--- title: "Generosity vs Justice V5" author: 'Marcus + Lizy + Nadia' date: "7/24/2023" 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- Data collected July 19, 2023- N = 200# 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" )#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 in data files <- read.csv ("/Users/mtrenfield17/Desktop/Research/Boston College Research/Morality Lab Research/Generosity Vs Justice Project/just_vs_gen_v5/Gen_just_V5 (1).csv" )``` ## Reshaping data ```{r} # changing appropriate DVs to numeric <- c ("age" , "income" , "ses" , "political_overall" , "political_social" , "political_economic" , "attn_self" , "pid" )<- lapply (gjg[cols_to_convert], as.numeric)$ reputation_signal_just <- NA $ reputation_signal_gen <- NA $ reputation_signal_just <- ifelse (gjg$ vigOrder == "justiceFirst" , gjg$ reputation_signal_1, gjg$ reputation_signal_2)$ reputation_signal_gen <- ifelse (gjg$ vigOrder == "justiceFirst" , gjg$ reputation_signal_2, gjg$ reputation_signal_1)<- function (data, column_just, column_gen, vigOrder_col) {# Create new columns for "_just" and "_gen" <- ifelse (data[[vigOrder_col]] == "justiceFirst" ,paste0 (column_just, "_1" )]], data[[paste0 (column_just, "_2" )]])<- ifelse (data[[vigOrder_col]] == "justiceFirst" ,paste0 (column_just, "_2" )]], data[[paste0 (column_just, "_1" )]])# Replace missing values with NA is.na (data[[column_just]])] <- NA is.na (data[[column_gen]])] <- NA # Remove "_1" and "_2" from the column names names (data[[column_just]]) <- gsub ("_1$" , "_just" , names (data[[column_just]]))names (data[[column_gen]]) <- gsub ("_2$" , "_gen" , names (data[[column_gen]]))# Return the modified data frame return (data)<- convert_vigOrder_columns (gjg, "norm_signal" , "norm_signal_gen" , "vigOrder" )names (gjg)[names (gjg) == "norm_signal" ] <- "norm_signal_just" <- convert_vigOrder_columns (gjg, "approval" , "approval_gen" , "vigOrder" )names (gjg)[names (gjg) == "approval" ] <- "approval_just" <- convert_vigOrder_columns (gjg, "moral" , "moral_gen" , "vigOrder" )names (gjg)[names (gjg) == "moral" ] <- "moral_just" <- convert_vigOrder_columns (gjg, "genuine" , "genuine_gen" , "vigOrder" )names (gjg)[names (gjg) == "genuine" ] <- "genuine_just" <- convert_vigOrder_columns (gjg, "deliberate" , "deliberate_gen" , "vigOrder" )names (gjg)[names (gjg) == "deliberate" ] <- "deliberate_just" <- convert_vigOrder_columns (gjg, "spontaneous" , "spontaneous_gen" , "vigOrder" )names (gjg)[names (gjg) == "spontaneous" ] <- "spontaneous_just" <- convert_vigOrder_columns (gjg, "emotion" , "emotion_gen" , "vigOrder" )names (gjg)[names (gjg) == "emotion" ] <- "emotion_just" <- convert_vigOrder_columns (gjg, "logic" , "logic_gen" , "vigOrder" )names (gjg)[names (gjg) == "logic" ] <- "logic_just" <- convert_vigOrder_columns (gjg, "quick" , "quick_gen" , "vigOrder" )names (gjg)[names (gjg) == "quick" ] <- "quick_just" <- convert_vigOrder_columns (gjg, "slow" , "slow_gen" , "vigOrder" )names (gjg)[names (gjg) == "slow" ] <- "slow_just" <- convert_vigOrder_columns (gjg, "warm" , "warm_gen" , "vigOrder" )names (gjg)[names (gjg) == "warm" ] <- "warm_just" <- convert_vigOrder_columns (gjg, "good.natured" , "good.natured_gen" , "vigOrder" )names (gjg)[names (gjg) == "good.natured" ] <- "good.natured_just" <- convert_vigOrder_columns (gjg, "tolerant" , "tolerant_gen" , "vigOrder" )names (gjg)[names (gjg) == "tolerant" ] <- "tolerant_just" <- convert_vigOrder_columns (gjg, "sincere" , "sincere_gen" , "vigOrder" )names (gjg)[names (gjg) == "sincere" ] <- "sincere_just" <- convert_vigOrder_columns (gjg, "competent" , "competent_gen" , "vigOrder" )names (gjg)[names (gjg) == "competent" ] <- "competent_just" <- convert_vigOrder_columns (gjg, "confident" , "confident_gen" , "vigOrder" )names (gjg)[names (gjg) == "confident" ] <- "confident_just" <- convert_vigOrder_columns (gjg, "independent" , "independent_gen" , "vigOrder" )names (gjg)[names (gjg) == "independent" ] <- "independent_just" <- convert_vigOrder_columns (gjg, "competitive" , "competitive_gen" , "vigOrder" )names (gjg)[names (gjg) == "competitive" ] <- "competitive_just" <- convert_vigOrder_columns (gjg, "intelligent" , "intelligent_gen" , "vigOrder" )names (gjg)[names (gjg) == "intelligent" ] <- "intelligent_just" <- convert_vigOrder_columns (gjg, "politics" , "politics_gen" , "vigOrder" )names (gjg)[names (gjg) == "politics" ] <- "politics_just" <- convert_vigOrder_columns (gjg, "diff_org_polit" , "diff_org_polit_gen" , "vigOrder" )names (gjg)[names (gjg) == "diff_org_polit" ] <- "diff_org_polit_just" ``` # Attention check ```{r,message=F, results='hide', warning=FALSE} # filtering out failures/nonresponses to attention checks length (gjg$ pid)<- gjg %>% filter (! is.na (attn_bucket), attn_self> 2 )length (gjg$ pid)``` # Demographics ```{r,message=F, results='hide', warning=FALSE} # Subset your data frame to include only the demographic columns <- gjg[,c ("age" , "income" , "ses" , "political_social" , "political_economic" )]# descriptive stats for numeric demos describeBy (numeric_demos)$ gender <- as.factor (as.character (gjg$ gender))levels (gjg$ gender)table (gjg$ gender)$ gender <- ifelse (gjg$ gender == 1 , "Man" ,ifelse (gjg$ gender == 2 , "Woman" ,ifelse (gjg$ gender == 3 , "Nonbinary person or Other" ,ifelse (gjg$ gender == 4 , NA , NA ))))prop.table (table (gjg$ gender, useNA= "always" ))$ race<- as.factor (gjg$ race)levels (gjg$ race)table (gjg$ race)$ race <- ifelse (gjg$ race == 1 , "American Indian or Alaskan Native" ,ifelse (gjg$ race == 2 , "Asian" ,ifelse (gjg$ race == 3 , "Black" ,ifelse (gjg$ race == 100 , "Middle Eastern" ,ifelse (gjg$ race == 4 , "Native Hawaiian or Other Pacific Islander" ,ifelse (gjg$ race == 5 , "White" ,ifelse (gjg$ race == 6 , "Latinx" ,ifelse (gjg$ race == 7 | gjg$ race == 99 , "Unlisted" ,ifelse (gjg$ race == "" , "Unlisted" , ifelse (grepl ("," , gjg$ race), "Multiracial" , NA ))))))))))$ race<- as.factor (gjg$ race)levels (gjg$ race)table (gjg$ race)prop.table (table (gjg$ race))$ income <- as.factor (as.character (gjg$ income))levels (gjg$ income)table (gjg$ income)$ income <- ifelse (gjg$ income == 1 , "<$10,000" ,ifelse (gjg$ income == 2 , "$10,000-$19,999" ,ifelse (gjg$ income == 3 , "$20,000-$29,999" ,ifelse (gjg$ income == 4 , "$30,000-$39,999" ,ifelse (gjg$ income == 5 , "$40,000-$49,999" ,ifelse (gjg$ income == 6 , "$50,000-$74,999" ,ifelse (gjg$ income == 7 , "$75,000-$99,999" ,ifelse (gjg$ income == 8 , "$100,000-$149,999" , ifelse (gjg$ income == 9 , ">$150,000" , NA )))))))))prop.table (table (gjg$ income, useNA= "always" ))$ education <- as.factor (as.character (gjg$ education))levels (gjg$ education)table (gjg$ education)$ education <- ifelse (gjg$ education == 1 , "Less than a high school diploma" ,ifelse (gjg$ education == 2 , "High school degree or equivalent" ,ifelse (gjg$ education == 3 , "Some college, no degree" ,ifelse (gjg$ education == 4 , "Associate Degree" ,ifelse (gjg$ education == 5 , "Bachelor's Degree" ,ifelse (gjg$ education == 6 , "Postgraduate Degree" , NA ))))))prop.table (table (gjg$ education, useNA= "always" ))$ political_party <- as.factor (as.character (gjg$ political_party))levels (gjg$ political_party)table (gjg$ political_party)$ political_party <- ifelse (gjg$ political_party == 1 , "Republican" ,ifelse (gjg$ political_party == 2 , "Democrat" ,ifelse (gjg$ political_party == 3 , "Independent" ,ifelse (gjg$ political_party == 4 , "Something else" , NA ))))prop.table (table (gjg$ political_party, useNA= "always" ))# Create composite political measures $ polit_self <- ifelse (gjg$ political_overall %in% c (1 , 2 , 3 ), "Democrat" ,ifelse (gjg$ political_overall == 4 , "Moderate" ,ifelse (gjg$ political_overall %in% c (5 , 6 , 7 ), "Republican" , NA )))``` # 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" , "same_org_polit" , "diff_org_polit_just" , "diff_org_polit_gen" , "age" , "ses" , "political_overall" , "political_social" , "political_economic" )names (gjg)<- 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" , "same_org_polit" , "diff_org_polit_just" , "diff_org_polit_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 <- 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 )# 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" , "politics_just" , "politics_gen" , "same_org_polit" , "diff_org_polit_just" , "diff_org_polit_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" , "warm_just" , "good.natured_just" , "tolerant_just" , "sincere_just" , "competent_just" , "confident_just" , "independent_just" , "competitive_just" , "intelligent_just" , "warmth_just" , "competence_just" ,"politics_just" , "diff_org_polit_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" , "warm_gen" , "good.natured_gen" , "tolerant_gen" , "sincere_gen" , "competent_gen" , "confident_gen" , "independent_gen" , "competitive_gen" , "intelligent_gen" , "warmth_gen" , "competence_gen" ,"politics_gen" , "diff_org_polit_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" ,"diffOrgPolit_just" = "diff_org_polit_just" ,"diffOrgPolit_gen" = "diff_org_polit_gen" )<- 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" ,"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" ,"warmth_just" ,"warmth_gen" ,"competence_just" ,"competence_gen" , "politics_just" , "politics_gen" , "diffOrgPolit_just" , "diffOrgPolit_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" , "good.natured" , "tolerant" , "sincere" , "competent" , "confident" , "independent" , "competitive" , "intelligent" , "warmth" , "competence" ,"politics" , "diffOrgPolit" , "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 )``` # Changing to long format ```{r} # splitting data into gen and just motives names (gjg)[names (gjg) == "reputation_signal_1" ] <- "reputationSignal1" names (gjg)[names (gjg) == "reputation_signal_2" ] <- "reputationSignal2" names (gjg)[names (gjg) == "norm_signal_1" ] <- "normSignal1" names (gjg)[names (gjg) == "norm_signal_2" ] <- "normSignal2" names (gjg)[names (gjg) == "approval_1" ] <- "approval1" names (gjg)[names (gjg) == "approval_2" ] <- "approval2" names (gjg)[names (gjg) == "moral_1" ] <- "moral1" names (gjg)[names (gjg) == "moral_2" ] <- "moral2" names (gjg)[names (gjg) == "genuine_1" ] <- "genuine1" names (gjg)[names (gjg) == "genuine_2" ] <- "genuine2" names (gjg)[names (gjg) == "deliberate_1" ] <- "deliberate1" names (gjg)[names (gjg) == "deliberate_2" ] <- "deliberate2" names (gjg)[names (gjg) == "spontaneous_1" ] <- "spontaneous1" names (gjg)[names (gjg) == "spontaneous_2" ] <- "spontaneous2" names (gjg)[names (gjg) == "emotion_1" ] <- "emotion1" names (gjg)[names (gjg) == "emotion_2" ] <- "emotion2" names (gjg)[names (gjg) == "logic_1" ] <- "logic1" names (gjg)[names (gjg) == "logic_2" ] <- "logic2" names (gjg)[names (gjg) == "quick_1" ] <- "quick1" names (gjg)[names (gjg) == "quick_2" ] <- "quick2" names (gjg)[names (gjg) == "slow_1" ] <- "slow1" names (gjg)[names (gjg) == "slow_2" ] <- "slow2" names (gjg)[names (gjg) == "warm_1" ] <- "warm1" names (gjg)[names (gjg) == "warm_2" ] <- "warm2" names (gjg)[names (gjg) == "good.natured_1" ] <- "good.natured1" names (gjg)[names (gjg) == "good.natured_2" ] <- "good.natured2" names (gjg)[names (gjg) == "tolerant_1" ] <- "tolerant1" names (gjg)[names (gjg) == "tolerant_2" ] <- "tolerant2" names (gjg)[names (gjg) == "sincere_1" ] <- "sincere1" names (gjg)[names (gjg) == "sincere_2" ] <- "sincere2" names (gjg)[names (gjg) == "competent_1" ] <- "competent1" names (gjg)[names (gjg) == "competent_2" ] <- "competent2" names (gjg)[names (gjg) == "confident_1" ] <- "confident1" names (gjg)[names (gjg) == "confident_2" ] <- "confident2" names (gjg)[names (gjg) == "independent_1" ] <- "independent1" names (gjg)[names (gjg) == "independent_2" ] <- "independent2" names (gjg)[names (gjg) == "competitive_1" ] <- "competitive1" names (gjg)[names (gjg) == "competitive_2" ] <- "competitive2" names (gjg)[names (gjg) == "intelligent_1" ] <- "intelligent1" names (gjg)[names (gjg) == "intelligent_2" ] <- "intelligent2" names (gjg)[names (gjg) == "politics_1" ] <- "politics1" names (gjg)[names (gjg) == "politics_2" ] <- "politics2" names (gjg)[names (gjg) == "same_org" ] <- "sameOrg" names (gjg)[names (gjg) == "same_org_polit" ] <- "sameOrgPolit" names (gjg)[names (gjg) == "diff_org_polit_1" ] <- "diffOrgPolit1" names (gjg)[names (gjg) == "diff_org_polit_2" ] <- "diffOrgPolit2" <- 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" ,"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" ,"warmth_just" ,"warmth_gen" ,"competence_just" ,"competence_gen" , "politics_just" , "politics_gen" , "diffOrgPolit_just" , "diffOrgPolit_gen" )<- gjg_long %>% separate (DV, into= c ("DV" , "motive" ), sep= "_" )<- spread (gjg_long, DV, resp)``` # Linear Mixed Effects ```{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" )# mixed effects model $ motive <- as.factor (gjg_long$ motive)<- lmer (reputationSignal ~ motive + (1 | pid), data = gjg_long)summary (mod_reputationSignaling)<- list (plot_cooker ("motive" , "reputationSignal" , "Reputation Signal" , c ("generosity" ,"justice" )," " , 199 ),plot_cooker ("motive" , "normSignal" , "Norm Signal" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "approval" , "Approval" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "moral" , "Moral" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "genuine" , "Genuine" , c ("generosity" , "justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 2 , nrow = 3 )<- ggdraw () + draw_label ("Perception DVs" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (reputationSignal ~ motive + (1 | pid), data = gjg_long)summary (mod_reputationSignaling)<- lmer (normSignal ~ motive + (1 | pid), data = gjg_long)summary (mod_normSignaling)<- lmer (approval ~ motive + (1 | pid), data = gjg_long)summary (mod_approval)<- lmer (moral ~ motive + (1 | pid), data = gjg_long)summary (mod_moral)<- lmer (genuine ~ motive + (1 | pid), data = gjg_long)summary (mod_genuine)``` ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "deliberate" , "Deliberate" , c ("generosity" ,"justice" )," " , 199 ),plot_cooker ("motive" , "spontaneous" , "Spontaneous" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "emotion" , "Emotion" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "logic" , "Logic" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "quick" , "Quick" , c ("generosity" , "justice" ),"" , 199 ),plot_cooker ("motive" ,"slow" ,"Slow" ,c ("generosity" ,"justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 2 , nrow = 3 )<- ggdraw () + draw_label ("Deliberation DVs" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (deliberate ~ motive + (1 | pid), data = gjg_long)summary (mod_deliberate)<- lmer (spontaneous ~ motive + (1 | pid), data = gjg_long)summary (mod_spontaneous)<- lmer (emotion ~ motive + (1 | pid), data = gjg_long)summary (mod_emotion)<- lmer (logic ~ motive + (1 | pid), data = gjg_long)summary (mod_logic)<- lmer (quick ~ motive + (1 | pid), data = gjg_long)summary (mod_quick)<- lmer (slow ~ motive + (1 | pid), data = gjg_long)summary (mod_slow)``` ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "warmth" , "Warmth" , c ("generosity" ,"justice" )," " , 199 ),plot_cooker ("motive" , "competence" , "Competence" , c ("generosity" , "justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 2 , nrow = 1 )<- ggdraw () + draw_label ("Warmth/Competence DVs" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (warmth ~ motive + (1 | pid), data = gjg_long)summary (mod_warmth)<- lmer (competence ~ motive + (1 | pid), data = gjg_long)summary (mod_competence)``` ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "warm" , "Warm" , c ("generosity" ,"justice" )," " , 199 ),plot_cooker ("motive" , "good.natured" , "Good-Natured" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "tolerant" , "Tolerant" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "sincere" , "Sincere" , c ("generosity" , "justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 2 , nrow = 2 )<- ggdraw () + draw_label ("Warmth DVs" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (warm ~ motive + (1 | pid), data = gjg_long)summary (mod_warm)<- lmer (good.natured ~ motive + (1 | pid), data = gjg_long)summary (mod_good.natured)<- lmer (tolerant ~ motive + (1 | pid), data = gjg_long)summary (mod_tolerant)<- lmer (sincere ~ motive + (1 | pid), data = gjg_long)summary (mod_sincere)``` ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "competent" , "Competent" , c ("generosity" ,"justice" )," " , 199 ),plot_cooker ("motive" , "confident" , "Confident" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "independent" , "Independent" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "competitive" , "Competitive" , c ("generosity" , "justice" )," " , 199 ),plot_cooker ("motive" , "intelligent" , "Intelligent" , c ("generosity" , "justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 2 , nrow = 3 )<- ggdraw () + draw_label ("Competence DVs" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (competent ~ motive + (1 | pid), data = gjg_long)summary (mod_competent)<- lmer (confident ~ motive + (1 | pid), data = gjg_long)summary (mod_confident)<- lmer (independent ~ motive + (1 | pid), data = gjg_long)summary (mod_independent)<- lmer (competitive ~ motive + (1 | pid), data = gjg_long)summary (mod_competitive)<- lmer (intelligent ~ motive + (1 | pid), data = gjg_long)summary (mod_intelligent)``` ## Political Perceptions ```{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 : 7 )) + 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" )<- list (plot_cooker7 ("motive" , "politics" , "Donor Politics" , c ("generosity" ,"justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Donor Politics" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (politics ~ motive + (1 | pid), data = gjg_long)summary (mod_politics)# effect size = 0.163 <- 0.2412 / sqrt (2.196 )# by own politics <- lmer (politics ~ motive * polit_self + (1 | pid), data = gjg_long)summary (mod_politics2)``` ```{r fig.dim=c(12,10)} <- list (plot_cooker7 ("motive" , "diffOrgPolit" , "Organization Politics" , c ("generosity" ,"justice" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Organization Politics" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (diffOrgPolit ~ motive + (1 | pid), data = gjg_long)summary (mod_orgPolitics)# effect size = 0.245 <- 0.3622 / sqrt (2.18 )# by own politics <- lmer (diffOrgPolit ~ motive * polit_self + (1 | pid), data = gjg_long)summary (mod_orgPolitics2)``` ```{r message=F, results='hide', warning=FALSE} # percent of people who thought they donated to same org prop.table (table (gjg$ sameOrg, useNA= "always" )) # 64% different, 36% same table (gjg$ sameOrgPolit)$ sameOrgPolit <- ifelse (gjg$ sameOrgPolit == 10 , 1 ,ifelse (gjg$ sameOrgPolit == 1 , 2 ,ifelse (gjg$ sameOrgPolit == 2 , 3 ,ifelse (gjg$ sameOrgPolit == 3 , 4 ,ifelse (gjg$ sameOrgPolit == 4 , 5 ,ifelse (gjg$ sameOrgPolit == 5 , 6 , ifelse (gjg$ sameOrgPolit == 6 , 7 , NA )))))))prop.table (table (gjg$ sameOrgPolit))shapiro.test (gjg$ sameOrgPolit) <- wilcox.test (gjg$ sameOrgPolit, mu= 4 )mean (gjg$ sameOrgPolit, na.rm= T)``` # Exploratory Factor Analyses ## Running Factor Analysis for Generosity, 5 factor ```{r message=F, results='hide', warning=FALSE} #deleting unrelated columns <- gjg_long[, ! names (gjg_long) %in% c ("attn_bucket" ,"openFeedback" ,"confusion" ,"attn_self" ,"polit_comp" ,"pid" ,"age" ,"gender" ,"race" ,"income" ,"education" ,"ses" ,"political_party" ,"political_overall" ,"political_social" ,"political_economic" ,"warmth" ,"competence" ,"reputationSignal1" ,"reputationSignal2" ,"normSignal1" ,"normSignal2" ,"approval1" ,"approval2" ,"moral1" ,"moral2" ,"genuine1" ,"genuine2" ,"deliberate1" ,"deliberate2" ,"spontaneous1" ,"spontaneous2" ,"emotion1" ,"emotion2" ,"logic1" ,"logic2" ,"quick1" ,"quick2" ,"slow1" ,"slow2" ,"warm1" ,"warm2" ,"good.natured1" ,"good.natured2" ,"tolerant1" ,"tolerant2" ,"sincere1" ,"sincere2" ,"competent1" ,"competent2" ,"confident1" ,"confident2" , "independent1" ,"independent2" ,"competitive1" ,"competitive2" ,"intelligent1" ,"intelligent2" ,"warmth1" ,"warmth2" ,"competence1" ,"competence2" , "politics1" , "politics2" , "diffOrgPolit1" , "diffOrgPolit2" , "political_party_4_TEXT" , "vigOrder" , "firstSentence" , "secondSentence" , "vigFirstVirtue" , "vigSecondVirtue" , "nameOne" , "nameTwo" , "sameOrg" , "sameOrgPolit" , "diffOrgPolit" , "politics" , "polit_self" )]#generous factor analysis <- subset (gjg_long2, motive != "just" )<- gjg_long_gen[, ! names (gjg_long_gen) %in% c ("motive" )]which (is.na (gjg_long_gen), arr.ind = TRUE )str (gjg_long_gen)<- as.data.frame (gjg_long_gen)<- na.omit (gjg_long_gen)#evaluating correlation matrix write.csv (cor (gjg_long_gen)> 0.8 , file= "Suspect_Correlations.csv" )write.csv (cor (gjg_long_gen), file= "Correlation_Values.csv" )#KMO test KMO (gjg_long_gen)cortest.bartlett (gjg_long_gen)#reject the null <- eigen (cor (gjg_long_gen)) $ valuesscree (gjg_long_gen, pc= FALSE )fa.parallel (gjg_long_gen, fa= "fa" )<- 5 <- factanal (gjg_long_gen, Nfacs, rotation= "promax" )print (fit, digits= 2 , cutoff= 0.3 , sort= TRUE )<- fit$ loadingsfa.diagram (loads)``` ## Running Factor Analysis for Justice, 4 factor ```{r message=F, results='hide', warning=FALSE} <- subset (gjg_long2, motive != "gen" )<- gjg_long_just[, ! names (gjg_long_just) %in% c ("motive" )]which (is.na (gjg_long_just), arr.ind = TRUE )str (gjg_long_just)<- as.data.frame (gjg_long_just)<- na.omit (gjg_long_just)#evaluating correlation matrix write.csv (cor (gjg_long_just)> 0.8 , file= "Suspect_Correlations.csv" )write.csv (cor (gjg_long_just), file= "Correlation_Values.csv" )#KMO test KMO (gjg_long_just)cortest.bartlett (gjg_long_just)#reject the null <- eigen (cor (gjg_long_just)) $ valuesscree (gjg_long_just, pc= FALSE )fa.parallel (gjg_long_just, fa= "fa" )<- 4 <- factanal (gjg_long_just, Nfacs, rotation= "promax" )print (fit, digits= 2 , cutoff= 0.3 , sort= TRUE )<- fit$ loadingsfa.diagram (loads)``` ## Running Factor Analysis Overall, 5 factor ```{r message=F, results='hide', warning=FALSE} #deleting unrelated columns <- gjg_long[, ! names (gjg_long) %in% c ("attn_bucket" ,"gender_text" ,"race_text" ,"income_text" ,"education_text" ,"political_party_text" ,"political_party_4_TEXT" ,"openFeedback" ,"confusion" ,"attn_self" ,"polit_comp" ,"pid" ,"age" ,"gender" ,"race" ,"income" ,"education" ,"ses" ,"political_party" ,"political_social" ,"political_economic" ,"warmth" ,"competence" ,"motive" ,"political_overall" ,"reputationSignal1" ,"reputationSignal2" ,"normSignal1" ,"normSignal2" ,"approval1" ,"approval2" ,"moral1" ,"moral2" ,"genuine1" ,"genuine2" ,"deliberate1" ,"deliberate2" ,"spontaneous1" ,"spontaneous2" ,"emotion1" ,"emotion2" ,"logic1" ,"logic2" ,"quick1" ,"quick2" ,"slow1" ,"slow2" ,"warm1" ,"warm2" ,"good.natured1" ,"good.natured2" ,"tolerant1" ,"tolerant2" ,"sincere1" ,"sincere2" ,"competent1" ,"competent2" ,"confident1" ,"confident2" , "independent1" ,"independent2" ,"competitive1" ,"competitive2" ,"intelligent1" ,"intelligent2" ,"warmth1" ,"warmth2" ,"competence1" ,"competence2" , "politics1" , "politics2" , "diffOrgPolit1" , "diffOrgPolit2" , "political_party_4_TEXT" , "vigOrder" , "firstSentence" , "secondSentence" , "vigFirstVirtue" , "vigSecondVirtue" , "nameOne" , "nameTwo" , "sameOrg" , "sameOrgPolit" , "diffOrgPolit" , "politics" , "polit_self" )]write.csv (cor (gjg_long3)> 0.8 , file= "Suspect_Correlations.csv" )write.csv (cor (gjg_long3), file= "Correlation_Values.csv" )#KMO test KMO (gjg_long3)cortest.bartlett (gjg_long3)#reject the null <- eigen (cor (gjg_long3)) $ valuesscree (gjg_long3, pc= FALSE )fa.parallel (gjg_long3, fa= "fa" )<- 5 <- factanal (gjg_long3, Nfacs, rotation= "promax" )print (fit, digits= 2 , cutoff= 0.3 , sort= TRUE )#diagram <- fit$ loadingsfa.diagram (loads)``` ### Linear Mixed Effects for 5 Factors ```{r message=F, results='hide', warning=FALSE} #new columns for Factor 1-competence (plus logic, minus competitive), Factor 2-moral goodness (approval, moral, genuine, sincere), Factor 3-warmth (minus sincere), Factor 4-decision speed (quick, spontaneous), Factor 5-decision style (slow, competitive, norm signal) <- gjg %>% mutate (competence2_just= (competent_just+ confident_just+ independent_just+ logic_just+ / 5 )%>% mutate (competence2_gen= (competent_gen+ confident_gen+ independent_gen+ logic_gen+ / 5 )%>% mutate (moralGoodness_just= (approval_just+ moral_just+ genuine_just+ sincere_just)/ 4 )%>% mutate (moralGoodness_gen= (approval_gen+ moral_gen+ genuine_gen+ sincere_gen)/ 4 )%>% mutate (warmth2_just= (warm_just+ good.natured_just+ tolerant_just)/ 3 )%>% mutate (warmth2_gen= (warm_gen+ good.natured_gen+ tolerant_gen)/ 3 )%>% mutate (decisionSpeed_just= (quick_just+ spontaneous_just)/ 2 )%>% mutate (decisionSpeed_gen= (quick_gen+ spontaneous_gen)/ 2 )%>% mutate (decisionStyle_just= (slow_just+ competitive_just+ normSignal_just)/ 3 )%>% mutate (decisionStyle_gen= (slow_gen+ competitive_gen+ normSignal_gen)/ 3 )<- 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" ,"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" ,"warmth_just" ,"warmth_gen" ,"competence_just" ,"competence_gen" ,"competence2_just" ,"competence2_gen" ,"moralGoodness_just" ,"moralGoodness_gen" ,"warmth2_just" , "warmth2_gen" ,"decisionSpeed_just" ,"decisionSpeed_gen" ,"decisionStyle_just" ,"decisionStyle_gen" )# splitting data into gen and just motives <- gjg_long %>% separate (DV, into= c ("DV" , "motive" ), sep= "_" )<- spread (gjg_long, DV, resp) ``` #### Mixed Effects Model Competence (Factor 1) ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "competence2" , "Competence" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 1 (Competence)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (competence2 ~ motive + (1 | pid), data = gjg_long)summary (mod_competence2)``` #### Mixed Effects Model Moral Goodness (Factor 2) ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "moralGoodness" , "Moral Goodness" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 2 (Moral Goodness)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (moralGoodness ~ motive + (1 | pid), data = gjg_long)summary (mod_moralGoodness)``` #### Mixed Effects Model Warmth (Factor 3) ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "warmth2" , "Warmth" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 3 (Warmth)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (warmth2 ~ motive + (1 | pid), data = gjg_long)summary (mod_warmth2)``` #### Mixed Effects Model Decision Speed (Factor 4) ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "decisionSpeed" , "Decision Speed" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 4 (Decision Speed)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (decisionSpeed ~ motive + (1 | pid), data = gjg_long)summary (mod_decisionSpeed)``` #### Mixed Effects Model Decision Speed (Factor 5) ```{r fig.dim=c(12,10)} <- list (plot_cooker ("motive" , "decisionStyle" , "Decision Style" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 5 (Decision Style)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (decisionStyle ~ motive + (1 | pid), data = gjg_long)summary (mod_decisionStyle)``` # Confirmatory Factor Analyses ## Running Factor Analysis for V3 Factors, 6 Factor ```{r message=F, results='hide', warning=FALSE} #install.packages("lavaan") library (lavaan)<- ' f1 =~ warm + good.natured + tolerant + sincere f2 =~ logic + independent + intelligent + confident + competent f3 =~ approval + moral + genuine f4 =~ reputationSignal f5 =~ quick + spontaneous f6 =~ emotion ' <- cfa (path, data= gjg_long3)summary (model, fit.measures= TRUE )``` ### Linear Mixed Effects for 6 Factors (from V3) ```{r message=F, results='hide', warning=FALSE} #new column for Factor 3-moral goodness (approval, moral, genuine) <- gjg %>% mutate (moralGoodness2_just= (approval_just+ moral_just+ genuine_just)/ 3 )%>% mutate (moralGoodness2_gen= (approval_gen+ moral_gen+ genuine_gen)/ 3 )<- 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" ,"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" ,"warmth_just" ,"warmth_gen" ,"competence_just" ,"competence_gen" ,"competence2_just" ,"competence2_gen" ,"moralGoodness2_just" ,"moralGoodness2_gen" ,"decisionSpeed_just" ,"decisionSpeed_gen" )# splitting data into gen and just motives <- gjg_long %>% separate (DV, into= c ("DV" , "motive" ), sep= "_" )<- spread (gjg_long, DV, resp) ``` #### Mixed Effects Model Warmth (Factor 1) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "warmth" , "Warmth" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 1 (Warmth)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (warmth ~ motive + (1 | pid), data = gjg_long)summary (mod_warmth)``` #### Mixed Effects Model Competence (Factor 2) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "competence2" , "Competence" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 2 (Competence)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (competence2 ~ motive + (1 | pid), data = gjg_long)summary (mod_competence)``` #### Mixed Effects Model Moral Goodness (Factor 3) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "moralGoodness2" , "Moral Goodness" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 3 (Moral Goodness)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (moralGoodness2 ~ motive + (1 | pid), data = gjg_long)summary (mod_moralGoodness)``` #### Mixed Effects Model Reputation Signaling (Factor 4) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "reputationSignal" , "Reputation Signaling" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 4 (Reputation Signaling)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (reputationSignal ~ motive + (1 | pid), data = gjg_long)summary (mod_reputationSignal)``` #### Mixed Effects Model Decision Speed (Factor 5) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "decisionSpeed" , "Decision Speed" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 5 (Decision Speed)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (decisionSpeed ~ motive + (1 | pid), data = gjg_long)summary (mod_decisionSpeed)``` #### Mixed Effects Model Emotion (Factor 6) ```{r message=F, results='hide', warning=FALSE} <- list (plot_cooker ("motive" , "emotion" , "Emotion" , c ("justice" ,"generosity" )," " , 199 ))<- ggarrange (plotlist = percep_plot_list, ncol = 1 , nrow = 1 )<- ggdraw () + draw_label ("Factor 6 (Emotion)" , fontface = "bold" )plot_grid (overall_percep_title, percep_plot_arranged, ncol = 1 , rel_heights = c (0.1 , 0.9 ))<- lmer (emotion ~ motive + (1 | pid), data = gjg_long)summary (mod_emotion)``` ## Running Factor Analysis for Original Factors, 6 Factor ```{r message=F, results='hide', warning=FALSE} library (lavaan)<- ' f1 =~ warm + good.natured + tolerant + sincere f2 =~ independent + intelligent + confident + competent + competitive f3 =~ approval + moral + genuine f4 =~ quick + spontaneous + emotion + slow + deliberate + logic f5 =~ reputationSignal f6 =~ normSignal ' <- cfa (path, data= gjg_long3)summary (model, fit.measures= TRUE )``` 
