--- title: "IS Obesity Pilot (N = 150)" author: 'Marcus' date: "8/6/2024" 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: yes header-includes: - \usepackage{Roboto Condensed} knitr: opts_chunk: dev: ragg_png execute: warning: false --- # Setup {.tabset .tabset-pills} ## Libraries and functions {.unlisted .unnumbered} ```{r setup, message=F, results='hide', warning=FALSE} knitr::opts_chunk$set(warning = FALSE, message = FALSE) Mypackages <- 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", "lavaan") # install.packages(Mypackages) #you must remove the # in this comment if you need to install the packages! lapply(Mypackages, require, character.only = TRUE) options(knitr.kable.NA = '—') set.seed(1) ``` ## Load Data ```{r, message=F, results='hide', warning=FALSE} # read in data files setwd("~/Desktop") gjg_raw <-read.csv("/Users/mtrenfield17/Desktop/Research/Boston College Research/SISC Lab Research/IS Project/Obesity/IS Obesity Pilot 1.csv") ``` ## Functions ```{r, message=F, results='hide', warning=FALSE} plot_cooker <- function(data, iv, dv) { part1 <- ggplot(data, aes(x = {{iv}}, y = {{dv}}, fill = {{iv}})) + geom_violin(alpha = 0.3, scale = "count") + stat_summary(fun = "mean", geom = "point", size = 3, color = "black") + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2, #change to make a data set from allEffects with mean, low CI, high CI size = 1.5, color = "black") + theme_classic() + xlab("") + ylab("") ggpar(part1, legend = "none") } pol_line <- function(data, iv, dv) { ggplot(data, aes(x = {{iv}}, y = {{dv}}, color = condition)) + 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 = "Condition") } lizy_cooker <- function(dv, iv, Title, x_axis_labs, y_label, sample_size, coln, rown) { part1 <- ggviolin(gjg, 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(~ vignette, ncol = coln, nrow = rown, scales = "free", as.table = TRUE) ggpar(part1, legend = "none") } #POL_gjg_long <- filter(gjg_long, political_overall %in% c("Democrat", "Republican")) #gjg_long$political_overall #ggplot(gjg_long, aes(x = condition, y = p_approve, color = condition)) + #geom_point(stat="summary", fun="mean", size = 2) + #facet_wrap(~political_overall) + #scale_x_discrete(labels = NULL) ``` ## Reshaping data ```{r} #### filtering people who failed the attn check #### <- gjg_raw %>% filter (attentionCheck == 3 )## changing condition to factor and reordering ## $ condition <- as.factor (gjg$ condition)$ condition <- factor (gjg$ condition, levels = c ("individualScope" , "populationScope" , "mergedScope" ))# changing numeric DVs to numeric <- gjg %>% mutate_at (vars (policyDV, donation, helpDV_1: efficacy_3, age, pol, pid, edu, inc, weight, charityRealYN, Duration..in.seconds.), as.numeric)## renaming matrix variables names (gjg)[names (gjg) == 'helpDV_1' ] <- 'usGovHelp' names (gjg)[names (gjg) == 'helpDV_2' ] <- 'manufacturerHelp' names (gjg)[names (gjg) == 'helpDV_3' ] <- 'individualHelp' names (gjg)[names (gjg) == 'helpDV_4' ] <- 'youHelp' names (gjg)[names (gjg) == 'preventDV_1' ] <- 'usGovPrevent' names (gjg)[names (gjg) == 'preventDV_2' ] <- 'manufacturerPrevent' names (gjg)[names (gjg) == 'preventDV_3' ] <- 'individualPrevent' names (gjg)[names (gjg) == 'preventDV_4' ] <- 'youPrevent' names (gjg)[names (gjg) == 'feelingsMeasures_1' ] <- 'upset' names (gjg)[names (gjg) == 'feelingsMeasures_2' ] <- 'sympathetic' names (gjg)[names (gjg) == 'feelingsMeasures_3' ] <- 'touched' names (gjg)[names (gjg) == 'moral_1' ] <- 'donatingMoral' names (gjg)[names (gjg) == 'moral_2' ] <- 'eatingMoral' names (gjg)[names (gjg) == 'moral_3' ] <- 'adMoral' names (gjg)[names (gjg) == 'moral_4' ] <- 'sugarMoral' names (gjg)[names (gjg) == 'blame_1' ] <- 'manufacturerBlame' names (gjg)[names (gjg) == 'blame_2' ] <- 'individualBlame' names (gjg)[names (gjg) == 'efficacy_1' ] <- 'studyDonationEfficacy' names (gjg)[names (gjg) == 'efficacy_2' ] <- 'studyPolicyEfficacy' names (gjg)[names (gjg) == 'efficacy_3' ] <- 'donationEfficacy' ``` # Data Quality Checks ## Attention Check * 3 people failed the attention check```{r, message=F, results='hide', warning=FALSE} gjg_raw <- gjg_raw %>% filter(consent == 8) gjg_raw %>% group_by(attentionCheck) %>% dplyr::summarise(n = n()) %>% mutate(freq = n / sum(n)) ``` ## Charity Belief (Return and double check this) - People are *less* doubtful of the individual charity in the individual frame than combined frame (neither different from structural)```{r} ggplot (gjg, aes (x = condition, y = charityRealYN)) + geom_violin () + geom_boxplot (width= 0.1 , fill= "white" ) + theme_minimal () + labs (y = "Not at all to Extremely" , x = "Condition" , title = "Extent Participants Doubted the Charity was real" )# Check for duplicate column names anyDuplicated (names (gjg))names (gjg)$ condition <- relevel (gjg$ condition, ref = "mergedScope" )<- gjg$ condition <- relevel (gjg_I$ condition, ref = "individualScope" )<- lm (charityRealYN ~ condition, data = gjg)summary (mod_belief)<- lm (charityRealYN ~ condition, data = gjg_I)summary (mod_belief_I)``` # Demographics ```{r fig.dim = c(15, 13)} # Subset your data frame to include only the demographic columns demo_gjg <- gjg[, c("weight_TEXT", "gen_TEXT", "race_TEXT", "inc_TEXT", "edu_TEXT", "pol_TEXT", "pid_TEXT", "area_TEXT")] # Age mean(gjg$age, na.rm=TRUE) sd(gjg$age, na.rm=TRUE) # Loop through each demographic column and calculate frequency counts freq_tables <- list() for (col in names(demo_gjg)) { { freq_table <- as.data.frame(table(demo_gjg[[col]])) freq_table$Percent <- round(freq_table$Freq / sum(freq_table$Freq) * 100, 2) freq_tables[[col]] <- freq_table } } # Print the frequency tables for (i in seq_along(freq_tables)) { if (!is.null(freq_tables[[i]])) { cat("\nTable of frequencies for", names(freq_tables)[i], ":\n") print(freq_tables[[i]]) } } ``` # Correlations ```{r} <- gjg[c ("policyDV" , "donation" , "usGovHelp" , "manufacturerHelp" , "individualHelp" , "youHelp" , "usGovPrevent" , "manufacturerPrevent" , "individualPrevent" , "youPrevent" , "upset" , "sympathetic" , "touched" , "donatingMoral" , "eatingMoral" , "adMoral" , "sugarMoral" , "individualBlame" , "manufacturerBlame" , "studyDonationEfficacy" , "studyPolicyEfficacy" , "donationEfficacy" , "pol" , "edu" , "inc" , "age" , "weight" )]# Compute pairwise correlations <- cor (DVs, use = "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 )``` # EFA ```{r, include=FALSE} factorDVs <- gjg[c("policyDV", "donation", "usGovHelp", "manufacturerHelp", "individualHelp", "youHelp", "usGovPrevent", "manufacturerPrevent", "individualPrevent", "youPrevent", "upset", "sympathetic", "touched", "donatingMoral", "eatingMoral", "adMoral", "sugarMoral", "individualBlame", "manufacturerBlame", "studyDonationEfficacy", "studyPolicyEfficacy", "donationEfficacy")] ``` ## Evaluating the correlation matrix ```{r, include=FALSE} factorCor <- cor(factorDVs, use = "complete.obs") # Assuming you have a correlation matrix named "correlation_matrix" correlation_threshold <- 0.8 high_correlations <- which(factorCor > correlation_threshold & upper.tri(factorCor, diag = FALSE), arr.ind = TRUE) # Extract the row and column indices of high correlations row_indices <- high_correlations[, 1] column_indices <- high_correlations[, 2] # Extract the correlations greater than 0.8 high_correlation_values <- factorCor[high_correlations] # Print the results high_correlation_results <- data.frame(row_indices, column_indices, high_correlation_values) print(high_correlation_results) # removing one from all pairs of overcorrelated variables overcorrelated <- c() # youPrevent and individualPrevent overcorrelated efaDVs <- factorDVs[ ,!(names(factorDVs) %in% overcorrelated)] efaDVs <- factorDVs ``` ## Determining number of factors - Scree plot suggests 3-5- eigenvalue method suggests 3- Parallel Analysis suggests 5```{r, include=FALSE} ## evaluating data corr_DVs = cor(efaDVs, use="complete.obs") KMO(efaDVs) cortest.bartlett(efaDVs) #tests correlations between variables ## finding number of factors ev<-eigen(corr_DVs) #gets eigenvalues (variance explained by each component) ev$values scree(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 dat_fa <- na.omit(DVs) ``` ## 3 Factors ```{r} <- factanal (na.omit (efaDVs), factors= 3 , rotation= "promax" , scores = "regression" )print (fit, digits = 2 , cutoff = .3 , sort = TRUE )<- fit$ loadingsfa.diagram (loads)# ## Cronbach's Alpha # f1 <- efaDVs[ , c("futureSacrifice", "futureImpact", "futureResponsibility", "moralMitigate", # "moralCollective", "intergenerational1", "opportunityChoiceNum", "donation", "moralProtect", "dayToDayEfficacy")] # f2 <- efaDVs[ , c("effortEfficacy", "americansEfficacy1", "americansEfficacy2", "optimism")] # f3 <- efaDVs[ , c("thankfulPG", "sacrificesPG", "gratitudePG")] # f4 <- efaDVs[ , c("intergenerational1", "intergenerational2", "american1", "american2")] # f5 <- efaDVs[ , c("hope", "optimism")] # alpha(f1) # alpha(f2) # alpha(f3) # alpha(f4) # alpha(f5) ``` ## 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 # f1 <- efaDVs[ , c("futureSacrifice", "futureImpact", "futureResponsibility", "moralMitigate", # "moralCollective", "intergenerational1", "opportunityChoiceNum", "donation", "moralProtect", "dayToDayEfficacy")] # f2 <- efaDVs[ , c("effortEfficacy", "americansEfficacy1", "americansEfficacy2", "optimism")] # f3 <- efaDVs[ , c("thankfulPG", "sacrificesPG", "gratitudePG")] # f4 <- efaDVs[ , c("intergenerational1", "intergenerational2", "american1", "american2")] # f5 <- efaDVs[ , c("hope", "optimism")] # alpha(f1) # alpha(f2) # alpha(f3) # alpha(f4) # alpha(f5) ``` # CFA - Bad fit```{r} <- ' responsibilityHelp =~ usGovHelp + manufacturerHelp + individualHelp + youHelp responsibilityPrevent =~ usGovPrevent + manufacturerPrevent + individualPrevent + youPrevent affect =~ upset + sympathetic + touched moralIssue =~ donatingMoral + eatingMoral + adMoral + sugarMoral blame =~ manufacturerBlame + individualBlame efficacy =~ studyDonationEfficacy + studyPolicyEfficacy + donationEfficacy ' <- cfa (model, data = factorDVs)summary (fit, fit.measures= TRUE )``` # Behavioral Outcomes ```{r fig.dim = c(12, 10)} gjg$condition <- factor(gjg$condition, levels = c("individualScope", "populationScope", "mergedScope")) percep_plot_list <- list(plot_cooker(gjg, condition, donation), plot_cooker(gjg, condition, policyDV)) # Adding titles to each plot percep_plot_list[[1]] <- percep_plot_list[[1]] + ggtitle("Donations") percep_plot_list[[2]] <- percep_plot_list[[2]] + ggtitle("Support for Policy") percep_plot_arranged <- ggarrange(plotlist = percep_plot_list, ncol = 2, nrow = 1) overall_percep_title <- ggdraw() + draw_label("Behavioral DVs", fontface = "bold") plot_grid(overall_percep_title, percep_plot_arranged, ncol = 1, rel_heights = c(0.1, 0.9)) gjg %>% group_by(condition) %>% dplyr::summarise(mean(policyDV)) gjg %>% group_by(condition) %>% dplyr::summarise(mean(donation)) ``` <!-- ## Cleaner Plots --> <!-- ```{r} --> <!-- gjg %>% --> <!-- group_by(condition) %>% --> <!-- dplyr::summarise(meanDonation = mean(donation), meanSupport = mean(policyDV)) %>% --> <!-- print() --> <!-- # reordering data --> <!-- gjg$condition <- factor(gjg$condition, levels = c("individualScope", "populationScope", "mergedScope")) --> <!-- # Donation Plot --> <!-- donationP <- ggplot(gjg, aes(x = condition, y = donation)) + --> <!-- geom_point(stat = "summary", fun = "mean", size = 4, position = position_dodge(width = 0.25)) + --> <!-- stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.25), width = 0.2, size = 1.5) + --> <!-- scale_x_discrete(labels = c("Individual", "Structural", "Combined")) + --> <!-- labs(x = "Narrative Frame", y = "Amount ($)", title = "Donation to an Individual Victim") + --> <!-- theme_minimal(base_size = 15) + # Use a minimal theme --> <!-- theme( --> <!-- plot.title = element_text(hjust = 0.5, size = 35), --> <!-- axis.text.x = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.text.y = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.title.y = element_text(face = "plain", size = 30, color = "black"), --> <!-- axis.title.x = element_text(face = "plain", size = 30, color = "black"), --> <!-- legend.title = element_text(size = 14), # Increase legend title size --> <!-- legend.text = element_text(size = 12), --> <!-- panel.grid.major = element_line(color = "gray"), # Set major grid lines to gray --> <!-- panel.grid.minor = element_blank(), # Hide minor grid lines --> <!-- panel.background = element_rect(fill = "white", color = "white"), --> <!-- panel.border = element_rect(color = "black", fill = NA, size = 1) --> <!-- ) + --> <!-- coord_cartesian(ylim = c(1, 6)) + --> <!-- scale_y_continuous(breaks = c(1,2,3,4,5,6)) --> <!-- print(donationP) --> <!-- ggsave("obesity_donations.png", plot = donationP, width = 10, height = 12) --> <!-- gjg_noDem <- gjg %>% filter(pol<5) --> <!-- ## Policy Support Plot --> <!-- policyP <- ggplot(gjg, aes(x = condition, y = policyDV)) + --> <!-- geom_point(stat = "summary", fun = "mean", size = 4, position = position_dodge(width = 0.25)) + --> <!-- stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", position = position_dodge(width = 0.25), width = 0.2, size = 1.5) + --> <!-- geom_hline(yintercept = 4, linetype = "dashed", color = "black", size = 1) + --> <!-- scale_x_discrete(labels = c("Individual", "Structural", "Combined")) + --> <!-- labs(x = "Narrative Frame", y = "Support", title = " Support for Federal \n\ Decarbonization Policy") + --> <!-- theme_minimal(base_size = 15) + # Use a minimal theme --> <!-- theme( --> <!-- plot.title = element_text(hjust = 0.5, size = 35), --> <!-- axis.text.x = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.text.y = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.title.y = element_text(face = "plain", size = 30, color = "black"), --> <!-- axis.title.x = element_text(face = "plain", size = 30, color = "black"), --> <!-- legend.title = element_text(size = 14), # Increase legend title size --> <!-- legend.text = element_text(size = 12), --> <!-- panel.grid.major = element_line(color = "gray"), # Set major grid lines to gray --> <!-- panel.grid.minor = element_blank(), # Hide minor grid lines --> <!-- panel.background = element_rect(fill = "white", color = "white"), --> <!-- panel.border = element_rect(color = "black", fill = NA, size = 1) --> <!-- ) + --> <!-- coord_cartesian(ylim = c(1, 7)) + --> <!-- scale_y_continuous(breaks = c(1,2,3,4,5,6,7)) --> <!-- print(policyP) --> <!-- ggsave("obesity_policy_support.png", plot = policyP, width = 10, height = 12) --> <!-- library(gridExtra) --> <!-- # Now arrange your plots with adjusted spacing --> <!-- combinedB <- grid.arrange(donationP, policyP, ncol = 2, nrow = 1, --> <!-- widths = c(1, 1), # Adjust the relative widths of the plots --> <!-- layout_matrix = rbind(c(1, 2))) --> <!-- ggsave("combined_behavioral_outcomes.png", plot = combinedB, width = 14, height = 10) --> <!-- ``` --> ## Inferential Stats ```{r} $ condition <- relevel (gjg$ condition, ref = "mergedScope" )<- gjg$ condition <- relevel (gjg_I$ condition, ref = "individualScope" )<- lm (donation ~ condition, data = gjg)summary (mod_donation)confint (mod_donation)<- lm (donation ~ condition* pol, data = gjg)summary (mod_donation)<- lm (donation ~ condition* pol, data = gjg_I)summary (mod_donationI)<- lm (donation ~ condition, data = gjg_I)summary (mod_donationI)<- lm (policyDV ~ condition, data = gjg)summary (mod_policy)<- lm (policyDV ~ condition, data = gjg_I)summary (mod_policyI)``` # Responsibility (Help and Prevent) ```{r fig.dim = c(12, 10)} gjg_long<-gjg %>% gather(stim, resp, "usGovHelp":"donationEfficacy") gjg_long$condition <- factor(gjg_long$condition, levels = c("individualScope", "populationScope", "mergedScope")) responsibilityHelpLong <- gjg_long %>% filter(grepl("Help", stim)) facet_cooker <- function(data, iv, dv, coln, rown) { part1 <- ggplot(data, aes(x = {{iv}}, y = {{dv}}, fill = {{iv}})) + geom_violin(alpha = 0.3, scale = "count") + stat_summary(fun = "mean", geom = "point", size = 3, color = "black") + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2, #change to make a data set from allEffects with mean, low CI, high CI size = 1.5, color = "black") + theme_classic() + xlab("") + ylab("") + facet_wrap(~ stim, ncol = coln, nrow = rown, scales = "free", as.table = TRUE) ggpar(part1, legend = "none") } facet_cooker(responsibilityHelpLong, condition, resp, coln = 2, rown = 2) responsibilityPreventLong <- gjg_long %>% filter(grepl("Prevent", stim)) facet_cooker(responsibilityPreventLong, condition, resp, coln = 2, rown = 2) ``` ## Inferential Stats ```{r } mod_usGovHelp <- lm(usGovHelp ~ condition, data = gjg) summary(mod_usGovHelp) mod_usGovHelpI <- lm(usGovHelp ~ condition, data = gjg_I) summary(mod_usGovHelpI) mod_manufacturerHelp <- lm(manufacturerHelp ~ condition, data = gjg) summary(mod_manufacturerHelp) mod_manufacturerHelpI <- lm(manufacturerHelp ~ condition, data = gjg_I) summary(mod_manufacturerHelpI) mod_individualHelp <- lm(individualHelp ~ condition, data = gjg) summary(mod_individualHelp) mod_individualHelpI <- lm(individualHelp ~ condition, data = gjg_I) summary(mod_individualHelpI) mod_youHelp <- lm(youHelp ~ condition, data = gjg) summary(mod_youHelp) mod_youHelpI <- lm(youHelp ~ condition, data = gjg_I) summary(mod_youHelpI) # Prevent mod_usGovPrevent <- lm(usGovPrevent ~ condition, data = gjg) summary(mod_usGovPrevent) mod_usGovPreventI <- lm(usGovPrevent ~ condition, data = gjg_I) summary(mod_usGovPreventI) mod_manufacturerPrevent <- lm(manufacturerPrevent ~ condition, data = gjg) summary(mod_manufacturerPrevent) mod_manufacturerPreventI <- lm(manufacturerPrevent ~ condition, data = gjg_I) summary(mod_manufacturerPreventI) mod_individualPrevent <- lm(individualPrevent ~ condition, data = gjg) summary(mod_individualPrevent) mod_individualPreventI <- lm(individualPrevent ~ condition, data = gjg_I) summary(mod_individualPreventI) mod_youPrevent <- lm(youPrevent ~ condition, data = gjg) summary(mod_youPrevent) mod_youPreventI <- lm(youPrevent ~ condition, data = gjg_I) summary(mod_youPreventI) ``` # Affect Help ```{r fig.dim = c(12, 10)} affectLong <- gjg_long %>% filter(stim %in% c("upset", "sympathetic", "touched")) facet_cooker(affectLong, condition, resp, coln = 2, rown = 2) ``` ## Inferential Stats ```{r } mod_upset <- lm(upset ~ condition, data = gjg) summary(mod_upset) mod_upsetI <- lm(upset ~ condition, data = gjg_I) summary(mod_upsetI) mod_sympathetic <- lm(sympathetic ~ condition, data = gjg) summary(mod_sympathetic) mod_sympatheticI <- lm(sympathetic ~ condition, data = gjg_I) summary(mod_sympatheticI) mod_touched <- lm(touched ~ condition, data = gjg) summary(mod_touched) mod_touchedI <- lm(touched ~ condition, data = gjg_I) summary(mod_touchedI) ``` # Efficacy ```{r fig.dim = c(12, 10)} efficacyHelpLong <- gjg_long %>% filter(grepl("Efficacy", stim)) facet_cooker(efficacyHelpLong, condition, resp, coln = 2, rown = 2) ``` ## Inferential Stats ```{r } mod_studyDonationEfficacy <- lm(studyDonationEfficacy ~ condition, data = gjg) summary(mod_studyDonationEfficacy) mod_studyDonationEfficacyI <- lm(studyDonationEfficacy ~ condition, data = gjg_I) summary(mod_studyDonationEfficacyI) mod_studyPolicyEfficacy <- lm(studyPolicyEfficacy ~ condition, data = gjg) summary(mod_studyPolicyEfficacy) mod_studyPolicyEfficacyI <- lm(studyPolicyEfficacy ~ condition, data = gjg_I) summary(mod_studyPolicyEfficacyI) mod_donationEfficacy <- lm(donationEfficacy ~ condition, data = gjg) summary(mod_donationEfficacy) mod_donationEfficacyI <- lm(donationEfficacy ~ condition, data = gjg_I) summary(mod_donationEfficacyI) ``` # Morality ```{r fig.dim = c(12, 10)} moralIssueLong <- gjg_long %>% filter(grepl("Moral", stim)) facet_cooker(moralIssueLong, condition, resp, coln = 2, rown = 2) blameLong <- gjg_long %>% filter(grepl("Blame", stim)) facet_cooker(blameLong, condition, resp, coln = 2, rown = 2) ``` ## Inferential Stats ```{r } mod_donatingMoral <- lm(donatingMoral ~ condition, data = gjg) summary(mod_donatingMoral) mod_donatingMoralI <- lm(donatingMoral ~ condition, data = gjg_I) summary(mod_donatingMoralI) mod_eatingMoral <- lm(eatingMoral ~ condition, data = gjg) summary(mod_eatingMoral) mod_eatingMoralI <- lm(eatingMoral ~ condition, data = gjg_I) summary(mod_eatingMoralI) mod_adMoral <- lm(adMoral ~ condition, data = gjg) summary(mod_adMoral) mod_adMoralI <- lm(adMoral ~ condition, data = gjg_I) summary(mod_adMoralI) mod_sugarMoral <- lm(sugarMoral ~ condition, data = gjg) summary(mod_sugarMoral) mod_sugarMoralI <- lm(sugarMoral ~ condition, data = gjg_I) summary(mod_sugarMoralI) mod_manufacturerBlame <- lm(manufacturerBlame ~ condition, data = gjg) summary(mod_manufacturerBlame) mod_manufacturerBlameI <- lm(manufacturerBlame ~ condition, data = gjg_I) summary(mod_manufacturerBlameI) mod_indBlame <- lm(individualBlame ~ condition, data = gjg) summary(mod_indBlame) mod_indBlameI <- lm(individualBlame ~ condition, data = gjg_I) summary(mod_indBlameI) ``` # Creating Composites Z-score ```{r} <- gjg %>% mutate (govResponsibility = (usGovHelp + usGovPrevent) / 2 ,indivResponsibility = (individualHelp + individualPrevent + individualBlame) / 3 ,manufacturerResponsibility = (manufacturerHelp + manufacturerPrevent + manufacturerBlame) / 3 ,manufacturerMoral = (adMoral + sugarMoral) / 2 ,youResponsibility = (youHelp + youPrevent) / 2 ,compassion = (upset + sympathetic + touched) / 3 ,indivEfficacy = (studyDonationEfficacy + donationEfficacy) / 2 ,indivMoral = (donatingMoral + eatingMoral) / 2 # Standardizing variables <- c ("usGovHelp" , "manufacturerHelp" , "individualHelp" , "youHelp" , "usGovPrevent" , "manufacturerPrevent" , "individualPrevent" , "youPrevent" , "upset" , "sympathetic" , "touched" , "donatingMoral" , "eatingMoral" , "adMoral" , "sugarMoral" , "manufacturerBlame" , "individualBlame" , "studyDonationEfficacy" , "studyPolicyEfficacy" , "donationEfficacy" , "govResponsibility" , "indivResponsibility" , "manufacturerResponsibility" , "youResponsibility" , "compassion" , "indivEfficacy" , "manufacturerMoral" , "indivMoral" )<- gjg %>% mutate (across (all_of (variables_to_standardize), ~ as.numeric (scale (.)))) ``` ## Inferential Stats - Individual & Merged donate more than Structural```{r} $ condition <- relevel (gjg$ condition, ref = "mergedScope" )<- gjg$ condition <- relevel (gjg_I$ condition, ref = "individualScope" )<- lm (manufacturerResponsibility ~ condition, data = gjg)summary (mod_compRes)confint (mod_compRes)<- lm (indivResponsibility ~ condition, data = gjg)summary (mod_indRes)confint (mod_indRes)<- lm (compassion ~ condition, data = gjg)summary (mod_compassion)confint (mod_compassion)<- lm (indivEfficacy ~ condition, data = gjg)summary (mod_indivEff)confint (mod_indivEff)<- lm (studyPolicyEfficacy ~ condition, data = gjg)summary (mod_polEff)confint (mod_polEff)``` <!-- # Mechanisms Plot --> <!-- ## Plot with all measures --> <!-- ```{r fig.width=12, fig.height=10} --> <!-- # gather the standardized variables into a long format --> <!-- gjg_z_long <- gjg_z %>% --> <!-- gather(key = "measure", value = "value", --> <!-- manufacturerHelp, usGovHelp, youHelp, manufacturerPrevent, individualPrevent, --> <!-- usGovPrevent, youPrevent, upset, sympathetic, touched, --> <!-- studyDonationEfficacy, donationEfficacy, studyPolicyEfficacy, --> <!-- manufacturerBlame, donatingMoral, manufacturerMoral) --> <!-- # setting order --> <!-- gjg_z_long$measure <- factor( --> <!-- gjg_z_long$measure, --> <!-- levels = rev(c("studyDonationEfficacy", "donationEfficacy", --> <!-- "studyPolicyEfficacy", "sympathetic", "touched", "upset", --> <!-- "individualPrevent", "youHelp", "youPrevent", "usGovHelp", --> <!-- "usGovPrevent", "manufacturerHelp", "manufacturerPrevent","manufacturerBlame", --> <!-- "individualBlame", "donatingMoral", "manufacturerMoral")) --> <!-- ) --> <!-- # Rename levels --> <!-- levels(gjg_z_long$measure) <- rev(c( --> <!-- "Efficacy: Study Donation Efficacy", --> <!-- "Efficacy: Donations", --> <!-- "Efficacy: Policy", --> <!-- "Affect: Sympathetic", --> <!-- "Affect: Touched", --> <!-- "Affect: Upset", --> <!-- "Responsibility: Individual Prevent", --> <!-- "Responsibility: You Help", --> <!-- "Responsibility: You Prevent", --> <!-- "Responsibility: US Gov Help", --> <!-- "Responsibility: US Gov Prevent", --> <!-- "Responsibility: manufacturer Help", --> <!-- "Responsibility: manufacturer Prevent", --> <!-- "Responsibility: manufacturer Blame", --> <!-- "Responsibility: Individual Blame", --> <!-- "Moral Issue: Donating to Victims", --> <!-- "Moral Issue: Companies Reducing Emissions" --> <!-- )) --> <!-- # Update the condition factor with new labels --> <!-- gjg_z_long$condition <- factor(gjg_z_long$condition, --> <!-- levels = c("individualScope", "populationScope", "mergedScope"), --> <!-- labels = c("Individual", "Structural", "Combined")) --> <!-- # Custom colors --> <!-- library(RColorBrewer) --> <!-- set1_palette <- brewer.pal(n = 3, name = 'Set1') --> <!-- custom_colors <- c("Individual" = set1_palette[1], --> <!-- "Structural" = set1_palette[2], --> <!-- "Combined" = "#800080") --> <!-- # Custom shapes --> <!-- custom_shapes <- c("Individual" = 16, "Structural" = 15, "Combined" = 17) --> <!-- # Create the plot with customizations --> <!-- zplot <- ggplot(gjg_z_long, aes(x = measure, y = value, color = condition, shape = condition)) + --> <!-- stat_summary(fun = mean, geom = "point", position = position_dodge(width = 0.5), size = 3) + --> <!-- stat_summary(fun.data = mean_cl_normal, geom = "errorbar", --> <!-- position = position_dodge(width = 0.5), width = 0.2) + --> <!-- scale_color_manual(values = custom_colors) + --> <!-- scale_shape_manual(values = custom_shapes) + --> <!-- coord_flip() + --> <!-- theme_minimal() + --> <!-- labs(x = "Measure", y = "Z-Score", title = "Performance of Each Narrative on Outcomes", color = "Narrative Frame", shape = "Narrative Frame") + --> <!-- theme( --> <!-- legend.position = "bottom", --> <!-- legend.text = element_text(size = 20), --> <!-- legend.title = element_text(size = 20), --> <!-- plot.title = element_text(size = 25, hjust = 0.5, face = "bold"), --> <!-- axis.title.y = element_text(face = "plain", size = 22, color = "black"), --> <!-- axis.title.x = element_text(face = "plain", size = 22, color = "black"), axis.text.x = element_text(face = "plain", size = 18), --> <!-- axis.text.y = element_text(face = "plain", size = 18), --> <!-- panel.border = element_rect(color = "black", fill = NA, size = 1), --> <!-- legend.key.size = unit(3, "lines") --> <!-- ) --> <!-- zplot <- zplot + --> <!-- theme(panel.spacing = unit(2, "lines")) # Adjust the space between facets/panels --> <!-- # Print the plot --> <!-- print(zplot) --> <!-- ``` --> <!-- ## Plot with collapsed measures --> <!-- ```{r} --> <!-- # First, let's gather the standardized variables into a long format --> <!-- gjg_z_long <- gjg_z %>% --> <!-- gather(key = "measure", value = "value", --> <!-- manufacturerResponsibility, usGovResponsibility, individualPrevent, --> <!-- selfResponsibility, individualPrevent, compassion, donationEfficacy, --> <!-- studyPolicyEfficacy) --> <!-- # setting order --> <!-- gjg_z_long$measure <- factor( --> <!-- gjg_z_long$measure, --> <!-- levels = rev(c( --> <!-- "donationEfficacy", "studyPolicyEfficacy", --> <!-- "compassion", "selfResponsibility", "individualPrevent", --> <!-- "usGovResponsibility", "manufacturerResponsibility" --> <!-- )) --> <!-- ) --> <!-- # Rename levels --> <!-- levels(gjg_z_long$measure) <- rev(c( --> <!-- "Efficacy: Donations", --> <!-- "Efficacy: Policy", --> <!-- "Responsibility: Self", --> <!-- "Compassion", --> <!-- )) --> <!-- # Update the condition factor with new labels --> <!-- gjg_z_long$condition <- factor(gjg_z_long$condition, --> <!-- levels = c("individualScope", "populationScope", "mergedScope"), --> <!-- labels = c("Individual", "Structural", "Combined")) --> <!-- # Custom colors --> <!-- library(RColorBrewer) --> <!-- set1_palette <- brewer.pal(n = 3, name = 'Set1') --> <!-- custom_colors <- c("Individual" = set1_palette[1], --> <!-- "Structural" = set1_palette[2], --> <!-- "Combined" = "#800080") --> <!-- # Custom shapes --> <!-- custom_shapes <- c("Individual" = 16, "Structural" = 15, "Combined" = 17) --> <!-- # Create the plot with customizations --> <!-- zplot <- ggplot(gjg_z_long, aes(x = measure, y = value, color = condition, shape = condition)) + --> <!-- stat_summary(fun = mean, geom = "point", position = position_dodge(width = 0.7), size = 3) + --> <!-- stat_summary(fun.data = mean_cl_normal, geom = "errorbar", --> <!-- position = position_dodge(width = 0.7), width = 0.2) + --> <!-- scale_color_manual(values = custom_colors) + --> <!-- scale_shape_manual(values = custom_shapes) + --> <!-- coord_flip() + --> <!-- theme_minimal() + --> <!-- labs(x = "Measure", y = "Z-Score", title = "Performance of Each Narrative on Outcomes", color = "Narrative Frame", shape = "Narrative Frame") + --> <!-- theme( --> <!-- legend.position = "bottom", --> <!-- legend.text = element_text(size = 20, color = "black"), --> <!-- legend.title = element_text(size = 20, color = "black"), --> <!-- plot.title = element_blank(), --> <!-- axis.title.y = element_text(face = "plain", size = 22, color = "black"), --> <!-- axis.title.x = element_text(face = "plain", size = 22, color = "black"), axis.text.x = element_text(face = "plain", size = 18, color = "black"), --> <!-- axis.text.y = element_text(face = "plain", size = 18, color = "black"), --> <!-- panel.border = element_rect(color = "black", fill = NA, size = 1), --> <!-- legend.key.size = unit(3, "lines") --> <!-- ) --> <!-- zplot <- zplot + --> <!-- theme(panel.spacing = unit(2.5, "lines")) # Adjust the space between facets/panels --> <!-- # Print the plot --> <!-- print(zplot) --> <!-- ``` --> <!-- # Mediation --> <!-- ## S to I Policy DV Mediation --> <!-- ```{r} --> <!-- gjg_noCombined <- gjg %>% filter(condition != "mergedScope") --> <!-- gjg_noCombined <- within(gjg_noCombined, { --> <!-- structuralNarrative <- ifelse(condition == "populationScope", 1, 0) --> <!-- }) --> <!-- model <- ' --> <!-- # outcome model --> <!-- policyDV ~ c*structuralNarrative + b1*youResponsibility + b2*indivResponsibility + b3*manufacturerResponsibility + b4*govResponsibility + b5*indivEfficacy + --> <!-- b6*studyPolicyEfficacy + b7*compassion + b8*donatingMoral + b9*manufacturerMoral --> <!-- # mediator models --> <!-- youResponsibility ~ a1*structuralNarrative --> <!-- indivResponsibility ~ a2*structuralNarrative --> <!-- manufacturerResponsibility ~ a3*structuralNarrative --> <!-- govResponsibility ~ a4*structuralNarrative --> <!-- indivEfficacy ~ a5*structuralNarrative --> <!-- studyPolicyEfficacy ~ a6*structuralNarrative --> <!-- compassion ~ a7*structuralNarrative --> <!-- donatingMoral ~ a8*structuralNarrative --> <!-- manufacturerMoral ~ a9*structuralNarrative --> <!-- # Indirect Effects --> <!-- youResIDE := a1*b1 --> <!-- indivResIDE := a2*b2 --> <!-- manufacturerResIDE := a3*b3 --> <!-- govResIDE := a4*b4 --> <!-- indivEffIDE := a5*b5 --> <!-- studyPolEffIDE := a6*b6 --> <!-- compassionIDE := a7*b7 --> <!-- donatingMoralIDE := a8*b8 --> <!-- manufacturerMoralIDE := a9*b9 --> <!-- # total effect --> <!-- total := c + (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- # total indirect effect --> <!-- totalIDE := (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- ' --> <!-- fit <- sem(model, data = gjg_noCombined) --> <!-- summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) --> <!-- summary(lm(policyDV ~ structuralNarrative, gjg_noCombined)) --> <!-- summary(lm(policyDV ~ structuralNarrative + youResponsibility + indivResponsibility + --> <!-- manufacturerResponsibility + indivEfficacy + studyPolicyEfficacy + compassion --> <!-- + donatingMoral + manufacturerMoral, gjg_noCombined)) --> <!-- library(lavaanPlot) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE, sig = .05) --> <!-- ``` --> <!-- ## S to I Phonebank DV Mediation --> <!-- ```{r} --> <!-- gjg_noCombined <- gjg %>% filter(condition != "mergedScope") --> <!-- gjg_noCombined <- within(gjg_noCombined, { --> <!-- structuralNarrative <- ifelse(condition == "populationScope", 1, 0) --> <!-- }) --> <!-- model <- ' --> <!-- # outcome model --> <!-- phoneYN ~ c*structuralNarrative + b1*youResponsibility + b2*indivResponsibility + b3*manufacturerResponsibility + b4*govResponsibility + b5*indivEfficacy + --> <!-- b6*studyPolicyEfficacy + b7*compassion + b8*donatingMoral + b9*manufacturerMoral --> <!-- # mediator models --> <!-- youResponsibility ~ a1*structuralNarrative --> <!-- indivResponsibility ~ a2*structuralNarrative --> <!-- manufacturerResponsibility ~ a3*structuralNarrative --> <!-- govResponsibility ~ a4*structuralNarrative --> <!-- indivEfficacy ~ a5*structuralNarrative --> <!-- studyPolicyEfficacy ~ a6*structuralNarrative --> <!-- compassion ~ a7*structuralNarrative --> <!-- donatingMoral ~ a8*structuralNarrative --> <!-- manufacturerMoral ~ a9*structuralNarrative --> <!-- # Indirect Effects --> <!-- youResIDE := a1*b1 --> <!-- indivResIDE := a2*b2 --> <!-- manufacturerResIDE := a3*b3 --> <!-- govResIDE := a4*b4 --> <!-- indivEffIDE := a5*b5 --> <!-- studyPolEffIDE := a6*b6 --> <!-- compassionIDE := a7*b7 --> <!-- donatingMoralIDE := a8*b8 --> <!-- manufacturerMoralIDE := a9*b9 --> <!-- # total effect --> <!-- total := c + (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- ' --> <!-- fit <- sem(model, data = gjg_noCombined) --> <!-- summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) --> <!-- summary(lm(phoneYN ~ structuralNarrative, gjg_noCombined)) --> <!-- summary(lm(phoneYN ~ structuralNarrative + youResponsibility + indivResponsibility + --> <!-- manufacturerResponsibility + indivEfficacy + studyPolicyEfficacy + compassion --> <!-- + donatingMoral + manufacturerMoral, gjg_noCombined)) --> <!-- library(lavaanPlot) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE, sig = .05) --> <!-- ``` --> <!-- ## C to I Policy DV Mediation --> <!-- ```{r} --> <!-- gjg_noStructural <- gjg %>% filter(condition != "populationScope") --> <!-- gjg_noStructural <- within(gjg_noStructural, { --> <!-- combinedNarrative <- ifelse(condition == "mergedScope", 1, 0) --> <!-- }) --> <!-- model <- ' --> <!-- # outcome model --> <!-- policyDV ~ c*combinedNarrative + b1*youResponsibility + b2*indivResponsibility + b3*manufacturerResponsibility + b4*govResponsibility + b5*indivEfficacy + --> <!-- b6*studyPolicyEfficacy + b7*compassion + b8*donatingMoral + b9*manufacturerMoral --> <!-- #outcome without other mediators --> <!-- # mediator models --> <!-- youResponsibility ~ a1*combinedNarrative --> <!-- indivResponsibility ~ a2*combinedNarrative --> <!-- manufacturerResponsibility ~ a3*combinedNarrative --> <!-- govResponsibility ~ a4*combinedNarrative --> <!-- indivEfficacy ~ a5*combinedNarrative --> <!-- studyPolicyEfficacy ~ a6*combinedNarrative --> <!-- compassion ~ a7*combinedNarrative --> <!-- donatingMoral ~ a8*combinedNarrative --> <!-- manufacturerMoral ~ a9*combinedNarrative --> <!-- # Indirect Effects --> <!-- youResIDE := a1*b1 --> <!-- indivResIDE := a2*b2 --> <!-- manufacturerResIDE := a3*b3 --> <!-- govResIDE := a4*b4 --> <!-- indivEffIDE := a5*b5 --> <!-- studyPolEffIDE := a6*b6 --> <!-- compassionIDE := a7*b7 --> <!-- donatingMoralIDE := a8*b8 --> <!-- manufacturerMoralIDE := a9*b9 --> <!-- # total effect --> <!-- total := c + (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- # total indirect effect --> <!-- totalIDE := (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- ' --> <!-- fit <- sem(model, data = gjg_noStructural) --> <!-- summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) --> <!-- summary(lm(policyDV ~ combinedNarrative, gjg_noStructural)) --> <!-- summary(lm(policyDV ~ combinedNarrative + youResponsibility + indivResponsibility + --> <!-- manufacturerResponsibility + indivEfficacy + studyPolicyEfficacy + compassion --> <!-- + donatingMoral + manufacturerMoral, gjg_noStructural)) --> <!-- library(lavaanPlot) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE, sig = .05) --> <!-- ``` --> <!-- ## C to I Phonebank DV Mediation --> <!-- ```{r} --> <!-- gjg_noStructural <- gjg %>% filter(condition != "populationScope") --> <!-- gjg_noStructural <- within(gjg_noStructural, { --> <!-- combinedNarrative <- ifelse(condition == "mergedScope", 1, 0) --> <!-- }) --> <!-- model <- ' --> <!-- # outcome model --> <!-- phoneYN ~ c*combinedNarrative + b1*youResponsibility + b2*indivResponsibility + b3*manufacturerResponsibility + b4*govResponsibility + b5*indivEfficacy + --> <!-- b6*studyPolicyEfficacy + b7*compassion + b8*donatingMoral + b9*manufacturerMoral --> <!-- # mediator models --> <!-- youResponsibility ~ a1*combinedNarrative --> <!-- indivResponsibility ~ a2*combinedNarrative --> <!-- manufacturerResponsibility ~ a3*combinedNarrative --> <!-- govResponsibility ~ a4*combinedNarrative --> <!-- indivEfficacy ~ a5*combinedNarrative --> <!-- studyPolicyEfficacy ~ a6*combinedNarrative --> <!-- compassion ~ a7*combinedNarrative --> <!-- donatingMoral ~ a8*combinedNarrative --> <!-- manufacturerMoral ~ a9*combinedNarrative --> <!-- # Indirect Effects --> <!-- youResIDE := a1*b1 --> <!-- indivResIDE := a2*b2 --> <!-- manufacturerResIDE := a3*b3 --> <!-- govResIDE := a4*b4 --> <!-- indivEffIDE := a5*b5 --> <!-- studyPolEffIDE := a6*b6 --> <!-- compassionIDE := a7*b7 --> <!-- donatingMoralIDE := a8*b8 --> <!-- manufacturerMoralIDE := a9*b9 --> <!-- # total effect --> <!-- total := c + (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5) + (a6*b6) + (a7*b7) + --> <!-- (a8*b8) + (a9*b9) --> <!-- ' --> <!-- fit <- sem(model, data = gjg_noStructural) --> <!-- summary(fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) --> <!-- summary(lm(phoneYN ~ combinedNarrative, gjg_noStructural)) --> <!-- summary(lm(phoneYN ~ combinedNarrative + youResponsibility + indivResponsibility + --> <!-- manufacturerResponsibility + indivEfficacy + studyPolicyEfficacy + compassion --> <!-- + donatingMoral + manufacturerMoral, gjg_noStructural)) --> <!-- library(lavaanPlot) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE) --> <!-- lavaanPlot(model=fit, node_options = list(shape = "box", fontname = "Helvetica"), coefs = TRUE, sig = .05) --> <!-- library(rstatix) --> <!-- gjg %>% --> <!-- group_by(condition) %>% --> <!-- cor_test(policyDV, youResponsibility, manufacturerResponsibility, studyPolicyEfficacy, manufacturerMoral) --> <!-- corr.test(gjg[gjg$condition == "individualScope", c(46, 50, 86, 87)], gjg [gjg$condition == "individualScope", 23]) --> <!-- corr.test(gjg[gjg$condition == "populationScope", c(46, 50, 86, 87)], gjg [gjg$condition == "populationScope", 23]) --> <!-- summary(lm(policyDV ~ condition + youResponsibility + indivResponsibility + --> <!-- manufacturerResponsibility + indivEfficacy + studyPolicyEfficacy + compassion --> <!-- + donatingMoral + manufacturerMoral + pol, gjg_noCombined)) --> <!-- names(gjg) --> <!-- gjg$condition --> <!-- cor_test(iris, "Sepal.Length", "Sepal.Width", partial = TRUE) --> <!-- cor(youResponsibility, manufacturerResponsibility) --> <!-- ``` --> <!-- # SPSP Plot --> <!-- ```{r} --> <!-- # First, let's gather the standardized variables into a long format --> <!-- gjg_z_long <- gjg_z %>% --> <!-- gather(key = "measure", value = "value", indivEfficacy, govResponsibility, indivResponsibility, manufacturerResponsibility, youResponsibility, compassion, studyPolicyEfficacy) --> <!-- library(forcats) # for fct_recode --> <!-- gjg_z_long <- gjg_z_long %>% --> <!-- mutate(measure = fct_recode(measure, --> <!-- "Companies" = "manufacturerResponsibility", --> <!-- "Self" = "youResponsibility", --> <!-- "US Gov." = "govResponsibility", --> <!-- "Individuals" = "indivResponsibility", --> <!-- "Donations" = "indivEfficacy", --> <!-- "Compassion" = "compassion", --> <!-- "Policy" = "studyPolicyEfficacy" --> <!-- )) --> <!-- gjg_z_long <- gjg_z_long %>% --> <!-- mutate(measure = factor(measure, levels = rev(c( --> <!-- "Compassion", --> <!-- "Self", --> <!-- "Individuals", --> <!-- "Companies", --> <!-- "US Gov.", --> <!-- "Donations", --> <!-- "Policy" --> <!-- )))) --> <!-- # Update the condition factor with new labels --> <!-- gjg_z_long$condition <- factor(gjg_z_long$condition, --> <!-- levels = rev(c("individualScope", "populationScope", "mergedScope")), --> <!-- labels = rev(c("Individual", "Structural", "Combined"))) --> <!-- # Custom colors --> <!-- library(RColorBrewer) --> <!-- set1_palette <- brewer.pal(n = 3, name = 'Set1') --> <!-- custom_colors <- c("Individual" = set1_palette[1], --> <!-- "Structural" = set1_palette[2], --> <!-- "Combined" = "#800080") --> <!-- # Custom shapes --> <!-- custom_shapes <- c("Individual" = 16, "Structural" = 15, "Combined" = 17) --> <!-- zplot <- ggplot(gjg_z_long, aes(x = measure, y = value, color = condition, shape = condition)) + --> <!-- stat_summary(fun = mean, geom = "point", position = position_dodge(width = 0.5), size = 3) + --> <!-- stat_summary(fun.data = mean_cl_normal, geom = "errorbar", --> <!-- position = position_dodge(width = 0.5), width = 0.2) + --> <!-- scale_color_manual(values = custom_colors) + --> <!-- scale_shape_manual(values = custom_shapes) + --> <!-- coord_flip() + # Use coord_flip to make the measures appear on the y-axis --> <!-- theme_minimal() + --> <!-- labs(x = "Measure", y = "Z-Score", title = "", --> <!-- color = "Narrative Frame", shape = "Narrative Frame") + --> <!-- theme( --> <!-- legend.position = "bottom", --> <!-- legend.text = element_text(size = 20), --> <!-- legend.title = element_text(size = 20), --> <!-- plot.title = element_text(size = 35, hjust = 0.5, face = "bold"), --> <!-- axis.title.y = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.title.x = element_text(face = "plain", size = 25, color = "black"), --> <!-- axis.text.x = element_text(face = "plain", size = 30), --> <!-- axis.text.y = element_text(face = "plain", size = 30), --> <!-- panel.border = element_rect(color = "black", fill = NA, size = 1), --> <!-- legend.key.size = unit(3, "lines") --> <!-- ) + --> <!-- theme(panel.spacing = unit(2, "lines")) --> <!-- # Print the plot --> <!-- print(zplot) --> <!-- ggsave("obesitys_mechs.png", plot = zplot, width = 17, height = 10) --> <!-- ``` --> <!-- ## Inferential Stats --> <!-- - Individual & Merged donate more than Structural --> <!-- ```{r} --> <!-- variables_to_standardize <- c("usGovHelp", "manufacturerHelp", "individualHelp", "youHelp", "usGovPrevent", "manufacturerPrevent", "individualPrevent", "youPrevent", "upset", "sympathetic", "touched", "donatingMoral", "manufacturerMoral", "manufacturerBlame", "individualBlame", "studyDonationEfficacy", "studyPolicyEfficacy", "donationEfficacy") --> <!-- gjg$condition <- relevel(gjg$condition, ref = "mergedScope") --> <!-- gjg_I <- gjg --> <!-- gjg_I$condition <- relevel(gjg_I$condition, ref = "individualScope") --> <!-- mod_donation<- lm(donation ~ condition, data = gjg) --> <!-- summary(mod_donation) --> <!-- confint(mod_punishingIndividualsEfficacy) --> <!-- confint(mod_punishingIndividualsEfficacy) --> <!-- confint(mod_punishingIndividualsEfficacy) --> <!-- confint(mod_punishingIndividualsEfficacy) --> <!-- mod_donationI <- lm(donation ~ condition, data = gjg_I) --> <!-- summary(mod_donationI) --> <!-- mod_policy<- lm(policyDV ~ condition, data = gjg) --> <!-- summary(mod_policy) --> <!-- mod_policyI <- lm(policyDV ~ condition, data = gjg_I) --> <!-- summary(mod_policyI) --> <!-- mod_phone <- lm(phoneYN ~ condition, data = gjg) --> <!-- summary(mod_phone) --> <!-- mod_phoneI <- lm(phoneYN ~ condition, data = gjg_I) --> <!-- summary(mod_phoneI) --> <!-- ``` --> <!-- # Bonus --> <!-- ```{r} --> <!-- bonus_gjg <- gjg_raw %>% filter(attentionCheck == 3, Finished == 1, consent == 8) --> <!-- bonus_gjg <- bonus_gjg %>% --> <!-- dplyr::select(PROLIFIC_PID, donation) --> <!-- view(bonus_gjg) --> <!-- winners_bonus_gjg <- bonus_gjg[seq(20, nrow(bonus_gjg), 20), ] --> <!-- # write.csv(winners_bonus_gjg, "BONUS_IS_obesitys2.csv", row.names = FALSE) --> <!-- ``` --> 
