.beh)$cohen.d[3]) ,"]")`)

* Polynomial regressions also showed that only higher levels of state agreeableness and state honesty-humility (but not interactions of fit patterns) were significantly associated with more active mood across all models (*b*s ranging from `r printnum(min(c(h3b.rsa.a$LM$coefficients[2,1], h3b.rsa.h$LM$coefficients[2,1], h4b.rsa.a.a$LM$coefficients[2,1], h4b.rsa.a.d$LM$coefficients[2,1], h4b.rsa.h.a$LM$coefficients[2,1], h4b.rsa.h.d$LM$coefficients[2,1])))`  to `r printnum(max(c(h3b.rsa.a$LM$coefficients[2,1], h3b.rsa.h$LM$coefficients[2,1], h4b.rsa.a.a$LM$coefficients[2,1], h4b.rsa.a.d$LM$coefficients[2,1], h4b.rsa.h.a$LM$coefficients[2,1], h4b.rsa.h.d$LM$coefficients[2,1])))`)

* In summary, across both analysis of variance and response surface analysis, neither interactions nor fit patterns were significantly associated with positive affect

# Congruence and Stroop Performance {.tabset .tabset-pills -}

## Hypothesis {-}

H3c: Congruence between personality trait and personality state is associated with with Stroop performance such that trait-congruent personality states are related to faster reaction times or less errors than trait-incongruent personality states.

H4c: Congruence between personality state and situation characteristic is associated with Stroop performance such that situation-congruent personality states are related are related to faster reaction times or less errors than situation-incongruent personality states.

## Analytic Strategy {-}

Statistical data analysis inevitably offers so-called researcher degrees of freedom. Many decisions must be made for which there are several equally valid options. Therefore, any decision is often both defensible and arbitrary (Simonsohn, Simmons, & Nelson, 2019). Handling of reaction times (RTs) in psychological experiments seems to offer a particularly great amount of researcher degrees of freedom. Therefore, we decided to conduct a mini specification-curve analysis (Simonsohn, Simmons, & Nelson, 2019) for all hypotheses that involve RTs. We call it mini specification-curve analysis because it does not involve all analyses or all steps of data handling, but merely focuses on identifying and handling outliers and calculating summary statistics in RT data. Additionally, we want to emphasize that we most certainly did not include all conceivable options for these decisions, but surely selected a fair and representative amount.

### Step 1: Set of Reasonable Specifications {.tabset .tabset-pills -}

Here, we list the specifications we will include for four different decisions: (1) How to identify outliers in RTs, (2) how to treat these outliers, (3) which trials to use for calculation, and (4) which summary statistic to calculate.
Numbers in brackets represent the number of specifications in the branch.

#### Reaction Times {.tabset .tabset-pills -}

##### Total Number of Specifications {-}

**Total Number of Specifications**  
46 methods for the identification of outliers x 3 methods for the treatment of outliers x 8 options for selecting trials to be included x 2 methods for the summary statistic x 4 options for covariates + no identification/treatment of outliers x 8 options for selecting trials to be included x 2 methods for the summary statistic x 4 options for covariates = 8832 + 64 = **8896 analyses**


##### Identification of Outliers {-}

###### Fixed Cutoffs (4) {-}
*All RTs below or above a certain, prespecified cutoff are identified as outliers*

* < 100ms, > 1500ms
* < 350ms, > 2000ms
* < 300ms, > 4000ms
* < 200ms, > 2000ms


###### Relative cutoffs: Global (14) {-}
*Applied to all RTs from all participants and all conditions at the same time*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
* Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR

**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values


###### Relative cutoffs: Per cell (14) {-}
*Applied separately to the RTs from each experimental cell, i.e., condition (but across participants)*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
* Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR

**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values

###### Relative cutoffs: Per participant (14) {-}
*Applied separately to RTs from each participant (but across cells)*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
* Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR


**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values

##### Treatment of Outliers {-}

* Trimming (i.e. outliers are removed)
* Winsorizing (i.e., outliers are replaced by the cutoff)
* Interpolating (i.e., outliers are replaced by the mean/median)
* None (i.e., outliers are kept in the data)

##### Selection of the Trials to be Considered {-}

**Use all trials (4)**

* Overall (i.e., across all conditions)
* Congruent trials only
* Incongruent trials only
* Neutral trials only

**Use only correctly answered trials (4)**

* Overall (i.e., across all conditions)
* Congruent trials only
* Incongruent trials only
* Neutral trials only

##### Summary Statistic {-}

* Mean
* Median

##### Inclusion of Covariates {-}

* Experimental conditions only
* Experimental conditions & points won in the game
* Experimental conditions & point difference in the game
* Experimental conditions, points won in the game, & point difference in the game



#### Error Rates {.tabset .tabset-pills -}

##### Total Number of Specifications {-}

**Total Number of Specifications**  
46 methods for the identification of outliers x 3 methods for the treatment of outliers x 4 options for selecting trials to be included x 1 methods for the calculation of summary statistics x 4 options for covariates + no identification/treatment of outliers x 4 options for selecting trials to be included x 1 methods for the calculation of summary statistics x 4 options for covariates = **2224 analyses**

##### Identification of Outliers {-}

###### Fixed cutoffs (4) {-}
*All RTs below or above a certain, pre-specified cutoff are identified as outliers*

* < 100ms, > 1500ms
* < 350ms, > 2000ms
* < 300ms, > 4000ms
* < 200ms, > 2000ms

###### Relative cutoffs: Global (14) {-}
*Applied to all RTs from all participants and all conditions at the same time*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
* Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR

**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values


###### Relative cutoffs: Per cell (14) {-}
*Applied separately to the RTs from each experimental cell, i.e., condition (but across participants)*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
* Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR

**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values

###### Relative cutoffs: Per participant (14) {-}
*Applied separately to RTs from each participant*

**Mean RT +- SDs (5)**

* Mean RT ± 2 SDs
* Mean RT ± 2.5 SDs
* Mean RT ± 3 SDs
* Mean RT ± 3.5 SDs
*Mean RT ± 4 SDs

**Mean RT +- IQR (2)**

* Mean RT ± 3 IQR
* Tukey (1977) fences: Q1-1.5 IQR or above Q3+1.5 IQR


**Median +- MADs (3)**

* median plus or minus 2 times the MAD
* median plus or minus 2.5 times the MAD
* median plus or minus 3 times the MAD

**Percentages (4)**

* 2% most extreme values
* 5% most extreme values
* 10% most extreme values
* 15% most extreme values


##### Treatment of Outliers {-}

* Trimming (i.e. outliers are removed)
* None (i.e., outliers are kept in the data)
* Error (i.e., outliers are considered errors)
* Correct (i.e., outliers are considered correct)

##### Selection of the Trials to be Considered {-}

* Overall (i.e., across all conditions)
* Congruent trials only
* Incongruent trials only
* Neutral trials only

##### Inclusion of Covariates {-}

* Experimental conditions
* Experimental conditions & points won in the game
* Experimental conditions & point difference in the game
* Experimental conditions, points won in the game, & point difference in the game


### Step 2: Descriptive Specification Curves {-}

We conducted all analyses that include Stroop performance (RTs or error rates) separately for each specification. The results from the different specifications were then be graphically displayed in descriptive specification curves. These curves displayed the effects of interest (i.e., interaction coefficients or presence of fit patterns) from all specifications ranked by size of the effect. Additionally, these plots indicate which analytic decisions lead to which effects. Descriptive specification curves were used to examine the range of the effects across all specifications, the proportion of significant effects, and how the analytic decisions impacted the estimated effects.

### Step 3: Permutation Test {-}

Finally, we applied a permutation technique to test how inconsistent the results are with the null hypothesis of no effect across the specification curve. We created 500 data sets by shuffling the dependent variable (RT or error rate). In these datasets, because of the random distribution of RTs/error rates, the null hypothesis is per definition true. 

We then needed to derive a test statistic from the specification curves under the null hypothesis (obtained with the 500 shuffled datasets). Because the congruence hypotheses are compound hypotheses for which several conditions must be fulfilled (see Humberg, Nestler, & Back, 2019), we used the percentage of specifications supporting the alternative hypothesis (i.e., the congruence effect) as the test statistic. 

For each shuffled dataset, we calculated the percentage of specifications supporting the congruence hypothesis and compared the distribution of this percentage across the 500 samples to the observed percentage in the real data.

This comparison allowed us to determine whether we could reject the null hypothesis of no congruence effect across the specification curve. Specifically, the relative frequency of shuffled samples with at least as many specifications supporting the congruence effect as the unshuffled data represented the *p* value of the permutation test. This *p* value reflects the probability of observing this many or even more significant specifications under the assumption of no congruence effect.

Additionally, we graphically compared the observed and the expected-under-the-null specification curves.

## Stroop Effect {.tabset .tabset-pills -}

```{r stroop-prep, warning=F, message=F, error=F}

sub <- data.clean %>% 
   ungroup() %>% 
   select("ResponseId", contains("Stroop"), "condition.bs") %>% 
   rename(id=ResponseId) %>% 
   filter(numStroopTrial=="passed")

stroop.long <- data.frame(id=NA, answer=NA, correct=NA, stimulus=NA, condition=NA, rt=NA, exp.cell=NA)

for(i in 1:nrow(sub)) {
   if(length(unlist(str_split(sub$numStroopAnswer[i],",")))==75) {
      answer=unlist(str_split(sub$numStroopAnswer[i],","))
   } else {
      answer=rep("-999",75)
   }
   new <- data.frame(id=rep(as.character(sub[i,1]),75), answer=answer, correct=unlist(str_split(sub$numStroopCorrectAnswer[i],",")),
                     stimulus=unlist(str_split(sub$numStroopStimulus[i],",")), condition=unlist(str_split(sub$numStroopCondition[i],",")),
                     rt=unlist(str_split(sub$numStroopReactionTime[i],",")), exp.cell=sub$condition.bs[i])
   stroop.long <- rbind(stroop.long, new)
}

stroop.long <- stroop.long[2:nrow(stroop.long),]
stroop.long$rt <- as.numeric(stroop.long$rt)
stroop.long$rt[which(stroop.long$rt==-999)] <- NA
stroop.long$rt[which(is.na(stroop.long$answer))] <- NA
stroop.long$answer[which(is.na(stroop.long$rt))] <- NA
stroop.long$condition <- factor(stroop.long$condition, levels=c(0, -1, 1), labels=c("neutral", "congruent", "incongruent"))
stroop.long$correct <- factor(stroop.long$correct, levels=c(0,1), labels=c("right", "left"))
stroop.long$answer[stroop.long$answer!="ArrowLeft" & stroop.long$answer!="ArrowRight"] <- NA
stroop.long$answer <- factor(stroop.long$answer, levels=c("ArrowLeft", "ArrowRight"), labels=c("left", "right"))
stroop.long$error <- ifelse(stroop.long$answer==stroop.long$correct, FALSE, TRUE) # TRUE means that there is an error as in error? true


stroop.short <- stroop.long %>% 
   filter(!is.na(condition)) %>% 
   filter(!is.na(rt)) %>% 
   filter(!is.na(error)) %>% 
   group_by(id, condition) %>% 
   summarize(error.rate = sum(error)*1/75,
             error.perc = sum(error)*100/75,
             mean.rt = mean(rt)) %>% 
   ungroup() %>% 
   left_join(., subset(data, select=c("ResponseId", "condition.beh", "condition.sit", "condition.bs")), by=c("id" = "ResponseId"))

```

### Exemplary Specification {.tabset .tabset-pills -}

#### Descriptives {-}

Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect. 

```{r}

tab <- bind_rows(describeBy(stroop.short$mean.rt, group=stroop.short$condition, mat = T, digits=2)[,c(2, 5:15, 4)],
                 describeBy(stroop.short$error.rate, group=stroop.short$condition, mat = T, digits=2)[,c(2, 5:15, 4)])

kable(tab,
      row.names = FALSE,
      col.names = c("Stroop trial type", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis", "se", "n"),
      caption="Descriptive statistics of average reaction times and error rates in the different Stroop trial types aggregated within persons.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   pack_rows("DV: Reaction Time", 1, 3) %>% 
   pack_rows("DV: Error Rate", 4, 6)

```

#### Regression Models {-}

Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect. 

```{r}
stroop.short$condition <- relevel(stroop.short$condition, ref="congruent")

# model 
m.rt <- lmer(mean.rt ~ condition + (1|id), data=stroop.short)
m.er <- lmer(error.perc ~ condition + (1|id), data=stroop.short)

tab <- bind_rows(as.data.frame(summary(m.rt)$coefficients),as.data.frame(summary(m.er)$coefficients))
tab$df <- floor(tab$df)
tab$`Pr(>|t|)` <- printp(tab$`Pr(>|t|)`)
row.names(tab) <- c("Intercept (i.e., mean of congruent trials)", 
                    "Difference (in ms) between congruent and neutral trials",
                    "Difference (in ms) between congruent and incongruent trials", 
                    "Intercept (i.e., error rate of congruent trials)", 
                    "Difference (in %) between congruent and neutral trials",
                    "Difference (in %) between congruent and incongruent trials")

kable(tab,
      row.names = TRUE,
      escape = FALSE,
      digits = 3,
      align = "r",
      col.names = c("Coefficient", "SE", "df", "<i>t</i>", "<i>p</i>"),
      caption="Multilevel regression model predicting average reaction times and error rates from the different Stroop trial types aggregated within persons.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   pack_rows("DV: Reaction Time", 1, 3) %>% 
   pack_rows("DV: Error Rate", 4, 6) %>% 
   column_spec(1, width = "20em") %>%
   column_spec(2:5, width = "8em")

```

#### Visualization {-}

Here we present exemplary analyses of the Stroop effect for the one specification of reaction time (mean across all trials, no treatment of outliers) and error rate (error rate across all trials, no treatment of outliers). Below, we additionally provide a specification curve analysis of the Stroop effect. 

```{r , fig.asp=0.4, fig.width=11, out.width="\\textwidth", warning=F, message=F}

rt <- ggplot(stroop.short, aes(x = condition, y = mean.rt, fill = condition, colour = condition)) +
   theme_pub() +
   geom_point(position = position_jitter(width = .25), size = 1, alpha = .5) +
   geom_boxplot(outlier.shape = NA, alpha = 0.3, width = .15, colour = "BLACK") +
   guides(fill = "none", colour = "none") +
   ylab("Mean reaction   time") +
   theme_pub() +
   theme(axis.title.x = element_blank()) +
   ggtitle("Mean Reaction Time")

er <- ggplot(stroop.short, aes(x = condition, y = error.perc, fill = condition, colour = condition)) +
   theme_pub() +
   geom_point(position = position_jitter(width = .25, height = .1), size = 1, alpha = .5) +
   geom_boxplot(outlier.shape = NA, alpha = 0.5, width = .15, colour = "BLACK") +
   guides(fill = "none", colour = "none") +
   ylab("Error rate (%)") +
   theme_pub() +
   theme(axis.title.x = element_blank()) +
   ggtitle("Error Rate")

rt | er

```

### Descriptive Specification Curves {-}

```{r stroop-eff-spec-curves, fig.asp=0.8, fig.width=11, out.width="\\textwidth", warning=F, message=F}

p1a <- plot_curve_aov(results.stroop.rt,
                      "estimate_conditionincongruent",
                      desc = FALSE,
                      ci = FALSE,
                      ribbon = TRUE,
                      legend = FALSE,
                      null = 0) + 
   theme_pub() + ylab("numerical Stroop effect (in ms)") + ggtitle("Reaction Time") +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

p2a <- plot_curve_aov(results.stroop.er,
                      "estimate_conditionincongruent",
                      desc = FALSE,
                      ci = FALSE,
                      ribbon = TRUE,
                      legend = FALSE,
                      null = 0) + 
   theme_pub() + ylab("numerical Stroop effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) + ggtitle("Error Rate") +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

p1b <- plot_choices_aov(results.stroop.rt,
                        "estimate_conditionincongruent",
                        choices = c("identification", "treatment", "summary", "subset"),
                        desc = FALSE,
                        null = 0) + theme_pub() + 
   theme(strip.text.y = element_text(size=9,face="bold"),
         panel.spacing = unit(.6, "lines"),
         axis.text.y = element_text(size=10, hjust=1)) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

p2b <- plot_choices_aov(results.stroop.er,
                        "estimate_conditionincongruent",
                        choices = c("identification", "treatment"),
                        desc = FALSE,
                        null = 0) + theme_pub() +
   theme(strip.text.y = element_text(size=9,face="bold", hjust=1, margin=margin(l=10, b=10, r=10)),
         axis.text.y = element_text(size=10, hjust=1)) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

#p1/p2

cowplot::plot_grid(p1a, p2a, p1b, p2b, ncol=2, labels=c("A","B","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.3))

```

### Permutation Test {-}

```{r stroop-eff-perm-test}

## create table
tab <- bind_cols(data.frame(dv=c("Reaction time", "Error rate")), res.stroop)

tab$predictor <- c("Stroop effect: difference in reaction time (in ms) between incongruent and congruent trials",
                   "Stroop effect: difference in error rate (in %) between incongruent and congruent trials")
tab$med.eff[2] <- tab$med.eff[2]*100
tab$p.val <- printp(tab$p.val)
tab$n.sig <- round(tab$n.sig*100/tab$n.specs,0)


## print table
kable(tab,
      align = c("l", "l", "r", "r", "r", "r", "r"),
      escape = F, 
      col.names = c("DV", "Relevant predictor", "Number of specifications", "Median effect size", 
                    "Significant specifications (%)", "Number of shuffled samples with more significant 
                    specifications than for the original sample", "<i>p</i> value of permutation test")) %>% 
   add_header_above(c(" " = 2, 
                      "Description of specification curve\n(for original sample)" = 3, 
                      "Permutation test with\n500 shuffled samples" = 2)) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### Inferential Specification Curves {-}

```{r stroop-eff-perm-curves, fig.asp=0.3, fig.width=11, out.width="\\textwidth", warning=F, message=F}

p1 <- plot_curve_aov(results.stroop.rt, "estimate_conditionincongruent", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "numerical Stroop effect (in ms)") +
   theme_pub() + 
   geom_ribbon(data=conf.curves[["conf.curve.stroop.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   scale_x_continuous(breaks = seq(0,600,100)) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Reaction time") 

p2 <- plot_curve_aov(results.stroop.er, "estimate_conditionincongruent", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "numerical Stroop effect (in % of errors)") +
   theme_pub() + 
   geom_ribbon(data=conf.curves[["conf.curve.stroop.er"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   scale_y_continuous(labels = function(x) x * 100) +
   scale_x_continuous(breaks = seq(0,150,25)) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Error rates")

cowplot::plot_grid(p1, p2, ncol=2, labels=c("A","B"), label_size = 10, align = "v", axis = "rbl")

```

### Conclusion {-}

As expected, we found significant Stroop effects in the numerical Stroop task:

* Participants had significantly longer reaction times in incongruent trials than in congruent trials (median difference across all specifications: `r res.stroop$med.eff[1]`ms) 
* Participants made significantly more errors in incongruent trials than in congruent trials (median difference across all specifications: `r res.stroop$med.eff[2]*100`%)
* Permutation tests demonstrated that the null hypothesis of no Stroop effect had to be rejected across the whole specification curve, *p* `r printp(res.stroop$p.val[1], add_equals=T)` for reaction times and *p* `r printp(res.stroop$p.val[2], add_equals=T)` for error rates

## Analysis of Variance {.tabset .tabset-pills -}

### Descriptive Specification Curves {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r descriptive-spec-curves-aov-rt, eval=T, fig.width=11, fig.asp=1.5, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates."}

labels.pre = c("condition.beh + condition.sit", 
               "condition.beh + condition.sit + points.c",
               "condition.beh + condition.sit + points.c + points.diff",
               "condition.beh + condition.sit + points.diff")
labels.post = c("exp. groups",
                "exp. groups + points",
                "exp. groups + points + diff",
                "exp. groups + diff")

res.aov.rt.a <- res.aov.rt.a %>% 
   mutate(controls = factor(controls, 
                            levels=c("condition.beh + trait.a.ms + condition.sit", 
                                     "condition.beh + trait.a.ms + condition.sit + points.c",
                                     "condition.beh + trait.a.ms + condition.sit + points.c + points.diff",
                                     "condition.beh + trait.a.ms + condition.sit + points.diff"), 
                            labels=c("exp. groups",
                                     "exp. groups + points",
                                     "exp. groups + points + diff",
                                     "exp. groups + diff")))



res.aov.rt.h <- res.aov.rt.h %>% 
   mutate(controls = factor(controls, 
                            levels=c("condition.beh + trait.h.ms + condition.sit", 
                                     "condition.beh + trait.h.ms + condition.sit + points.c",
                                     "condition.beh + trait.h.ms + condition.sit + points.c + points.diff",
                                     "condition.beh + trait.h.ms + condition.sit + points.diff"), 
                            labels=c("exp. groups",
                                     "exp. groups + points",
                                     "exp. groups + points + diff",
                                     "exp. groups + diff")))

res.aov.rt.sit <- res.aov.rt.sit %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

a <- plot_curve_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("Trait Agreeableness x Behavior Conditions") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("Trait Honesty x Behavior Conditions") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_curve_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("Situation x Behavior Conditions") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

d <- plot_choices_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", 
                      choices = c("identification", "treatment", "conditions", "summary", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_choices_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", 
                      choices = c("identification", "treatment", "conditions", "summary", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


f <- plot_choices_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", 
                      choices = c("identification", "treatment", "conditions", "summary", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left



## combine plots
cowplot::plot_grid(a,b,d,e,c,NULL,f, ncol=2,  labels=c("A","B", "","", "C","", ""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.5))

```

```{r, fig.width=8, fig.asp=0.7, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Illustration of the significant interaction between median-split trait agreeableness and the behavior conditions predicting reaction times."}

d <- res.aov.rt.a %>% 
   select(contains("estimate")) %>% 
   summarize(intercept = median(`estimate_(Intercept)`),
             cond.high = median(`estimate_condition.behhigh agreeableness and honesty`),
             trait.high = median(`estimate_trait.a.mshigh trait agreeableness`),
             interaction = median(`estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness`)) 

df <- data.frame(value=rep(NA,4), trait=rep(c("disagreeable", "agreeable"),2), behavior=rep(c("disagreeable", "agreeable"),each=2))
df$value <- c(d$intercept, d$intercept+d$trait.high, d$intercept+d$cond.high, d$intercept+d$cond.high+d$trait.high+d$interaction)

ggplot(df, aes(x=behavior, y=value, group=trait, linetype=trait)) + theme_pub() +
   geom_line() + geom_point() +
   scale_x_discrete(expand=c(0.1,0.1)) +
   scale_y_continuous(limits=c(740,830)) +
   theme(legend.position = "right", legend.title = element_text(size=10,face="bold")) + 
   labs(x="Behavior conditions", y="Median predicted reaction time\nacross all specifications (in ms)", linetype="Trait Agreeableness") + ggtitle("Trait Agreeableness x Behavior Conditions")

```

#### Error Rate {-}

```{r descriptive-spec-curves-aov-error, eval=T, fig.width=11, fig.asp=1.5, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates."}

res.aov.er.a <- res.aov.er.a %>% 
   mutate(controls = factor(controls, 
                            levels=c("condition.beh + trait.a.ms + condition.sit", 
                                     "condition.beh + trait.a.ms + condition.sit + points.c",
                                     "condition.beh + trait.a.ms + condition.sit + points.c + points.diff",
                                     "condition.beh + trait.a.ms + condition.sit + points.diff"), 
                            labels=c("exp. groups",
                                     "exp. groups + points",
                                     "exp. groups + points + diff",
                                     "exp. groups + diff")))

res.aov.er.h <- res.aov.er.h %>% 
   mutate(controls = factor(controls, 
                            levels=c("condition.beh + trait.h.ms + condition.sit", 
                                     "condition.beh + trait.h.ms + condition.sit + points.c",
                                     "condition.beh + trait.h.ms + condition.sit + points.c + points.diff",
                                     "condition.beh + trait.h.ms + condition.sit + points.diff"), 
                            labels=c("exp. groups",
                                     "exp. groups + points",
                                     "exp. groups + points + diff",
                                     "exp. groups + diff")))

res.aov.er.sit <- res.aov.er.sit %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

a <- plot_curve_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("Trait-State Agreeableness") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("Trait-State Honesty") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_curve_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("Situation x Behavior Conditions") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

d <- plot_choices_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", 
                      choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_choices_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", 
                      choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


f <- plot_choices_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", 
                      choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,2400,500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left



## combine plots
cowplot::plot_grid(a,b,d,e,c,NULL,f, ncol=2,  labels=c("A","B", "","", "C","", ""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.5))

```

### Permutation Test {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r perm-tab-aov-rt, warning=FALSE, message=FALSE, error=FALSE, cache=T}

## create table
tab <- data.frame(congruence.op=rep("Interaction",3), 
                  predictor=c("Median-split Trait A x Behavior Conditions", 
                              "Median-split Trait H x Behavior Conditions",
                              "Situation x Behavior Conditions"),
                  n.specs = NA,
                  med.effect = NA,
                  perc.sig = NA,
                  n.shuff = NA,
                  p.val = NA)

## fill table
tab[c(1:3),3:7] <- res.aov.rt[,2:6]


## format table
tab$p.val <- printp(tab$p.val)
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))



## print table
kable(tab,
      align = c("l", "l", "r", "r", "r", "r", "r"),
      escape=F,
      col.names = c("Operationalization", 
                    "Relevant predictor", "Number of specifications", 
                    "Median effect size",  "Significant specifications (%)", 
                    "Number of shuffled samples\nwith more significant 
                    specifications\nthan for the original sample", "<i>p</i> value of permutation test")) %>% 
   add_header_above(c(" " = 2, 
                      "Description of specification curve (for original sample)" = 3, 
                      "Permutation test with 500 shuffled samples" = 2)) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

#### Error Rate {-}

```{r perm-tab-aov-er, warning=FALSE, message=FALSE, error=FALSE, cache=T}

## create table
tab <- data.frame(congruence.op=rep("Interaction",3), 
                  predictor=c("Median-split Trait A x Behavior Conditions", 
                              "Median-split Trait H x Behavior Conditions",
                              "Situation x Behavior Conditions"),
                  n.specs = NA,
                  med.effect = NA,
                  perc.sig = NA,
                  n.shuff = NA,
                  p.val = NA)

## fill table
tab[c(1:3),3:7] <- res.aov.error[,2:6]


## format table
tab$p.val <- printp(tab$p.val)
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))



## print table
kable(tab,
      align = c("l", "l", "r", "r", "r", "r", "r"),
      escape=F,
      col.names = c("Operationalization", 
                    "Relevant predictor", "Number of specifications", 
                    "Median effect size",  "Significant specifications (%)", 
                    "Number of shuffled samples with more significant 
                    specifications than for the original sample", "<i>p</i> value of permutation test")) %>% 
   add_header_above(c(" " = 2, 
                      "Description of specification curve (for original sample)" = 3, 
                      "Permutation test with 500 shuffled samples" = 2)) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### Inferential Specification Curves {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r perm-plot-aov-rt, eval=T, fig.width=11, fig.asp=0.8, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_aov(res.aov.rt.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.a"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Agreeableness x Behavior Conditions")

b <- plot_curve_aov(res.aov.rt.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.h"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Honesty x Behavior Conditions") 


c <- plot_curve_aov(res.aov.rt.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.rt"]][["results.aov.rt.sit"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Situation x Behavior Conditions") 

cowplot::plot_grid(a,b,c, ncol=2,  labels=c("A","B","C"), label_size = 12, align = "v", axis = "rbl")

```

#### Error Rate {-}

```{r perm-plot-aov-er, eval=T, fig.width=11, fig.asp=0.8, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_aov(res.aov.er.a, "estimate_condition.behhigh agreeableness and honesty:trait.a.mshigh trait agreeableness", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.a"]], 
               mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Agreeableness x Behavior Conditions")

b <- plot_curve_aov(res.aov.er.h, "estimate_condition.behhigh agreeableness and honesty:trait.h.mshigh trait honesty-humility", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.h"]], 
               mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait Honesty x Behavior Conditions") 


c <- plot_curve_aov(res.aov.er.sit, "estimate_condition.behhigh agreeableness and honesty:condition.sit2", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.aov.er"]][["results.aov.er.sit"]], 
               mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Situation x Behavior Conditions") 

cowplot::plot_grid(a,b,c, ncol=2,  labels=c("A","B","C"), label_size = 12, align = "v", axis = "rbl")

```

## Response Surface Analysis {.tabset .tabset-pills -}

### Descriptive Specification Curves {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r descriptive-spec-curves-rt, eval=T, fig.width=11, fig.asp=2.3, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop reaction times."}

labels.pre = c("condition.beh + condition.sit", 
               "condition.beh + condition.sit + points.c",
               "condition.beh + condition.sit + points.c + points.diff",
               "condition.beh + condition.sit + points.diff")
labels.post = c("exp. groups",
                "exp. groups + points",
                "exp. groups + points + diff",
                "exp. groups + diff")

res.rsa.rt.a <- res.rsa.rt.a %>%
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.rt.h <- res.rsa.rt.h %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.rt.a.adv <- res.rsa.rt.a.adv %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.rt.a.dec <- res.rsa.rt.a.dec %>%
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.rt.h.adv <- res.rsa.rt.h.adv %>%
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.rt.h.dec <- res.rsa.rt.h.dec %>%
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))



a <- plot_curve_rsa(res.rsa.rt.a, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("Trait-State Agreeableness") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_rsa(res.rsa.rt.h, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("Trait-State Honesty-Humility") +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_choices_rsa(res.rsa.rt.a, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

d <- plot_choices_rsa(res.rsa.rt.h, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_curve_rsa(res.rsa.rt.a.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("State Agreeableness-Adversity")+
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

f <- plot_curve_rsa(res.rsa.rt.h.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("State Honesty-Adversity")+
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

g <- plot_choices_rsa(res.rsa.rt.a.adv, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

h <- plot_choices_rsa(res.rsa.rt.h.adv, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


i <- plot_curve_rsa(res.rsa.rt.a.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("State Agreeableness-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

j <- plot_curve_rsa(res.rsa.rt.h.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect (in ms)") +
   theme_pub() + ggtitle("State Honesty-Deception")+
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

k <- plot_choices_rsa(res.rsa.rt.a.dec, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

l <- plot_choices_rsa(res.rsa.rt.h.dec, "est_b4", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   scale_x_continuous(breaks=seq(0,9000,1500)) +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold"),
         panel.spacing = unit(.75, "lines")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), 
                   label_size = 10, align = "v", axis = "rbl", rel_heights = c(1,2,1,2,1,2))

```

```{r descriptive-spec-curves-fit-rt, eval=T, fig.width=11, fig.asp=2.3, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state congruence and state-situation congruence predicting Stroop reaction times."}

a <- plot_curve_rsa(res.rsa.rt.a, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("Trait-State Agreeableness") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_rsa(res.rsa.rt.h, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("Trait-State Honesty-Humility") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_choices_rsa(res.rsa.rt.a, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

d <- plot_choices_rsa(res.rsa.rt.h, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_curve_rsa(res.rsa.rt.a.adv, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Agreeableness-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

f <- plot_curve_rsa(res.rsa.rt.h.adv, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Honesty-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

g <- plot_choices_rsa(res.rsa.rt.a.adv, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

h <- plot_choices_rsa(res.rsa.rt.h.adv, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


i <- plot_curve_rsa(res.rsa.rt.a.dec, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Agreeableness-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

j <- plot_curve_rsa(res.rsa.rt.h.dec, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Honesty-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

k <- plot_choices_rsa(res.rsa.rt.a.dec, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

l <- plot_choices_rsa(res.rsa.rt.h.dec, "fit", choices = c("identification", "treatment", "summary", "trials", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.8,1,1.8,1,1.8))

```

#### Error Rate {-}

```{r descriptive-spec-curves-error, eval=T, fig.width=11, fig.asp=2, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state and state-situation interaction coefficients predicting Stroop error rates."}

res.rsa.er.a <- res.rsa.er.a %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.er.h <- res.rsa.er.h %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.er.a.adv <- res.rsa.er.a.adv %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.er.a.dec <- res.rsa.er.a.dec %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.er.h.adv <- res.rsa.er.h.adv %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

res.rsa.er.h.dec <- res.rsa.er.h.dec %>% 
   mutate(controls = factor(controls, levels=labels.pre, labels=labels.post))

a <- plot_curve_rsa(res.rsa.er.a, "est_b4", ci = FALSE, ribbon = T) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("Trait-State Agreeableness") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   scale_y_continuous(labels = function(x) x * 100) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_rsa(res.rsa.er.h, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + ggtitle("Trait-State Honesty-Humility") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_choices_rsa(res.rsa.er.a, "est_b4", choices = c("identification", "treatment", "conditions","controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

d <- plot_choices_rsa(res.rsa.er.h, "est_b4", choices = c("identification", "treatment", "conditions","controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_curve_rsa(res.rsa.er.a.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + ggtitle("State Agreeableness-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

f <- plot_curve_rsa(res.rsa.er.h.adv, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   scale_y_continuous(labels = function(x) x * 100) +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("State Honesty-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

g <- plot_choices_rsa(res.rsa.er.a.adv, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

h <- plot_choices_rsa(res.rsa.er.h.adv, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

i <- plot_curve_rsa(res.rsa.er.a.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   scale_y_continuous(labels = function(x) x * 100) +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   theme_pub() + ggtitle("State Agreeableness-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

j <- plot_curve_rsa(res.rsa.er.h.dec, "est_b4", ci = FALSE, ribbon = TRUE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + ggtitle("State Honesty-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

k <- plot_choices_rsa(res.rsa.er.a.dec, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

l <- plot_choices_rsa(res.rsa.er.h.dec, "est_b4", choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10, hjust=1),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 12, align = "v", axis = "rbl", rel_heights = c(1, 1.6,1,1.6,1,1.6))

```

```{r descriptive-spec-curves-fit-error, eval=T, fig.width=11, fig.asp=2.3, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Descriptive specification curves of trait-state congruence and state-situation congruence predicting Stroop error rates."}

a <- plot_curve_rsa(res.rsa.er.a, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed",  color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("Trait-State Agreeableness") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

b <- plot_curve_rsa(res.rsa.er.h, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("Trait-State Honesty-Humility") +
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

c <- plot_choices_rsa(res.rsa.er.a, "fit", choices = c("identification", "treatment", "conditions",  "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

d <- plot_choices_rsa(res.rsa.er.h, "fit", choices = c("identification", "treatment","conditions",  "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

e <- plot_curve_rsa(res.rsa.er.a.adv, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Agreeableness-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

f <- plot_curve_rsa(res.rsa.er.h.adv, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Honesty-Adversity")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

g <- plot_choices_rsa(res.rsa.er.a.adv, "fit", choices = c("identification", "treatment","conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

h <- plot_choices_rsa(res.rsa.er.h.adv, "fit", choices = c("identification", "treatment","conditions",  "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


i <- plot_curve_rsa(res.rsa.er.a.dec, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Agreeableness-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

j <- plot_curve_rsa(res.rsa.er.h.dec, "fit", ci = FALSE, ribbon = FALSE) +
   geom_hline(yintercept = 0,  linetype = "dashed", color = "black") +
   labs(x = "", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + ggtitle("State Honesty-Deception")+
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(20,0,-20,-10), "pt")) #top, right, bottom, left

k <- plot_choices_rsa(res.rsa.er.a.dec, "fit", choices = c("identification", "treatment", "conditions", "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left

l <- plot_choices_rsa(res.rsa.er.h.dec, "fit", choices = c("identification", "treatment", "conditions",  "controls")) +
   labs(x = "specifications (ranked)") + theme_pub() +
   theme(strip.text.x = element_blank()) + 
   theme(axis.text.y = element_text(size=10),
         axis.text.x = element_text(size=10),
         strip.text.y = element_text(size=9),
         axis.title=element_text(size=10,face="bold")) +
   theme(plot.margin = unit(c(0,0,10,-10), "pt")) #top, right, bottom, left


## combine plots
cowplot::plot_grid(a,b,c,d,e,f,g,h,i,j,k,l, ncol=2, labels=c("A","B","","","C","D","","","E","F","",""), label_size = 10, align = "v", axis = "rbl", rel_heights = c(1, 1.6,1,1.6,1,1.6))

```

### Permutation Test {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r perm-tab, warning=FALSE, message=FALSE, error=FALSE, cache=T}

## create table
tab <- data.frame(congruence.op=c(rep("Interaction",6), rep("Fit pattern",6)),
                  predictor=rep(c("Trait A x State A", "Trait HH x State HH",
                                    "State A x Adversity", "State A x Deception",
                                    "State HH x Adversity", "State HH x Deception"),2),
                  n.specs = NA,
                  med.effect = NA,
                  perc.sig = NA,
                  n.shuff = NA,
                  p.val = NA)

## fill table
tab[,3:7] <- res.rsa.rt[,2:6]


## format table
tab$p.val <- printp(tab$p.val)
tab$med.effect[c(7:12)] <- ifelse(tab$med.effect[c(7:12)]==0, "no", "yes")
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))



## print table
kable(tab,
      align = c("l", "l", "r", "r", "r", "r", "r"),
      col.names = c("Operationalization", 
                    "Relevant predictor", "Number of specifications", 
                    "Median effect size",  "Significant specifications (%)", 
                    "Number of shuffled samples with more significant 
                    specifications than for the original sample", "<i>p</i> value of permutation test")) %>% 
   add_header_above(c(" " = 2, 
                      "Description of specification curve (for original sample)" = 3, 
                      "Permutation test with 500 shuffled samples" = 2)) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

#### Error Rate {-}

```{r perm-tab-er, warning=FALSE, message=FALSE, error=FALSE, cache=T}

## create table
tab <- data.frame(congruence.op=c(rep("Interaction",6), rep("Fit pattern",6)),
                  predictor=rep(c("Trait A x State A", "Trait HH x State HH",
                                    "State A x Adversity", "State A x Deception",
                                    "State HH x Adversity", "State HH x Deception"),2),
                  n.specs = NA,
                  med.effect = NA,
                  perc.sig = NA,
                  n.shuff = NA,
                  p.val = NA)

## fill table
tab[,3:7] <- res.rsa.error[,2:6]



## format table
tab$p.val <- printp(tab$p.val)
tab$med.effect[c(7:12)] <- ifelse(tab$med.effect[c(7:12)]==0, "no", "yes")
tab$perc.sig <- ifelse(is.na(tab$perc.sig),NA,paste0(round(tab$perc.sig*100/tab$n.specs,0),"%"))



## print table
kable(tab,
      align = c("l", "l", "r", "r", "r", "r", "r"),
      col.names = c("Operationalization", 
                    "Relevant predictor", "Number of specifications", "Median effect size", 
                    "Significant specifications (%)", "Number of shuffled samples with more significant 
                    specifications than for the original sample", "<i>p</i> value of permutation test")) %>% 
   add_header_above(c(" " = 2, 
                      "Description of specification curve (for original sample)" = 3, 
                      "Permutation test with 500 shuffled samples" = 2)) %>% 
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) 

```

### Inferential Specification Curves {.tabset .tabset-pills -}

#### Reaction Time {-}

```{r perm-rsa-rt, eval=T, fig.width=11, fig.asp=1.2, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_rsa(res.rsa.rt.a, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.trait.a.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")

b <- plot_curve_rsa(res.rsa.rt.h, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.trait.h.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility") 


c <- plot_curve_rsa(res.rsa.rt.a.adv, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.a.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity") 

d <- plot_curve_rsa(res.rsa.rt.h.adv, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.h.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")

e <- plot_curve_rsa(res.rsa.rt.a.dec, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.a.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")

f <- plot_curve_rsa(res.rsa.rt.h.dec, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt"]][["state.h.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")



cowplot::plot_grid(a,b,c,NULL,NULL,NULL, ncol=2, labels=c("A","B","C","D","E","F"), 
                   label_size = 12, align = "v", axis = "rbl")

```

```{r perm-rsa-fit-rt, eval=F, fig.width=11, fig.asp=1.2, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_rsa(res.rsa.rt.a, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.trait.a.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")

b <- plot_curve_rsa(res.rsa.rt.h, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.trait.h.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility") 


c <- plot_curve_rsa(res.rsa.rt.a.adv, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.a.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity") 

d <- plot_curve_rsa(res.rsa.rt.h.adv, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.h.adv.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity")

e <- plot_curve_rsa(res.rsa.rt.a.dec, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.a.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")

f <- plot_curve_rsa(res.rsa.rt.h.dec, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect (in ms)") +
   theme_pub() + scale_x_continuous(breaks=seq(0,9000,1000)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.rt.fit"]][["state.h.dec.rt"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception")


cowplot::plot_grid(a,b,c,d,e,f, ncol=2, labels=c("A","B","C","D","E","F"), 
                   label_size = 12, align = "v", axis = "rbl")

```

#### Error Rate {-}

```{r perm-rsa-error, eval=T, fig.width=11, fig.asp=1.2, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_rsa(res.rsa.er.a, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.trait.a.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness")

b <- plot_curve_rsa(res.rsa.er.h, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.trait.h.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility") 


c <- plot_curve_rsa(res.rsa.er.a.adv, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.a.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity") 

d <- plot_curve_rsa(res.rsa.er.h.adv, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.h.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity") 


e <- plot_curve_rsa(res.rsa.er.a.dec, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.a.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")

f <- plot_curve_rsa(res.rsa.er.h.dec, "est_b4", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "interaction effect\n(in % of errors)") +
   scale_y_continuous(labels = function(x) x * 100) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er"]][["state.h.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception") 


## combine plots
(a|b)/
   (c|d)/
   (e|f) + plot_annotation(tag_levels = 'A') 

```

```{r perm-rsa-fit-error, eval=F, fig.width=11, fig.asp=1.2, warning=FALSE, message=FALSE, error=FALSE, cache=T, fig.cap="Comparison of observed (descriptive) specification curves (black dots) and expected under-the-null specification curves (shaded area). The shaded area represents the range of effects observed in the shuffled datasets (between the 2.5th and and 97.5th percentiles of the ranked estimates)."}

a <- plot_curve_rsa(res.rsa.er.a, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.trait.a.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Agreeableness") 

b <- plot_curve_rsa(res.rsa.er.h, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.trait.h.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("Trait-State Honesty-Humility") 


c <- plot_curve_rsa(res.rsa.er.a.adv, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.a.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)), 
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Adversity") 

d <- plot_curve_rsa(res.rsa.er.h.adv, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.h.adv.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Adversity") 


e <- plot_curve_rsa(res.rsa.er.a.dec, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.a.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Agreeableness-Deception")

f <- plot_curve_rsa(res.rsa.er.h.dec, "fit", ci = FALSE, ribbon = F) +
   geom_hline(yintercept = 0, linetype = "solid", color = "black", size=0.5) +
   labs(x = "specifications (ranked)", y = "fit pattern") +
   scale_y_discrete(breaks=c(FALSE, TRUE), limits=c(FALSE, TRUE)) +
   theme_pub() + scale_x_continuous(breaks=seq(0,2400,500)) +
   geom_ribbon(data=conf.curves[["conf.curves.rsa.er.fit"]][["state.h.dec.e"]], mapping=(aes(ymin=low.perc, ymax=high.perc, x=rank)),
               inherit.aes = FALSE, alpha=.2, linetype="dashed", color="black")+
   geom_point(aes(color = "black"), size = 1) +
   theme(axis.title=element_text(size=10,face="bold")) + ggtitle("State Honesty-Deception") 

(a|b)/
   (c|d)/
   (e|f) + plot_annotation(tag_levels = 'A') 

```

# Exploratory Analyses {.tabset .tabset-pills -}

## Difference Scores for Congruence {.tabset .tabset-pills -}

### Descriptive Statistics {-}

We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study. 

```{r difference-score-tab, warning=FALSE, message=FALSE, error=FALSE}

data$diff.a <- data$state.a-data$trait.a.long
data$diff.h <- data$state.h-data$trait.h.long
data$diff.a.a <- data$state.a-8-data$adv
data$diff.d.a <- data$state.a-8-data$dec
data$diff.a.h <- data$state.h-8-data$adv
data$diff.d.h <- data$state.h-8-data$dec


tab <- bind_rows(as.data.frame(describe(data$diff.a))[,2:13],
                 as.data.frame(describe(data$diff.h))[,2:13],
                 as.data.frame(describe(data$diff.a.a))[,2:13],
                 as.data.frame(describe(data$diff.d.a))[,2:13],
                 as.data.frame(describe(data$diff.a.h))[,2:13],
                 as.data.frame(describe(data$diff.d.h))[,2:13]
)
row.names(tab) <- c("State A - Trait A", "State HH - Trait HH", 
                    "State A - Adversity(r)", "State A - Deception(r)",
                    "State HH - Adversity(r)", "State HH - Deception(r)")

kable(tab,
      digits=2,
      row.names = TRUE,
      col.names = c("n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis", "se"),
      caption = "Descriptive statistics of the descriptive difference scores representing congruence in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   footnote(general="Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. ")

```

### Histograms {-}

We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study. 

```{r, fig.asp=1.2, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between personality traits and personality states and between situation characteristics and personality states as a descriptive indicator of congruence."}

a <- ggplot(data=data, aes(x=trait.h-state.h)) + theme_pub() + 
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="Trait-State Congruence:\nHonesty", x="Deviation of state honesty from trait honesty") 

b <- ggplot(data=data, aes(x=trait.a-state.a)) + theme_pub() +
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="Trait-State Congruence:\nAgreeableness", x="Deviation of state agreeableness from trait agreeableness")

c <- ggplot(data=data, aes(x=8-adv-state.a)) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="State-Situation Congruence:\nAgreeableness and Adversity", 
        x="Deviation of state agreeableness from adversity (reverse coded)")

d <- ggplot(data=data, aes(x=8-dec-state.a)) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="State-Situation Congruence:\nAgreeableness and Deception", 
        x="Deviation of state agreeableness from deception (reverse coded)")

e <- ggplot(data=data, aes(x=8-adv-state.h)) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="State-Situation Congruence:\nHonesty and Adversity", 
        x="Deviation of state honesty from adversity (reverse coded)")

f <- ggplot(data=data, aes(x=8-dec-state.h)) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1)  +
   scale_x_continuous(limits=c(-4.5,4.5), breaks=c(-4:4), labels=c(-4:4)) + 
   scale_y_continuous(expand = c(0, 0.01)) +
   labs(title="State-Situation Congruence:\nHonesty and Deception", 
        x="Deviation of state honesty from deception (reverse coded)")

(a + b)/
   plot_spacer()/
   (c + d)/
   plot_spacer()/
   (e + f) + plot_annotation(tag_levels="A") + plot_layout(heights=c(1,0.2,1,0.2,1))

```

## Difference Scores in the Experimental Conditions {.tabset .tabset-pills -}

### Descriptive Statistics {-}

We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study. 

```{r difference-score-tab-2, warning=FALSE, message=FALSE, error=FALSE}

data$diff.a <- data$state.a-data$trait.a.long
data$diff.h <- data$state.h-data$trait.h.long
data$diff.a.a <- 8-data$adv-data$state.a
data$diff.d.a <- 8-data$dec-data$state.a
data$diff.a.h <- 8-data$adv-data$state.h
data$diff.d.h <- 8-data$dec-data$state.h


tab <- bind_rows(describeBy(data$diff.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$diff.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$diff.a.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$diff.d.a, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$diff.a.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15],
                 describeBy(data$diff.d.h, group=list(data$condition.beh, data$condition.sit), mat=TRUE, digits=2)[,2:15]
)
names(tab)[1] <- "Experimental conditions"

kable(tab,
      row.names = FALSE,
      col.names = c("behavior condition", "situation condition", "vars", "n", "mean", "sd", "median", 
                    "trimmed", "mad", "min", "max", "range", "skew", "kurtosis"),
      caption = "Descriptive statistics of the descriptive difference scores representing congruence in the different experimental groups.") %>%  
   kable_styling(bootstrap_options = c("striped", "hover"), fixed_thead = T) %>% 
   pack_rows("DV: Difference between trait agreeableness and state agreeableness", 1, 4) %>%
   pack_rows("DV: Difference between trait honesty-humility and state honesty-humility", 5, 8) %>%
   pack_rows("DV: Difference between aversity(r) and state agreeableness", 9, 12) %>%
   pack_rows("DV: Difference between deception(r) and state agreeableness", 13, 16) %>%
   pack_rows("DV: Difference between adversity(r) and state honesty-humility", 17, 20) %>%
   pack_rows("DV: Difference between deception(r) and state honesty-humility", 21, 24) %>% 
   footnote(general="Adversity and deception were reverse-coded (indicted by (r) ) such that higher levels indicate less adversity and deception, respectively. ")

```

### Histograms {-}

We calculated descriptive difference score between personality traits and personality states and between situation characteristics and personality states to represent congruence. However, these difference scores should be interpreted with caution because they assume strong equivalence between the trait and state scales which may not be given in this study. 

```{r, fig.asp=0.5, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between personality traits and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.beh=="high agreeableness and honesty"), aes(x=diff.a)) + 
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   labs(title="high agreeableness and honesty-condition", 
        x="Deviation of state agreeableness from trait agreeableness") + xlim(-6,6) + theme_pub() 

p2 <- ggplot(subset(data, condition.beh=="low agreeableness and honesty"), aes(x=diff.a)) + 
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   labs(title="low agreeableness and honesty-condition", 
        x="Deviation of state agreeableness from trait agreeableness") + xlim(-6,6) + theme_pub()


## combine plots
(p1+p2) + plot_annotation(
   title = 'Trait-State Congruence',
   subtitle = 'Difference scores between trait agreeableness and state agreeableness in the two behavior conditions'
) 

```

```{r, fig.asp=0.5, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between personality traits and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.beh=="high agreeableness and honesty"), aes(x=diff.h)) + 
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   labs(title="high agreeableness and honesty-condition", 
        x="Deviation of state honesty-humility from trait honesty-humility") + xlim(-6,6) + theme_pub() 

p2 <- ggplot(subset(data, condition.beh=="low agreeableness and honesty"), aes(x=diff.h)) + 
   geom_histogram(color="black", fill=cols.beh[1], binwidth=1) + 
   labs(title="low agreeableness and honesty-condition", 
        x="Deviation of state honesty-humility from trait honesty-humility") + xlim(-6,6) + theme_pub()


## combine plots
(p1+p2) + plot_annotation(
   title = 'Trait-State Congruence',
   subtitle = 'Difference scores between trait honesty and state honesty in the two behavior conditions'
) 

```

```{r, fig.asp=0.8, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behehavior: high, situation: high)",
        x="Deviation of state agreeableness from adversity (reverse coded)")

p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behavior: low, situation: low)",
        x="Deviation of state agreeableness from adversity (reverse coded)")

p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: high, situation: low)",
        x="Deviation of state agreeableness from adversity (reverse coded)")

p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.a.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: low, situation: high)",
        x="Deviation of state agreeableness from adversity (reverse coded)")


## combine plots
(p1+p2)/
   plot_spacer()/
   (p3+p4) + plot_layout(heights=c(1,0.2,1)) + 
   plot_annotation(
      title = 'State-Situation Congruence',
      subtitle = 'Difference scores between reverse-coded adversity and state agreeableness in the four experimental groups'
   ) 

```

```{r, fig.asp=0.8, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behehavior: high, situation: high)",
        x="Deviation of state agreeableness from deception (reverse coded)")

p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behavior: low, situation: low)",
        x="Deviation of state agreeableness from deception (reverse coded)")

p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: high, situation: low)",
        x="Deviation of state agreeableness from deception (reverse coded)")

p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.d.a)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: low, situation: high)",
        x="Deviation of state agreeableness from deception (reverse coded)")


## combine plots
(p1+p2)/
   plot_spacer()/
   (p3+p4) + plot_layout(heights=c(1,0.2,1)) + 
   plot_annotation(
      title = 'State-Situation Congruence',
      subtitle = 'Difference scores between reverse-coded deception and state agreeableness in the four experimental groups'
   ) 

```

```{r, fig.asp=0.8, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behehavior: high, situation: high)",
        x="Deviation of state honesty from adversity (reverse coded)")

p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behavior: low, situation: low)",
        x="Deviation of state honesty from adversity (reverse coded)")

p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: high, situation: low)",
        x="Deviation of state honesty from adversity (reverse coded)")

p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.a.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: low, situation: high)",
        x="Deviation of state honesty from adversity (reverse coded)")


## combine plots
(p1+p2)/
   plot_spacer()/
   (p3+p4) + plot_layout(heights=c(1,0.2,1)) + 
   plot_annotation(
      title = 'State-Situation Congruence',
      subtitle = 'Difference scores between reverse-coded adversity and state honesty in the four experimental groups'
   )

```

```{r, fig.asp=0.8, fig.width=11, warning=FALSE, message=FALSE, error=FALSE, fig.cap="Histograms of difference scores between between situation characteristics and personality states as a descriptive indicator of congruence in the different experimental groups."}

p1 <- ggplot(subset(data, condition.bs=="friendly-friendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behehavior: high, situation: high)",
        x="Deviation of state honesty from deception (reverse coded)")

p2 <- ggplot(subset(data, condition.bs=="unfriendly-unfriendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="congruent", subtitle="(behavior: low, situation: low)",
        x="Deviation of state honesty from deception (reverse coded)")

p3 <- ggplot(subset(data, condition.bs=="friendly-unfriendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: high, situation: low)",
        x="Deviation of state honesty from deception (reverse coded)")

p4 <- ggplot(subset(data, condition.bs=="unfriendly-friendly"), aes(x=diff.d.h)) + xlim(-6,6) + theme_pub() +
   geom_histogram(color="black", fill=cols.sit[1], binwidth=1) + 
   labs(title="incongruent", subtitle="(behavior: low, situation: high)",
        x="Deviation of state honesty from deception (reverse coded)")


## combine plots
(p1+p2)/
   plot_spacer()/
   (p3+p4) + plot_layout(heights=c(1,0.2,1)) +
   plot_annotation(
      title = 'State-Situation Congruence',
      subtitle = 'Difference scores between reverse-coded deception and state honesty in the four experimental groups'
   ) 

```