--- title: "Generosity vs Justice V8" author: 'Marcus + Lizy + Nadia' date: "9/6/2023" format: html: code-fold: true code-tools: true fontsize: 16px self-contained: true toc: true toc-location: left output: html_document: code_folding: hide number_sections: true toc: true toc_float: true toc_collapsed: true toc_depth: 3 keep_md: true header-includes: - \usepackage{Roboto Condensed} knitr: opts_chunk: dev: ragg_png execute: warning: false --- # Overview and Methodology - Study of third party judgments of donors motivated by a justice or generosity virtue when the information is shared either from a third person perspective- Data collected September 22, 2023- N = 300# Setup {.tabset .tabset-pills} ## Libraries and functions {.unlisted .unnumbered} ```{r setup, message=F, results='hide', warning=FALSE} :: opts_chunk$ set (warning = FALSE , message = FALSE ) <- c ("lme4" ,"tidyverse" ,"effects" ,"ggplot2" ,"psych" ,"MASS" ,"Rmisc" ,"lmerTest" ,"ggthemes" , "knitr" ,"lsmeans" ,"pastecs" ,"sjstats" ,"car" ,"ordinal" ,"Rcpp" ,"corrplot" , "ggpubr" , "EnvStats" ,"easyStats" , "cowplot" ,"see" ,"datawizard" , "ggcorrplot" , "RColorBrewer" , "lavaan" )#install.packages(Mypackages) #you must remove the # in this comment if you need to install the packages! lapply (Mypackages,character.only = TRUE )options (knitr.kable.NA = '—' )set.seed (1 ) ``` ## Load Data ```{r, message=F, results='hide', warning=FALSE} <- read.csv ("/Users/mtrenfield17/Desktop/Research/Boston College Research/Morality Lab Research/Generosity Vs Justice Project/just_vs_gen_v8/Generosity vs Justice V8.csv" )``` ## Reshaping data ```{r} <- gjg_raw %>% filter (consent == 5 , attn_bucket_num == 1 , Finished == 1 , attn_self >= 3 )# Explanation: # filter() function is used to filter rows based on specified conditions. # Rows are kept where consent is 5, attn_bucket_num is 1, Finished is 1, and attn_self is 3 or greater. # changing appropriate DVs to numeric <- c ("Age" , "Income" , "SES" , "Political_overall" , "Political_social" , "Political_economic" , "attn_self" , "PID" ,"Religiosity" , "Education" )<- lapply (gjg[cols_to_convert], as.numeric)<- gjg %>% mutate (norm_signal_just = ifelse (vigFirstVirtue == "justice" , norm_signal_10,ifelse (vigFirstVirtue == "generosity" , norm_signal_11, NA )),norm_signal_gen = ifelse (vigFirstVirtue == "generosity" , norm_signal_10,ifelse (vigFirstVirtue == "justice" , norm_signal_11, NA ))<- gjg %>% mutate (reputation_signal_just = ifelse (vigFirstVirtue == "justice" , reputation_signal_1,ifelse (vigFirstVirtue == "generosity" , reputation_signal_2, NA )),reputation_signal_gen = ifelse (vigFirstVirtue == "generosity" , reputation_signal_1,ifelse (vigFirstVirtue == "justice" , reputation_signal_2, NA ))<- gjg %>% mutate (approval_just = ifelse (vigFirstVirtue == "justice" , approval_1,ifelse (vigFirstVirtue == "generosity" , approval_2, NA )),approval_gen = ifelse (vigFirstVirtue == "generosity" , approval_1,ifelse (vigFirstVirtue == "justice" , approval_2, NA ))<- gjg %>% mutate (moral_just = ifelse (vigFirstVirtue == "justice" , moral_1,ifelse (vigFirstVirtue == "generosity" , moral_2, NA )),moral_gen = ifelse (vigFirstVirtue == "generosity" , moral_1,ifelse (vigFirstVirtue == "justice" , moral_2, NA ))<- gjg %>% mutate (genuine_just = ifelse (vigFirstVirtue == "justice" , genuine_1,ifelse (vigFirstVirtue == "generosity" , genuine_2, NA )),genuine_gen = ifelse (vigFirstVirtue == "generosity" , genuine_1,ifelse (vigFirstVirtue == "justice" , genuine_2, NA ))<- gjg %>% mutate (deliberate_just = ifelse (vigFirstVirtue == "justice" , deliberate_1,ifelse (vigFirstVirtue == "generosity" , deliberate_2, NA )),deliberate_gen = ifelse (vigFirstVirtue == "generosity" , deliberate_1,ifelse (vigFirstVirtue == "justice" , deliberate_2, NA ))<- gjg %>% mutate (spontaneous_just = ifelse (vigFirstVirtue == "justice" , spontaneous_1,ifelse (vigFirstVirtue == "generosity" , spontaneous_2, NA )),spontaneous_gen = ifelse (vigFirstVirtue == "generosity" , spontaneous_1,ifelse (vigFirstVirtue == "justice" , spontaneous_2, NA ))<- gjg %>% mutate (emotion_just = ifelse (vigFirstVirtue == "justice" , emotion_1,ifelse (vigFirstVirtue == "generosity" , emotion_2, NA )),emotion_gen = ifelse (vigFirstVirtue == "generosity" , emotion_1,ifelse (vigFirstVirtue == "justice" , emotion_2, NA ))<- gjg %>% mutate (logic_just = ifelse (vigFirstVirtue == "justice" , logic_1,ifelse (vigFirstVirtue == "generosity" , logic_2, NA )),logic_gen = ifelse (vigFirstVirtue == "generosity" , logic_1,ifelse (vigFirstVirtue == "justice" , logic_2, NA ))<- gjg %>% mutate (quick_just = ifelse (vigFirstVirtue == "justice" , quick_1,ifelse (vigFirstVirtue == "generosity" , quick_2, NA )),quick_gen = ifelse (vigFirstVirtue == "generosity" , quick_1,ifelse (vigFirstVirtue == "justice" , quick_2, NA ))<- gjg %>% mutate (slow_just = ifelse (vigFirstVirtue == "justice" , slow_1,ifelse (vigFirstVirtue == "generosity" , slow_2, NA )),slow_gen = ifelse (vigFirstVirtue == "generosity" , slow_1,ifelse (vigFirstVirtue == "justice" , slow_2, NA ))<- gjg %>% mutate (warm_just = ifelse (vigFirstVirtue == "justice" , warm_1,ifelse (vigFirstVirtue == "generosity" , warm_2, NA )),warm_gen = ifelse (vigFirstVirtue == "generosity" , warm_1,ifelse (vigFirstVirtue == "justice" , warm_2, NA ))<- gjg %>% mutate (good_natured_just = ifelse (vigFirstVirtue == "justice" , good.natured_1,ifelse (vigFirstVirtue == "generosity" , good.natured_2, NA )),good_natured_gen = ifelse (vigFirstVirtue == "generosity" , good.natured_1,ifelse (vigFirstVirtue == "justice" , good.natured_2, NA ))<- gjg %>% mutate (tolerant_just = ifelse (vigFirstVirtue == "justice" , tolerant_1,ifelse (vigFirstVirtue == "generosity" , tolerant_2, NA )),tolerant_gen = ifelse (vigFirstVirtue == "generosity" , tolerant_1,ifelse (vigFirstVirtue == "justice" , tolerant_2, NA ))<- gjg %>% mutate (sincere_just = ifelse (vigFirstVirtue == "justice" , sincere_1,ifelse (vigFirstVirtue == "generosity" , sincere_2, NA )),sincere_gen = ifelse (vigFirstVirtue == "generosity" , sincere_1,ifelse (vigFirstVirtue == "justice" , sincere_2, NA ))<- gjg %>% mutate (competent_just = ifelse (vigFirstVirtue == "justice" , competent_1,ifelse (vigFirstVirtue == "generosity" , competent_2, NA )),competent_gen = ifelse (vigFirstVirtue == "generosity" , competent_1,ifelse (vigFirstVirtue == "justice" , competent_2, NA ))<- gjg %>% mutate (confident_just = ifelse (vigFirstVirtue == "justice" , confident_1,ifelse (vigFirstVirtue == "generosity" , confident_2, NA )),confident_gen = ifelse (vigFirstVirtue == "generosity" , confident_1,ifelse (vigFirstVirtue == "justice" , confident_2, NA ))<- gjg %>% mutate (independent_just = ifelse (vigFirstVirtue == "justice" , independent_1,ifelse (vigFirstVirtue == "generosity" , independent_2, NA )),independent_gen = ifelse (vigFirstVirtue == "generosity" , independent_1,ifelse (vigFirstVirtue == "justice" , independent_2, NA ))<- gjg %>% mutate (competitive_just = ifelse (vigFirstVirtue == "justice" , competitive_1,ifelse (vigFirstVirtue == "generosity" , competitive_2, NA )),competitive_gen = ifelse (vigFirstVirtue == "generosity" , competitive_1,ifelse (vigFirstVirtue == "justice" , competitive_2, NA ))<- gjg %>% mutate (intelligent_just = ifelse (vigFirstVirtue == "justice" , intelligent_1,ifelse (vigFirstVirtue == "generosity" , intelligent_2, NA )),intelligent_gen = ifelse (vigFirstVirtue == "generosity" , intelligent_1,ifelse (vigFirstVirtue == "justice" , intelligent_2, NA ))<- gjg %>% mutate (politics_just = ifelse (vigFirstVirtue == "justice" , politics_1,ifelse (vigFirstVirtue == "generosity" , politics_2, NA )),politics_gen = ifelse (vigFirstVirtue == "generosity" , politics_1,ifelse (vigFirstVirtue == "justice" , politics_2, NA ))<- gjg %>% mutate (first_text = ifelse (grepl ("Value" , vigOrder, ignore.case = TRUE ), "value" , ifelse (grepl ("Commit" , vigOrder, ignore.case = TRUE ), "commit" , NA ))``` # Attention check ```{r,message=F, results='hide', warning=FALSE} # filtering out failures/nonresponses to attention checks length (gjg_raw$ PID)<- gjg_raw %>% filter (consent == 5 )length (gjg_raw_filter$ PID)<- gjg_raw_filter %>% filter (! is.na (attn_bucket), attn_self> 2 , attn_bucket_num == 1 )length (gjg_raw_filter$ PID)``` # Demographics ```{r,message=F, results='hide', warning=FALSE} # Analyzing numeric variables # Specify the variables you want to include in the table <- c ("Age" , "Income" , "Education" , "SES" , "Political_overall" , "Political_social" , "Political_economic" , "Religiosity" )# Create an empty data frame to store the summary <- data.frame (Variable = character (0 ), Mean = numeric (0 ), Median = numeric (0 ),Min = numeric (0 ), Max = numeric (0 ),N = numeric (0 ))# Loop through each variable and calculate summary statistics for (var in vars_to_include) {<- summary (gjg[[var]])<- data.frame (Variable = var,Mean = mean (gjg[[var]], na.rm = TRUE ),Median = median (gjg[[var]], na.rm = TRUE ),Min = min (gjg[[var]], na.rm = TRUE ),Max = max (gjg[[var]], na.rm = TRUE ),N = length (gjg[[var]]))<- rbind (summary_table, var_data)# Print the summary table print (summary_table)# Analyzing categorical demos # Subset your data frame to include only the demographic columns <- gjg[, c ("Gender_TEXT" , "Race_Ethnicity_TEXT" , "Income_TEXT" , "Education_TEXT" , "Political_party_TEXT" )]# Loop through each demographic column and calculate frequency counts <- list ()for (col in names (demo_gjg)) {<- as.data.frame (table (demo_gjg[[col]]))$ Percent <- round (freq_table$ Freq / sum (freq_table$ Freq) * 100 , 2 )<- freq_table# Print the frequency tables for (i in seq_along (freq_tables)) {if (! is.null (freq_tables[[i]])) {cat (" \n Table of frequencies for" , names (freq_tables)[i], ": \n " )print (freq_tables[[i]])``` # Correlation ```{r fig.dim = c(12, 10)} <- c ("reputation_signal_just" , "reputation_signal_gen" , "norm_signal_just" , "norm_signal_gen" , "approval_just" , "approval_gen" , "moral_just" , "moral_gen" , "genuine_just" , "genuine_gen" , "deliberate_just" , "deliberate_gen" , "spontaneous_just" , "spontaneous_gen" , "emotion_just" , "emotion_gen" , "logic_just" , "logic_gen" , "quick_just" , "quick_gen" , "slow_just" , "slow_gen" , "warm_just" , "warm_gen" , "good_natured_just" , "good_natured_gen" , "tolerant_just" , "tolerant_gen" , "sincere_just" , "sincere_gen" , "competent_just" , "competent_gen" , "confident_just" , "confident_gen" , "independent_just" , "independent_gen" , "competitive_just" , "competitive_gen" , "intelligent_just" , "intelligent_gen" , "politics_just" , "politics_gen" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )<- lapply (gjg[cols_to_convert], as.numeric)<- gjg[c ("reputation_signal_just" , "reputation_signal_gen" , "norm_signal_just" , "norm_signal_gen" , "approval_just" , "approval_gen" , "moral_just" , "moral_gen" , "genuine_just" , "genuine_gen" , "deliberate_just" , "deliberate_gen" , "spontaneous_just" , "spontaneous_gen" , "emotion_just" , "emotion_gen" , "logic_just" , "logic_gen" , "quick_just" , "quick_gen" , "slow_just" , "slow_gen" , "warm_just" , "warm_gen" , "good_natured_just" , "good_natured_gen" , "tolerant_just" , "tolerant_gen" , "sincere_just" , "sincere_gen" , "competent_just" , "competent_gen" , "confident_just" , "confident_gen" , "independent_just" , "independent_gen" , "competitive_just" , "competitive_gen" , "intelligent_just" , "intelligent_gen" , "politics_just" , "politics_gen" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )]# Compute pairwise correlations <- cor (DVs, use = "pairwise.complete.obs" )# Plot the correlation matrix corrplot (corr_DVs, is.corr = TRUE , type = "lower" , lower = "circle" , tl.cex = 0.7 , insig = "label_sig" , diag = TRUE )# New columns for composite of warmth and competence, moral goodness <- gjg %>% mutate (warmth_just= (warm_just+ good_natured_just+ tolerant_just+ sincere_just)/ 4 )%>% mutate (warmth_gen= (warm_gen+ good_natured_gen+ tolerant_gen+ sincere_gen)/ 4 )%>% mutate (competence_just= (competent_just+ confident_just+ independent_just+ competitive_just+ / 5 )%>% mutate (competence_gen= (competent_gen+ confident_gen+ independent_gen+ competitive_gen+ / 5 ) %>% mutate (moralGoodness_just= (approval_just+ moral_just+ genuine_just)/ 3 )%>% mutate (moralGoodness_gen= (approval_gen+ moral_gen+ genuine_gen)/ 3 ) %>% mutate (decisionSpeed_just= (quick_just+ spontaneous_just)/ 2 )%>% mutate (decisionSpeed_gen= (quick_gen+ spontaneous_gen)/ 2 )# DVs 2 <- gjg[c ("reputation_signal_just" , "reputation_signal_gen" , "norm_signal_just" , "norm_signal_gen" , "approval_just" , "approval_gen" , "moral_just" , "moral_gen" , "genuine_just" , "genuine_gen" , "deliberate_just" , "deliberate_gen" , "spontaneous_just" , "spontaneous_gen" , "emotion_just" , "emotion_gen" , "logic_just" , "logic_gen" , "quick_just" , "quick_gen" , "slow_just" , "slow_gen" , "warmth_just" , "warmth_gen" , "competence_just" , "competence_gen" , "moralGoodness_just" , "moralGoodness_gen" , "decisionSpeed_just" , "decisionSpeed_gen" , "politics_just" , "politics_gen" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )]# Compute pairwise correlations <- cor (DVs_2, use = "pairwise.complete.obs" )# Plot the correlation matrix corrplot (corr_DVs_2, is.corr = TRUE , type = "lower" , lower = "circle" , tl.cex = 0.7 , insig = "label_sig" , diag = TRUE )# DVs just <- gjg[c ("reputation_signal_just" , "norm_signal_just" , "approval_just" , "moral_just" , "genuine_just" , "deliberate_just" , "spontaneous_just" , "emotion_just" , "logic_just" , "quick_just" , "slow_just" , "warmth_just" , "competence_just" , "politics_just" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )]# Compute pairwise correlations <- cor (DVs_just, use = "pairwise.complete.obs" )# Plot the correlation matrix corrplot (corr_DVs_just, is.corr = TRUE , type = "lower" , lower = "circle" , tl.cex = 0.7 , insig = "label_sig" , diag = TRUE )# DVs gen <- gjg[c ("reputation_signal_gen" , "norm_signal_gen" , "approval_gen" , "moral_gen" , "genuine_gen" , "deliberate_gen" , "spontaneous_gen" , "emotion_gen" , "logic_gen" , "quick_gen" , "slow_gen" , "warmth_gen" , "competence_gen" , "politics_gen" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )]# Compute pairwise correlations <- cor (DVs_gen, use = "pairwise.complete.obs" )# Plot the correlation matrix corrplot (corr_DVs_gen, is.corr = TRUE , type = "lower" , lower = "circle" , tl.cex = 0.7 , insig = "label_sig" , diag = TRUE )# DVs collapsed # renaming DVs with underscores in name <- gjg %>% :: rename ("reputationSignal_just" = "reputation_signal_just" ,"reputationSignal_gen" = "reputation_signal_gen" ,"normSignal_just" = "norm_signal_just" , "normSignal_gen" = "norm_signal_gen" ,"goodNatured_gen" = "good_natured_gen" ,"goodNatured_just" = "good_natured_just" )<- gjg %>% gather (DV, resp, "reputationSignal_just" ,"reputationSignal_gen" ,"normSignal_just" ,"normSignal_gen" ,"approval_just" ,"approval_gen" ,"moral_just" ,"moral_gen" ,"genuine_just" ,"genuine_gen" ,"deliberate_just" ,"deliberate_gen" ,"spontaneous_just" ,"spontaneous_gen" ,"emotion_just" ,"emotion_gen" ,"logic_just" ,"logic_gen" ,"quick_just" ,"quick_gen" ,"slow_just" ,"slow_gen" ,"warm_just" ,"warm_gen" ,"goodNatured_just" ,"goodNatured_gen" ,"tolerant_just" ,"tolerant_gen" ,"sincere_just" ,"sincere_gen" ,"competent_just" ,"competent_gen" ,"confident_just" ,"confident_gen" , "independent_just" ,"independent_gen" ,"competitive_just" ,"competitive_gen" ,"intelligent_just" ,"intelligent_gen" ,"warmth_just" ,"warmth_gen" ,"competence_just" ,"competence_gen" , "moralGoodness_just" , "moralGoodness_gen" , "decisionSpeed_just" , "decisionSpeed_gen" , "politics_just" , "politics_gen" )<- gjg_long %>% separate (DV, into= c ("DV" , "motive" ), sep= "_" )<- spread (gjg_long, DV, resp)<- gjg_long[c ("reputationSignal" ,"normSignal" , "approval" , "moral" , "genuine" , "deliberate" , "spontaneous" , "emotion" , "logic" , "quick" , "slow" , "warm" , "goodNatured" , "tolerant" , "sincere" , "competent" , "confident" , "independent" , "competitive" , "intelligent" , "warmth" , "competence" ,"politics" , "Age" , "SES" , "Political_overall" , "Political_social" , "Political_economic" )]# Compute pairwise correlations <- cor (DVs_col, use = "pairwise.complete.obs" )# Plot the correlation matrix corrplot (corr_DVs_col, is.corr = TRUE , type = "lower" , lower = "circle" , tl.cex = 0.7 , insig = "label_sig" , diag = TRUE )``` # EFA ```{r, include=FALSE} <- gjg_long[c ("reputationSignal" ,"normSignal" , "approval" , "moral" , "genuine" , "deliberate" , "spontaneous" , "emotion" , "logic" , "quick" , "slow" , "warm" , "goodNatured" , "tolerant" , "sincere" , "competent" , "confident" , "independent" , "competitive" , "intelligent" )]``` ## Evaluating the correlation matrix ```{r} <- cor (factorDVs, use = "complete.obs" )# Assuming you have a correlation matrix named "correlation_matrix" <- 0.8 <- which (factorCor > correlation_threshold & upper.tri (factorCor, diag = FALSE ), arr.ind = TRUE )# Extract the row and column indices of high correlations <- high_correlations[, 1 ]<- high_correlations[, 2 ]# Extract the correlations greater than 0.8 <- factorCor[high_correlations]# Print the results <- data.frame (row_indices, column_indices, high_correlation_values)print (high_correlation_results)# removing one from all pairs of overcorrelated variables # overcorrelated <- c("") # efaDVs <- factorDVs[ ,!(names(factorDVs) %in% overcorrelated)] <- factorDVs``` ## Determining number of factors * Eigenvalues suggests 5 factors* Scree plot suggests 2 or 5 factors* Parallel Analysis suggests 5```{r} ## evaluating data = cor (efaDVs, use= "complete.obs" )KMO (efaDVs)cortest.bartlett (efaDVs) #tests correlations between variables ## <- eigen (corr_DVs) #gets eigenvalues (variance explained by each component) $ valuesscree (factorDVs, pc = FALSE ) #number of factors until plot levels off fa.parallel (factorDVs, fa= "fa" ) #checks eigenvalues of factors against eigenvalues of identity (no correlation) matrix <- na.omit (DVs)``` ## 5 Factors ```{r} <- factanal (na.omit (efaDVs), factors= 5 , rotation= "promax" , scores = "regression" )print (fit, digits = 2 , cutoff = .3 , sort = TRUE )<- fit$ loadingsfa.diagram (loads)## Cronbach's Alpha <- efaDVs[ , c ("approval" , "moral" , "genuine" , "sincere" , "deliberate" , "emotion" , "logic" )]<- efaDVs[ , c ("competent" , "confident" , "independent" , "intelligent" )]<- efaDVs[ , c ("warm" , "goodNatured" , "tolerant" )]<- efaDVs[ , c ("quick" , "spontaneous" )]<- efaDVs[ , c ("reputationSignal" , "normSignal" , "competitive" )]alpha (f1)alpha (f2)alpha (f3)alpha (f4)alpha (f5)``` # CFA ```{r, include=FALSE} <- ' moralGoodness =~ approval + moral + genuine competence =~ competent + confident + independent + intelligent + competitive + logic warmth =~ warm + goodNatured + tolerant + sincere decisionSpeed =~ quick + spontaneous signaling =~ reputationSignal + normSignal ' <- cfa (model, data = factorDVs)summary (fit, fit.measures= TRUE )``` # Factor 1: Warmth * Significant```{r fig.dim=c(12,10)} <- function (dv, iv, Title, x_axis_labs, y_label, sample_size) {<- ggviolin (gjg_long, x = dv, y = iv, color = dv,alpha = 0.1 , fill = dv, xlab = "Motive" ,trim = TRUE , ylab = y_label) + stat_summary (fun.data = "mean_cl_normal" , geom = "crossbar" , fatten = 1 ) + scale_y_continuous (breaks = c (1 : 5 )) + labs (title = paste0 (Title, " (n = " , sample_size, ")" )) + scale_x_discrete (labels = x_axis_labs) + theme (panel.background = element_rect (fill = "transparent" ), legend.position = "right" , ## Consider “gray97” for fill plot.title = element_text (face = "bold" , hjust = 0.5 , size = 16 ), plot.subtitle = element_text (hjust = 0.5 ),panel.grid.major.y = element_line (color= 'grey75' ), axis.text.x = element_text (face = "plain" , size = 13 , color = "black" ),axis.text.y = element_text (face = "plain" , size = 13 , color = "black" ),axis.title.y = element_text (face = "plain" , size = 13 , color = "black" , margin = margin (t = 0 , r = 10 , b = 0 , l = 0 )), ## lower X axis title panel.border = element_rect (color = "black" , fill = NA , size = 1 ))ggpar (part1, legend = "none" )$ motive <- as.factor (gjg_long$ motive)plot_cooker ("motive" , "warmth" , "Warmth" , c ("Generosity" , "Justice" )," " , 300 )``` ## Inferential Tests ```{r} <- lmer (warmth ~ motive + (1 | PID), data = gjg_long)summary (mod_warmth)<- lmer (warmth ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_warmth_pol)``` # Factor 2: Competence * Significant```{r fig.dim=c(12,10)} plot_cooker ("motive" , "competence" , "Competence" , c ("Generosity" , "Justice" )," " , 300 )``` ## Inferential Tests ```{r} <- lmer (competence ~ motive + (1 | PID), data = gjg_long)summary (mod_comp)<- lmer (competence ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_comp_pol)``` # Factor 3: Moral Goodness ```{r fig.dim=c(12,10)} plot_cooker ("motive" , "moralGoodness" , "Moral Goodness" , c ("Generosity" , "Justice" )," " , 300 )``` ## Inferential Tests ```{r} <- lmer (moralGoodness ~ motive + (1 | PID), data = gjg_long)summary (mod_moral)<- lmer (moralGoodness ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_moral_pol)``` # Factor 4: Decision Speed * Significant main effect* Interaction w/ participants' pol leaning -- conservatives think generous donor decision time is significantly longer```{r fig.dim=c(12,10)} plot_cooker ("motive" , "decisionSpeed" , "decisionSpeed" , c ("Generosity" , "Justice" )," " , 300 )<- function (data, iv, dv) {ggplot (data, aes (x = {{iv}}, y = {{dv}}, color = motive)) + stat_summary (fun.data = "mean_cl_normal" , geom = "line" ) + geom_point (position = position_jitter (width = 0.1 , height = 0.1 ), alpha = 0.5 ) + labs (x = "Political Leaning" , color = "Motive" )pol_line (gjg_long, Political_overall, decisionSpeed)``` ## Inferential Tests ```{r} <- lmer (decisionSpeed ~ motive + (1 | PID), data = gjg_long)summary (mod_speed)<- lmer (decisionSpeed ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_speed_pol)``` # Factor 5: Signaling * Significant Norm Signaling* Marginal Reputation Signaling```{r fig.dim=c(12,10)} plot_cooker ("motive" , "reputationSignal" , "Reputation Signaling" , c ("Generosity" , "Justice" )," " , 300 )plot_cooker ("motive" , "normSignal" , "Norm Signaling" , c ("Generosity" , "Justice" )," " , 300 )``` ## Inferential Tests ```{r} <- lmer (reputationSignal ~ motive + (1 | PID), data = gjg_long)summary (mod_rep)<- lmer (reputationSignal ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_rep_pol)<- lmer (normSignal ~ motive + (1 | PID), data = gjg_long)summary (mod_norm)<- lmer (normSignal ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_norm_pol)``` # Factor 6: Emotion ```{r fig.dim=c(12,10)} plot_cooker ("motive" , "emotion" , "Emotion" , c ("Generosity" , "Justice" )," " , 300 )``` ## Inferential Tests ```{r} <- lmer (emotion ~ motive + (1 | PID), data = gjg_long)summary (mod_emotion)<- lmer (emotion ~ motive* Political_overall + (1 | PID), data = gjg_long)summary (mod_emotion_pol)``` # Factors by Text Order ## Factor 1: Warmth ```{r fig.dim=c(12,10)} <- gjg_long %>% mutate (motive_long = ifelse (motive == "just" , "justice" ,ifelse (motive == "gen" , "generosity" , NA ))# # gjg_long %>% mutate( # type = case_when( # height > 200 | mass > 200 ~ "large", # species == "Droid" ~ "robot", # .default = "other" # ) # ) <- function (dv, iv, facet, Title, x_axis_labs, y_label, sample_size, coln, rown) {<- ggviolin (gjg_long, x = dv, y = iv, color = dv,alpha = 0.1 , fill = dv, xlab = "Motive" ,trim = TRUE , ylab = y_label) + stat_summary (fun.data = "mean_cl_normal" , geom = "crossbar" , fatten = 1 ) + scale_y_continuous (breaks = c (1 : 7 )) + labs (title = paste0 (Title, " (n = " , sample_size, ")" )) + theme (panel.background = element_rect (fill = "transparent" ), legend.position = "right" , ## Consider “gray97” for fill plot.title = element_text (face = "bold" , hjust = 0.5 , size = 16 ), plot.subtitle = element_text (hjust = 0.5 ),panel.grid.major.y = element_line (color= 'grey75' ), axis.text.x = element_text (face = "plain" , size = 13 , color = "black" ),axis.text.y = element_text (face = "plain" , size = 13 , color = "black" ),axis.title.y = element_text (face = "plain" , size = 13 , color = "black" , margin = margin (t = 0 , r = 10 , b = 0 , l = 0 )), ## lower X axis title panel.border = element_rect (color = "black" , fill = NA , size = 1 )) + scale_color_discrete (name = "Condition" ) + facet_wrap (~ vigOrder, ncol = coln, nrow = rown, scales = "free" , as.table = TRUE )ggpar (part1, legend = "none" )lizy_cooker ("motive" , "warmth" , "vigOrder" , "Warmth" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )``` ### Inferential Tests ```{r} # mod_warmth_vigOrder <- lmer(warmth ~ motive*first_text + (1 | PID), data = gjg_long) # summary(mod_warmth_vigOrder) # view(gjg_long) ``` ## Factor 2: Competence ```{r fig.dim=c(12,10)} lizy_cooker ("motive" , "competence" , "vigOrder" , "Competence" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (competence ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_competence_vigOrder)``` ## Factor 3: Moral Goodness ```{r fig.dim=c(12,10)} lizy_cooker ("motive" , "moralGoodness" , "vigOrder" , "Moral Goodness" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (moralGoodness ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_moral_vigOrder)``` ## Factor 4: Decision Speed ```{r fig.dim=c(12,10)} lizy_cooker ("motive" , "decisionSpeed" , "vigOrder" , "Decision Speed" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (decisionSpeed ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_speed_vigOrder)``` ## Factor 5: Signaling ```{r fig.dim=c(12,10)} lizy_cooker ("motive" , "reputationSignal" , "vigOrder" , "Reputation Signaling" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (reputationSignal ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_rep_vigOrder)lizy_cooker ("motive" , "normSignal" , "vigOrder" , "Norm Signaling" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (normSignal ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_norm_vigOrder)``` ## Factor 6: Emotion ```{r fig.dim=c(12,10)} lizy_cooker ("motive" , "emotion" , "Emotion" , "Reputation Signaling" , c ("Generosity" , "Justice" )," " , 300 , 2 , 2 )<- lmer (emotion ~ motive* vigOrder + (1 | PID), data = gjg_long)summary (mod_emotion_vigOrder)``` 
