Institutional Signaling Pilot 3
Marcus
7/11/2023
1 Setup
Libraries and functions
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",
"corrplot", "effects", "lme4", "sjPlot"
)
# 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) 1.1 Load Data
# read in data files
gjg <-read.csv("/Users/mtrenfield17/Desktop/Research/Boston College Research/Institutional Signaling/Study 3/IVS Action.csv")1.2 Functions
plot_cooker <- function(data, iv, dv, title) {
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("") +
ggtitle(title)
ggpar(part1, legend = "none")
}
wrapped_plot_cooker <- function(data, iv, dv, title, facet_var) {
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,
size = 1.5, color = "black") +
theme_classic() +
xlab("") +
ylab("") +
ggtitle(title) +
facet_wrap(vars({{facet_var}}))
ggpar(part1, legend = "none")
}
by_line <- function(data, iv, dv, x_label, y_label, color_label, plot_title) {
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 = 0.2) +
labs(x = x_label, y = y_label, color = color_label, title = plot_title)
}1.3 Reshaping data
#### filtering people who failed the attn check ####
filtered_gjg <- gjg %>% filter(attentionCheck == 3 | attentionCheck == 7)
#### make dataset long ####
gjg_long <- filtered_gjg %>% gather(stim, resp, "B_C_Chic_staticNorm_18":"C_C_Star_riskInact")
gjg_long<-gjg_long %>%
separate(stim, into= c("social_issue", "action", "company", "DV"), sep="_")
## shift dataset back to wide format ##
gjg_long <- spread(gjg_long, DV, resp)
gjg_long <- gjg_long %>% mutate_at(c("dyNorm", "noisyMin", "OComp", "OLike", "OMotivate", "OTrust", "peerPress", "Profit", "pubPress", "Rep", "riskInact", "SComp", "SGen", "SImport", "SLike", "SRelImport", "staticNorm", "STrust", "surprise","post_Real_Check", "age", "prolificID", "area", "pol", "edu", "gen", "inc"), as.numeric)
## Rename Vignettes
gjg_long <- gjg_long %>%
mutate(companyLeaning = case_when(
company == "Bud" ~ "R",
company == "CAR" ~ "R",
company == "Chic" ~ "R",
company == "Dis" ~ "L",
company == "Pop" ~ "L",
company == "Star" ~ "L",
company == "Tar" ~ "L",
company == "Wal" ~ "R",
company == "WNBA" ~ "L",
company == "Fox" ~ "R",
company == "NBC" ~ "L",
company == "NBA" ~ "L",
company == "NFL" ~ "R",
TRUE ~ company # if none of the above conditions are met, keep the original value
))
gjg_long$condition <- paste0(gjg_long$companyLeaning, gjg_long$action)
gjg_long <- gjg_long %>%
mutate(condition = case_when(
condition == "CC" ~ "Conservative \n\ Progressive Mess.",
condition == "CI" ~ "Liberal \n\ Control",
condition == "RS" ~ "Conservative \n\ Progressive Mess.",
condition == "RN" ~ "Conservative \n\ Control",
TRUE ~ condition # if none of the above conditions are met, keep the original value
))
gjg_long <- gjg_long %>%
mutate(companyLeaning = case_when(
companyLeaning == "R" ~ "Right",
companyLeaning == "L" ~ "Left",
TRUE ~ companyLeaning # if none of the above conditions are met, keep the original value
))
gjg_long <- gjg_long %>%
mutate(company = case_when(
company == "Bud" ~ "Budweiser",
company == "CAR" ~ "NASCAR",
company == "Chic" ~ "Chick-fil-A",
company == "Dis" ~ "Disney",
company == "Pop" ~ "Popeyes",
company == "Star" ~ "Starbucks",
company == "Tar" ~ "Target",
company == "Wal" ~ "Walmart",
TRUE ~ company # if none of the above conditions are met, keep the original value
))
## Rename Conditions
gjg_long$signal <- as.factor(gjg_long$action)
gjg_long <- gjg_long %>%
mutate(signal = case_when(
action == "C" ~ "Consistent",
action == "I" ~ "Inconsistent",
TRUE ~ signal # if none of the above conditions are met, keep the original value
))
gjg_long$social_issue <- as.factor(gjg_long$social_issue)
gjg_long <- gjg_long %>%
mutate(social_issue = case_when(
social_issue == "T" ~ "Transgendered Representation",
social_issue == "C" ~ "Climate Change",
social_issue == "L" ~ "LGBTQ Representation",
social_issue == "B" ~ "BLM",
TRUE ~ social_issue # if none of the above conditions are met, keep the original value
))
## filter out rows for condition-vignette pairs people DON'T see
gjg_long <- gjg_long %>% filter(is.na(dyNorm) == FALSE)2 Attention Check
gjg %>%
group_by(consent) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = n / sum(n))
gjg %>%
group_by(attentionCheck) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = n / sum(n))22 people did not accept the consent.
2/801 people failed the attention check.
3 Demographics
# Subset your data frame to include only the demographic columns
demo_gjg <- gjg[, c("gen_TEXT", "race_TEXT", "inc_TEXT", "edu_TEXT", "pol_TEXT", "pid_TEXT", "area_TEXT")]
# Age
mean(gjg$age, na.rm=TRUE)## [1] 42.80025sd(gjg$age, na.rm=TRUE)## [1] 14.16845# 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]])
}
}##
## Table of frequencies for gen_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 I identify as: 13 1.58
## 3 Man 396 48.12
## 4 Woman 392 47.63
##
## Table of frequencies for race_TEXT :
## Var1
## 1
## 2 American Indian and Native Alaskan
## 3 American Indian and Native Alaskan,Black
## 4 American Indian and Native Alaskan,Black,East Asian,South Asian,Southeast Asian,Pacific Islander or Native Hawaiian,Hispanic or Latino/a/x,Middle Eastern and North African,White
## 5 American Indian and Native Alaskan,Black,Hispanic or Latino/a/x
## 6 American Indian and Native Alaskan,Black,White
## 7 American Indian and Native Alaskan,Hispanic or Latino/a/x
## 8 American Indian and Native Alaskan,White
## 9 Black
## 10 Black,East Asian,Hispanic or Latino/a/x,White
## 11 Black,Hispanic or Latino/a/x,White
## 12 Black,White
## 13 East Asian
## 14 East Asian,Middle Eastern and North African
## 15 East Asian,Southeast Asian
## 16 East Asian,White
## 17 Hispanic or Latino/a/x
## 18 Hispanic or Latino/a/x,White
## 19 Middle Eastern and North African
## 20 Middle Eastern and North African,White
## 21 South Asian
## 22 South Asian,Pacific Islander or Native Hawaiian,White
## 23 Southeast Asian
## 24 Southeast Asian,Pacific Islander or Native Hawaiian,White
## 25 Southeast Asian,White
## 26 White
## Freq Percent
## 1 22 2.67
## 2 1 0.12
## 3 2 0.24
## 4 1 0.12
## 5 1 0.12
## 6 3 0.36
## 7 1 0.12
## 8 8 0.97
## 9 64 7.78
## 10 1 0.12
## 11 3 0.36
## 12 8 0.97
## 13 22 2.67
## 14 2 0.24
## 15 1 0.12
## 16 7 0.85
## 17 27 3.28
## 18 20 2.43
## 19 3 0.36
## 20 3 0.36
## 21 9 1.09
## 22 1 0.12
## 23 12 1.46
## 24 1 0.12
## 25 4 0.49
## 26 596 72.42
##
## Table of frequencies for inc_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 $100,000 - $149,999 107 13.00
## 3 $150,000 - $199,999 46 5.59
## 4 $25,000 - $49,999 205 24.91
## 5 $50,000 - $74,999 165 20.05
## 6 $75,000 - $99,999 131 15.92
## 7 less than $25,000 128 15.55
## 8 more than $200,000 19 2.31
##
## Table of frequencies for edu_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 Bachelor's degree 279 33.90
## 3 Graduate degree (Masters, PhD, etc) 111 13.49
## 4 High school diploma or GED 136 16.52
## 5 Some college, Technical degree, or Associates degree 271 32.93
## 6 Some schooling, but no high school diploma or degree 4 0.49
##
## Table of frequencies for pol_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 Conservative 142 17.25
## 3 Liberal 110 13.37
## 4 Moderate 187 22.72
## 5 Somewhat Conservative 92 11.18
## 6 Somewhat Liberal 94 11.42
## 7 Very Conservative 65 7.90
## 8 Very Liberal 111 13.49
##
## Table of frequencies for pid_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 Democrat 298 36.21
## 3 Independent / Other 255 30.98
## 4 Republican 248 30.13
##
## Table of frequencies for area_TEXT :
## Var1 Freq Percent
## 1 22 2.67
## 2 Rural 157 19.08
## 3 Suburban 439 53.34
## 4 Urban 205 24.914 Correlation
DVs <- gjg_long[c("staticNorm", "dyNorm", "OMotivate", "surprise", "SImport", "SRelImport", "SGen", "Profit", "Rep", "noisyMin", "peerPress", "pubPress", "riskInact", "SLike", "STrust", "SComp", "OLike", "OTrust", "OComp", "post_Real_Check", "age", "pol", "edu", "gen", "inc")]
# Compute pairwise correlations
corr_DVs <- cor(DVs, use = "complete.obs")
colnames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
rownames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
# Plot the correlation matrix
corrplot(corr_DVs, is.corr = TRUE, type = "full", method = "circle", tl.cex = 0.6, insig = "label_sig", diag = FALSE)
4.1 Correlation for liberal participants
gjg_polLib <- gjg_long %>% filter(pol > 4)
DVs <- gjg_polLib[c("staticNorm", "dyNorm", "OMotivate", "surprise", "SImport", "SRelImport", "SGen", "Profit", "Rep", "noisyMin", "peerPress", "pubPress", "riskInact", "SLike", "STrust", "SComp", "OLike", "OTrust", "OComp", "post_Real_Check", "age", "pol", "edu", "gen", "inc")]
# Compute pairwise correlations
corr_DVs <- cor(DVs, use = "complete.obs")
colnames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
rownames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
# Plot the correlation matrix
corrplot(corr_DVs, is.corr = TRUE, type = "full", method = "circle", tl.cex = 0.5, insig = "label_sig", diag = FALSE)
4.2 Correlation for conservative participants
gjg_polCon <- gjg_long %>% filter(pol < 4)
DVs <- gjg_polCon[c("staticNorm", "dyNorm", "OMotivate", "surprise", "SImport", "SRelImport", "SGen", "Profit", "Rep", "noisyMin", "peerPress", "pubPress", "riskInact", "SLike", "STrust", "SComp", "OLike", "OTrust", "OComp", "post_Real_Check", "age", "pol", "edu", "gen", "inc")]
# Compute pairwise correlations
corr_DVs <- cor(DVs, use = "complete.obs")
colnames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
rownames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "political leaning", "education", "gender", "income")
# Plot the correlation matrix
corrplot(corr_DVs, is.corr = TRUE, type = "full", method = "circle", tl.cex = 0.5, insig = "label_sig", diag = FALSE)
4.3 Correlation for moderate participants
gjg_polMod <- gjg_long %>% filter(pol == 4)
DVs <- gjg_polMod[c("staticNorm", "dyNorm", "OMotivate", "surprise", "SImport", "SRelImport", "SGen", "Profit", "Rep", "noisyMin", "peerPress", "pubPress", "riskInact", "SLike", "STrust", "SComp", "OLike", "OTrust", "OComp", "post_Real_Check", "age", "edu", "gen", "inc")]
# Compute pairwise correlations
corr_DVs <- cor(DVs, use = "complete.obs")
colnames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "education", "gender", "income")
rownames(corr_DVs) <- c("static norm", "dynamic norm", "Post motivates others", "Surprise", "I think cause is important", "How relatively important I think cause is", "I think company is genuine", "Reputation Motive", "Profit Motive", "Noisy Minority Motive", "Pressure from Peer organizations motive", "Pressure from public motive", "risky NOT to signal", "I like company", "I trust company", "I think company is competent", "Others like company", "Others trust company", "Others think company is competent", "belief in manipulation", "age", "education", "gender", "income")
# Plot the correlation matrix
corrplot(corr_DVs, is.corr = TRUE, type = "full", method = "circle", tl.cex = 0.5, insig = "label_sig", diag = FALSE)
5 EFA
5.1 Evaluating the correlation matrix
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)
efaDVs <- factorDVs5.2 Determining number of factors
Scree plot suggests 5-6 factors
Parallel Analysis suggests 6
eigenvalue method suggests 5
## evaluating data
corr_DVs = cor(efaDVs, use="complete.obs")
KMO(efaDVs)
cortest.bartlett(efaDVs) #tests correlations between variables
##
ev<-eigen(corr_DVs) #gets eigenvalues (variance explained by each component)
ev$values
scree(efaDVs, pc = FALSE) #number of factors until plot levels off
fa.parallel(efaDVs, fa="fa") #checks eigenvalues of factors against eigenvalues of identity (no correlation) matrix
dat_fa <- na.omit(DVs)5.3 5 Factors
fit <- factanal(na.omit(efaDVs), factors=5, rotation="promax", scores = "regression")
print(fit, digits = 2, cutoff = .4, sort = TRUE)
loads <- fit$loadings
fa.diagram(loads)
## Cronbach's Alpha
f1 <- efaDVs[ , c("STrust", "SLike", "SComp", "SGen", "OMotivate")]
f2 <- efaDVs[ , c("SImport", "SRelImport", "staticNorm", "noisyMin")]
f3 <- efaDVs[ , c("OTrust", "OComp", "OLike")]
f4 <- efaDVs[ , c("peerPress", "pubPress", "riskInact", "surprise")]
f5 <- efaDVs[ , c("Rep", "Profit")]
alpha(f1)
alpha(f2)
alpha(f3)
alpha(f4)
alpha(f5)5.4 6 Factors (Going with 6 factor)
fit <- factanal(na.omit(efaDVs), factors=6, rotation="promax", scores = "regression")
print(fit, digits = 2, cutoff = .4, sort = TRUE)
loads <- fit$loadings
fa.diagram(loads)
## Cronbach's Alpha
f1 <- efaDVs[ , c("STrust", "SLike", "SComp", "SGen")]
f2 <- efaDVs[ , c("OTrust", "OComp", "OLike")]
f3 <- efaDVs[ , c("SImport", "SRelImport", "staticNorm", "noisyMin")]
f4 <- efaDVs[ , c("peerPress", "pubPress", "riskInact")]
f5 <- efaDVs[ , c("Rep", "Profit")]
f6 <- efaDVs[ , c("dyNorm", "OMotivate", "SGen")]
alpha(f1)
alpha(f2)
alpha(f3)
alpha(f4)
alpha(f5)
alpha(f6)6 Plots + Inferential Stats
6.1 All Plots
plot_list <- list(plot_cooker(gjg_long, condition, staticNorm, "Percent of support for issue"),
plot_cooker(gjg_long, condition, dyNorm, "Change in support for issue"),
plot_cooker(gjg_long, condition, SImport, "I think cause is important"),
plot_cooker(gjg_long, condition, SRelImport, "I think cause is important relative to other causes"),
plot_cooker(gjg_long, condition, OMotivate, "Message motivates others to act"),
plot_cooker(gjg_long, condition, surprise, "Surprise"),
plot_cooker(gjg_long, condition, SGen, "I think company is genuine"),
plot_cooker(gjg_long, condition, Rep, "Reputation motive"),
plot_cooker(gjg_long, condition, Profit, "Profit motive"),
plot_cooker(gjg_long, condition, noisyMin, "Motive to cater to noisy minority"),
plot_cooker(gjg_long, condition, peerPress, "Pressured by peer companies"),
plot_cooker(gjg_long, condition, pubPress, "Pressured by public"),
plot_cooker(gjg_long, condition, riskInact, "Risky NOT to send signal"),
plot_cooker(gjg_long, condition, SLike, "I like company"),
plot_cooker(gjg_long, condition, STrust, "I trust company"),
plot_cooker(gjg_long, condition, SComp, "I think company is competent"),
plot_cooker(gjg_long, condition, OLike, "Others like company"),
plot_cooker(gjg_long, condition, OTrust, "Others trust company"),
plot_cooker(gjg_long, condition, OComp, "Others think company is competent")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 5, nrow = 4)
plot_arranged
6.2 Factor Plots + Tests
6.2.1 Creating Factors
# Factor 1
gjg_long$selfPercep_z <- rowMeans(scale(gjg_long[, c("STrust", "SLike", "SComp", "SGen")], center = TRUE, scale = TRUE), na.rm = TRUE)
# Factor 2
gjg_long$otherPercep_z <- rowMeans(scale(gjg_long[, c("OTrust", "OComp", "OLike")], center = TRUE, scale = TRUE), na.rm = TRUE)
# Factor 3
gjg_long$noisyMin_R <- max(gjg_long$noisyMin) + min(gjg_long$noisyMin) - gjg_long$noisyMin
gjg_long$issueImport_z <- rowMeans(scale(gjg_long[, c("SImport", "SRelImport", "staticNorm", "noisyMin_R")], center = TRUE, scale = TRUE), na.rm = TRUE)
# Factor 4
gjg_long$externalMotive_z <- rowMeans(scale(gjg_long[, c("peerPress", "pubPress", "riskInact")], center = TRUE, scale = TRUE), na.rm = TRUE)
# Factor 5
gjg_long$selfishMotive_z <- rowMeans(scale(gjg_long[, c("Rep", "Profit")], center = TRUE, scale = TRUE), na.rm = TRUE)
# Factor 6
gjg_long$driveAction_z <- rowMeans(scale(gjg_long[, c("dyNorm", "OMotivate", "SGen")], center = TRUE, scale = TRUE), na.rm = TRUE)6.2.2 All factor plot
plot_list <- list(plot_cooker(gjg_long, condition, selfPercep_z, "Perception of company"),
plot_cooker(gjg_long, condition, otherPercep_z, "Others' perception of company"),
plot_cooker(gjg_long, condition, issueImport_z, "Importance of cause"),
plot_cooker(gjg_long, condition, externalMotive_z, "External Motives"),
plot_cooker(gjg_long, condition, selfishMotive_z, "Selfish Motives"),
plot_cooker(gjg_long, condition, driveAction_z, "Motivate Others to Act")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 3, nrow = 2)
plot_arranged
6.2.3 Tests
There is a main effect of company leaning and consistency for all factors ASIDE for factor 3: issue importance
There is an interact effect for factors: selfish motivation (5) and motivating others to act (6)
Other perception (2) has a main effect of participants political leaning (liberals believe others perceive companies more positively)
Issue importance (3) only has a main effect for political leaning
Drive others to act has a marginal main effect of participants political leaning (liberals think they messages are more driving)
# Factor 1
mod_selfPercep <- lmer(selfPercep_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue) + (1 | company), data = gjg_long)
summary(mod_selfPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: selfPercep_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue) + (1 | company)
## Data: gjg_long
##
## REML criterion at convergence: 7376.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0907 -0.5969 0.0485 0.6017 3.8752
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.280153 0.5293
## company (Intercept) 0.030391 0.1743
## social_issue (Intercept) 0.008135 0.0902
## Residual 0.418201 0.6467
## Number of obs: 3196, groups: participantID, 799; company, 13; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.31997 0.08537 13.08367 3.748 0.00241
## companyLeaningRight -0.26845 0.10257 11.85057 -2.617 0.02270
## actionI -0.28900 0.03280 2391.00000 -8.810 < 2e-16
## companyLeaningRight:actionI -0.01234 0.04607 2392.09199 -0.268 0.78880
##
## (Intercept) **
## companyLeaningRight *
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.560
## actionI -0.186 0.155
## cmpnyLnnR:I 0.132 -0.221 -0.712mod_pol_selfPercep <- lmer(selfPercep_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue) + + (1 | company), data = gjg_long)
summary(mod_pol_selfPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: selfPercep_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue) + +(1 | company)
## Data: gjg_long
##
## REML criterion at convergence: 7380.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0889 -0.5955 0.0466 0.5979 3.8854
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.279386 0.52857
## company (Intercept) 0.030402 0.17436
## social_issue (Intercept) 0.008147 0.09026
## Residual 0.418171 0.64666
## Number of obs: 3196, groups: participantID, 799; company, 13; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.23967 0.09809 22.71076 2.443 0.0227
## companyLeaningRight -0.26855 0.10259 11.84900 -2.618 0.0227
## actionI -0.28890 0.03280 2391.28743 -8.808 <2e-16
## pol 0.01962 0.01179 792.00314 1.664 0.0965
## companyLeaningRight:actionI -0.01243 0.04606 2392.30546 -0.270 0.7872
##
## (Intercept) *
## companyLeaningRight *
## actionI ***
## pol .
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.487
## actionI -0.163 0.155
## pol -0.492 -0.001 0.002
## cmpnyLnnR:I 0.116 -0.221 -0.712 -0.001# Factor 2
mod_otherPercep <- lmer(otherPercep_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_otherPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: otherPercep_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7940.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9033 -0.4244 0.1238 0.5621 3.3713
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.322207 0.56763
## social_issue (Intercept) 0.004554 0.06749
## Residual 0.508626 0.71318
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.18057 0.04667 8.41497 3.869 0.00431
## companyLeaningRight -0.23097 0.03568 2389.74688 -6.473 1.16e-10
## actionI -0.10807 0.03595 2393.55912 -3.006 0.00267
## companyLeaningRight:actionI -0.02625 0.05064 2393.94294 -0.518 0.60421
##
## (Intercept) **
## companyLeaningRight ***
## actionI **
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.382
## actionI -0.380 0.496
## cmpnyLnnR:I 0.269 -0.705 -0.710mod_pol_otherPercep <- lmer(otherPercep_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_otherPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: otherPercep_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7918.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9612 -0.4278 0.1227 0.5633 3.4399
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.306707 0.55381
## social_issue (Intercept) 0.004581 0.06768
## Residual 0.508620 0.71318
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.09821 0.06927 39.48374 -1.418 0.16410
## companyLeaningRight -0.23097 0.03568 2389.80325 -6.473 1.16e-10
## actionI -0.10800 0.03595 2393.63375 -3.004 0.00269
## pol 0.06806 0.01253 796.69911 5.433 7.35e-08
## companyLeaningRight:actionI -0.02632 0.05064 2393.97917 -0.520 0.60328
##
## (Intercept)
## companyLeaningRight ***
## actionI **
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.258
## actionI -0.256 0.496
## pol -0.741 0.000 0.001
## cmpnyLnnR:I 0.182 -0.705 -0.710 0.000# Factor 3
mod_issueImport <- lmer(issueImport_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_issueImport)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: issueImport_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 5732.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3064 -0.5384 0.0074 0.5456 3.7772
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.3625 0.6021
## social_issue (Intercept) 0.1160 0.3406
## Residual 0.2068 0.4547
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.964e-02 1.724e-01 3.135e+00 0.230 0.832
## companyLeaningRight -1.406e-02 2.275e-02 2.391e+03 -0.618 0.537
## actionI -2.566e-05 2.295e-02 2.391e+03 -0.001 0.999
## companyLeaningRight:actionI -4.817e-02 3.230e-02 2.391e+03 -1.491 0.136
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.066
## actionI -0.065 0.496
## cmpnyLnnR:I 0.046 -0.704 -0.710mod_pol_issueImport <- lmer(issueImport_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_issueImport)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: issueImport_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 5273.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3415 -0.5494 0.0014 0.5405 3.6240
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.1793 0.4234
## social_issue (Intercept) 0.1151 0.3392
## Residual 0.2068 0.4547
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.027e-01 1.751e-01 3.391e+00 -5.156 0.0104
## companyLeaningRight -1.405e-02 2.275e-02 2.390e+03 -0.618 0.5368
## actionI -5.538e-04 2.295e-02 2.392e+03 -0.024 0.9807
## pol 2.300e-01 9.139e-03 7.964e+02 25.170 <2e-16
## companyLeaningRight:actionI -4.765e-02 3.230e-02 2.391e+03 -1.475 0.1403
##
## (Intercept) *
## companyLeaningRight
## actionI
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.065
## actionI -0.065 0.496
## pol -0.214 0.000 0.001
## cmpnyLnnR:I 0.046 -0.704 -0.710 0.000# Factor 4
mod_externalMotive <- lmer(externalMotive_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_externalMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: externalMotive_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 6801.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4879 -0.5784 0.0138 0.5981 3.3897
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.27151 0.5211
## social_issue (Intercept) 0.01088 0.1043
## Residual 0.34078 0.5838
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.16495 0.05905 4.55886 -2.793 0.0424
## companyLeaningRight 0.08706 0.02921 2390.91182 2.981 0.0029
## actionI 0.26743 0.02945 2394.36842 9.081 <2e-16
## companyLeaningRight:actionI -0.04387 0.04147 2392.78575 -1.058 0.2901
##
## (Intercept) *
## companyLeaningRight **
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.247
## actionI -0.245 0.496
## cmpnyLnnR:I 0.174 -0.704 -0.710mod_pol_externalMotive <- lmer(externalMotive_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_externalMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## externalMotive_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 6808.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4883 -0.5794 0.0137 0.5977 3.3892
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.27195 0.5215
## social_issue (Intercept) 0.01088 0.1043
## Residual 0.34078 0.5838
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.613e-01 7.520e-02 1.195e+01 -2.145 0.0532
## companyLeaningRight 8.706e-02 2.921e-02 2.391e+03 2.981 0.0029
## actionI 2.674e-01 2.945e-02 2.394e+03 9.081 <2e-16
## pol -8.876e-04 1.136e-02 7.969e+02 -0.078 0.9378
## companyLeaningRight:actionI -4.387e-02 4.147e-02 2.393e+03 -1.058 0.2901
##
## (Intercept) .
## companyLeaningRight **
## actionI ***
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.194
## actionI -0.193 0.496
## pol -0.619 0.000 0.001
## cmpnyLnnR:I 0.137 -0.704 -0.710 0.000# Factor 5
mod_selfishMotive <- lmer(selfishMotive_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_selfishMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: selfishMotive_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7718.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5209 -0.4509 0.1066 0.5613 3.3803
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.36148 0.60123
## social_issue (Intercept) 0.00581 0.07622
## Residual 0.45474 0.67435
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.17029 0.04974 7.08834 -3.424 0.01087
## companyLeaningRight 0.10818 0.03374 2390.38742 3.206 0.00136
## actionI 0.30551 0.03400 2394.70903 8.985 < 2e-16
## companyLeaningRight:actionI -0.12443 0.04789 2393.74615 -2.598 0.00943
##
## (Intercept) *
## companyLeaningRight **
## actionI ***
## companyLeaningRight:actionI **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.339
## actionI -0.337 0.496
## cmpnyLnnR:I 0.239 -0.704 -0.710plot_cooker(gjg_long, condition, selfishMotive_z, "Selfish Motives")
mod_selfishMotive <- lmer(selfishMotive_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_selfishMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## selfishMotive_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7725
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5148 -0.4517 0.1049 0.5622 3.3849
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.361969 0.60164
## social_issue (Intercept) 0.005815 0.07625
## Residual 0.454744 0.67435
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.19302 0.07323 32.65158 -2.636 0.01273
## companyLeaningRight 0.10818 0.03374 2390.39450 3.206 0.00136
## actionI 0.30552 0.03400 2394.70876 8.985 < 2e-16
## pol 0.00555 0.01311 796.92137 0.423 0.67227
## companyLeaningRight:actionI -0.12444 0.04789 2393.74784 -2.598 0.00942
##
## (Intercept) *
## companyLeaningRight **
## actionI ***
## pol
## companyLeaningRight:actionI **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.230
## actionI -0.229 0.496
## pol -0.734 0.000 0.001
## cmpnyLnnR:I 0.163 -0.704 -0.710 0.000# Factor 6
mod_driveAction <- lmer(driveAction_z ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_driveAction)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: driveAction_z ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 6131.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5246 -0.5641 0.0433 0.5961 2.8869
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.25197 0.5020
## social_issue (Intercept) 0.01406 0.1186
## Residual 0.26725 0.5170
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.26553 0.06454 4.00576 4.114 0.0146
## companyLeaningRight -0.21552 0.02586 2390.56912 -8.333 <2e-16
## actionI -0.25916 0.02608 2393.35463 -9.936 <2e-16
## companyLeaningRight:actionI -0.08032 0.03672 2391.98099 -2.187 0.0288
##
## (Intercept) *
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.200
## actionI -0.199 0.496
## cmpnyLnnR:I 0.141 -0.704 -0.710plot_cooker(gjg_long, condition, driveAction_z, "Motivate Others to Act")
mod_pol_driveAction <- lmer(driveAction_z ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_driveAction)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: driveAction_z ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 6135.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5240 -0.5593 0.0470 0.5963 2.8957
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.25107 0.5011
## social_issue (Intercept) 0.01408 0.1187
## Residual 0.26725 0.5170
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.18622 0.07809 8.54613 2.385 0.0423
## companyLeaningRight -0.21552 0.02586 2390.58405 -8.333 <2e-16
## actionI -0.25913 0.02608 2393.36640 -9.935 <2e-16
## pol 0.01936 0.01072 796.87577 1.806 0.0713
## companyLeaningRight:actionI -0.08035 0.03672 2391.99420 -2.188 0.0288
##
## (Intercept) *
## companyLeaningRight ***
## actionI ***
## pol .
## companyLeaningRight:actionI *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.166
## actionI -0.165 0.496
## pol -0.562 0.000 0.001
## cmpnyLnnR:I 0.117 -0.704 -0.710 0.000plot_cooker(gjg_long, condition, driveAction_z, "Motivate Others to Act")
6.3 Factor SubPlots + Tests
6.3.1 Factor 1: Perception of Companies
plot_list <- list(
plot_cooker(gjg_long, condition, SLike, "I like company"),
plot_cooker(gjg_long, condition, STrust, "I trust company"),
plot_cooker(gjg_long, condition, SComp, "I think company is competent"),
plot_cooker(gjg_long, condition, SGen, "I think company is genuine")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 2)
plot_arranged
6.3.1.1 Factor 1 Items Tests
All main effects, no interaction effects
Signficant main effect of participant political leaning for competence (but not warmth)
# Like
mod_like <- lmer(SLike ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_like)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SLike ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12660.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.65444 -0.69809 0.07469 0.69303 2.98340
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.93225 0.9655
## social_issue (Intercept) 0.04603 0.2145
## Residual 2.42198 1.5563
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.47211 0.12532 4.88688 35.686 4.25e-07
## companyLeaningRight -0.61112 0.07786 2390.93718 -7.849 6.29e-15
## actionI -0.35495 0.07849 2395.61999 -4.522 6.41e-06
## companyLeaningRight:actionI -0.02653 0.11053 2393.76447 -0.240 0.81
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.311
## actionI -0.308 0.496
## cmpnyLnnR:I 0.219 -0.704 -0.710mod_pol_like <- lmer(SLike ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_like)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SLike ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12663.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.63384 -0.70177 0.06935 0.69054 3.01288
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.92904 0.9639
## social_issue (Intercept) 0.04612 0.2148
## Residual 2.42197 1.5563
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.31445 0.15822 12.33290 27.268 2.13e-12
## companyLeaningRight -0.61112 0.07786 2390.95136 -7.849 6.28e-15
## actionI -0.35485 0.07849 2395.62436 -4.521 6.45e-06
## pol 0.03849 0.02356 797.04458 1.634 0.103
## companyLeaningRight:actionI -0.02662 0.11053 2393.77172 -0.241 0.810
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.246
## actionI -0.245 0.496
## pol -0.610 0.000 0.001
## cmpnyLnnR:I 0.174 -0.704 -0.710 -0.001# Trust
mod_pol_trust <- lmer(STrust ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_trust)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: STrust ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12085.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0243 -0.6300 0.0331 0.6277 3.3431
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.17684 1.0848
## social_issue (Intercept) 0.02828 0.1682
## Residual 1.86341 1.3651
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.11158 0.10428 5.99278 39.429 1.81e-08
## companyLeaningRight -0.45203 0.06830 2390.95968 -6.619 4.46e-11
## actionI -0.45434 0.06884 2395.13970 -6.600 5.04e-11
## companyLeaningRight:actionI -0.04612 0.09695 2393.88568 -0.476 0.634
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.327
## actionI -0.325 0.496
## cmpnyLnnR:I 0.231 -0.704 -0.710mod_like <- lmer(SLike ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_like)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SLike ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12663.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.63384 -0.70177 0.06935 0.69054 3.01288
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.92904 0.9639
## social_issue (Intercept) 0.04612 0.2148
## Residual 2.42197 1.5563
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.31445 0.15822 12.33290 27.268 2.13e-12
## companyLeaningRight -0.61112 0.07786 2390.95136 -7.849 6.28e-15
## actionI -0.35485 0.07849 2395.62436 -4.521 6.45e-06
## pol 0.03849 0.02356 797.04458 1.634 0.103
## companyLeaningRight:actionI -0.02662 0.11053 2393.77172 -0.241 0.810
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.246
## actionI -0.245 0.496
## pol -0.610 0.000 0.001
## cmpnyLnnR:I 0.174 -0.704 -0.710 -0.001# Competence
mod_comp <- lmer(SComp ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_comp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SComp ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0950 -0.5663 0.1048 0.6211 3.2020
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.97727 0.9886
## social_issue (Intercept) 0.04371 0.2091
## Residual 1.81414 1.3469
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.902e+00 1.201e-01 4.663e+00 40.822 3.88e-07
## companyLeaningRight -5.221e-01 6.739e-02 2.391e+03 -7.747 1.38e-14
## actionI -3.571e-01 6.794e-02 2.395e+03 -5.257 1.59e-07
## companyLeaningRight:actionI -7.286e-03 9.567e-02 2.393e+03 -0.076 0.939
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.281
## actionI -0.278 0.496
## cmpnyLnnR:I 0.198 -0.704 -0.710mod_pol_comp <- lmer(SComp ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_comp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SComp ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11902.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1095 -0.5625 0.1187 0.6204 3.1957
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.95165 0.9755
## social_issue (Intercept) 0.04391 0.2095
## Residual 1.81413 1.3469
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.538e+00 1.515e-01 1.169e+01 29.945 2.05e-12
## companyLeaningRight -5.221e-01 6.739e-02 2.391e+03 -7.747 1.38e-14
## actionI -3.570e-01 6.794e-02 2.395e+03 -5.255 1.62e-07
## pol 8.892e-02 2.254e-02 7.970e+02 3.945 8.69e-05
## companyLeaningRight:actionI -7.454e-03 9.567e-02 2.393e+03 -0.078 0.938
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.222
## actionI -0.221 0.496
## pol -0.609 0.000 0.001
## cmpnyLnnR:I 0.157 -0.704 -0.710 0.000mod_pol_comp <- lmer(SComp ~ companyLeaning*action*pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_comp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SComp ~ companyLeaning * action * pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11776.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2216 -0.5729 0.1145 0.6151 3.2878
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.97726 0.9886
## social_issue (Intercept) 0.04575 0.2139
## Residual 1.71222 1.3085
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.76764 0.17648 19.99491 21.349
## companyLeaningRight 0.30064 0.15857 2388.67129 1.896
## actionI 0.20092 0.15877 2389.55160 1.265
## pol 0.27695 0.03120 2286.11190 8.875
## companyLeaningRight:actionI 0.31451 0.22455 2389.46190 1.401
## companyLeaningRight:pol -0.20085 0.03526 2388.84612 -5.697
## actionI:pol -0.13548 0.03521 2388.21100 -3.847
## companyLeaningRight:actionI:pol -0.07930 0.04985 2388.67640 -1.591
## Pr(>|t|)
## (Intercept) 3.11e-15 ***
## companyLeaningRight 0.058077 .
## actionI 0.205839
## pol < 2e-16 ***
## companyLeaningRight:actionI 0.161459
## companyLeaningRight:pol 1.37e-08 ***
## actionI:pol 0.000123 ***
## companyLeaningRight:actionI:pol 0.111797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol cmLR:I cmpLR: actnI:
## cmpnyLnngRg -0.449
## actionI -0.449 0.501
## pol -0.724 0.515 0.514
## cmpnyLnnR:I 0.318 -0.707 -0.709 -0.364
## cmpnyLnngR: 0.409 -0.911 -0.457 -0.565 0.645
## actionI:pol 0.409 -0.456 -0.909 -0.564 0.644 0.500
## cmpnyLnR:I: -0.289 0.644 0.644 0.399 -0.910 -0.707 -0.708# Genuinely care about cause
mod_gen <- lmer(SGen ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## SGen ~ companyLeaning * action + (1 | participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12007.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10336 -0.65538 -0.01925 0.65306 3.03902
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.05521 1.0272
## social_issue (Intercept) 0.02946 0.1716
## Residual 1.85185 1.3608
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.59481 0.10490 5.72893 43.803 1.85e-08
## companyLeaningRight -0.80761 0.06808 2390.82664 -11.862 < 2e-16
## actionI -0.89976 0.06862 2395.22969 -13.111 < 2e-16
## companyLeaningRight:actionI -0.02131 0.09665 2393.77087 -0.221 0.825
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.325
## actionI -0.322 0.496
## cmpnyLnnR:I 0.229 -0.704 -0.710mod_pol_gen <- lmer(SGen ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_gen)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SGen ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12013.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10512 -0.65475 -0.01726 0.65262 3.03994
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.05707 1.0281
## social_issue (Intercept) 0.02947 0.1717
## Residual 1.85185 1.3608
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.581e+00 1.422e-01 1.922e+01 32.205 <2e-16
## companyLeaningRight -8.076e-01 6.808e-02 2.391e+03 -11.862 <2e-16
## actionI -8.998e-01 6.863e-02 2.395e+03 -13.111 <2e-16
## pol 3.461e-03 2.344e-02 7.970e+02 0.148 0.883
## companyLeaningRight:actionI -2.132e-02 9.665e-02 2.394e+03 -0.221 0.825
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.239
## actionI -0.238 0.496
## pol -0.675 0.000 0.001
## cmpnyLnnR:I 0.169 -0.704 -0.710 0.0006.3.2 Factor 2: Others’ Perception of Companies
plot_list <- list(
plot_cooker(gjg_long, condition, OLike, "Others like company"),
plot_cooker(gjg_long, condition, OTrust, "Others trust company"),
plot_cooker(gjg_long, condition, OComp, "Others think company is competent")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 2)
plot_arranged
by_line(gjg_long, pol, OLike, "political leaning", " ", "Condition", "Others like company")
by_line(gjg_long, pol, OTrust, "political leaning", " ", "Condition", "Others trust company")
by_line(gjg_long, pol, OComp, "political leaning", " ", "Condition", "Others think company is competent")
6.3.2.1 Factor 2 Items Tests
All main effects no interaction effects aside from noted below
Others liking of the comapny: The main effect of action consistency is nearing but not significant
# Other Like
mod_otherLike <- lmer(OLike ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_otherLike)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OLike ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10312.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6934 -0.4353 0.1287 0.5800 2.7269
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.519453 0.72073
## social_issue (Intercept) 0.007611 0.08724
## Residual 1.129443 1.06275
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.47657 0.06298 9.22003 86.958 9.39e-15
## companyLeaningRight -0.28049 0.05317 2389.86639 -5.275 1.44e-07
## actionI -0.09079 0.05355 2391.31604 -1.695 0.0901
## companyLeaningRight:actionI -0.06488 0.07545 2394.35843 -0.860 0.3899
##
## (Intercept) ***
## companyLeaningRight ***
## actionI .
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.422
## actionI -0.419 0.496
## cmpnyLnnR:I 0.298 -0.705 -0.709mod_pol_otherLike <- lmer(OLike ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_otherLike)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OLike ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10286.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7275 -0.4459 0.1443 0.5903 2.7859
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.488578 0.69898
## social_issue (Intercept) 0.007684 0.08766
## Residual 1.129424 1.06274
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.08380 0.09287 41.86602 54.744 < 2e-16
## companyLeaningRight -0.28049 0.05317 2389.94728 -5.275 1.44e-07
## actionI -0.09064 0.05355 2391.46753 -1.693 0.0907
## pol 0.09588 0.01670 796.79401 5.743 1.33e-08
## companyLeaningRight:actionI -0.06502 0.07545 2394.40282 -0.862 0.3889
##
## (Intercept) ***
## companyLeaningRight ***
## actionI .
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.286
## actionI -0.285 0.496
## pol -0.736 0.000 0.001
## cmpnyLnnR:I 0.202 -0.705 -0.709 0.000# Other Trust
mod_otherTrust <- lmer(OTrust ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_otherTrust)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OTrust ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10424.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5727 -0.4477 0.0808 0.5735 3.2633
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.671102 0.81921
## social_issue (Intercept) 0.004964 0.07046
## Residual 1.118816 1.05774
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.12883 0.05900 14.32278 86.927 < 2e-16
## companyLeaningRight -0.31555 0.05292 2389.68836 -5.963 2.85e-09
## actionI -0.16070 0.05327 2383.01774 -3.017 0.00258
## companyLeaningRight:actionI -0.02410 0.07507 2393.90328 -0.321 0.74822
##
## (Intercept) ***
## companyLeaningRight ***
## actionI **
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.448
## actionI -0.446 0.497
## cmpnyLnnR:I 0.316 -0.705 -0.709mod_pol_otherTrust <- lmer(OTrust ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_otherTrust)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OTrust ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10411.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6171 -0.4426 0.0958 0.5862 3.3225
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.649883 0.80615
## social_issue (Intercept) 0.005008 0.07077
## Residual 1.118799 1.05773
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.79945 0.09543 90.31467 50.295 < 2e-16
## companyLeaningRight -0.31555 0.05292 2389.75657 -5.963 2.85e-09
## actionI -0.16058 0.05327 2383.23782 -3.015 0.0026
## pol 0.08041 0.01833 796.74528 4.386 1.31e-05
## companyLeaningRight:actionI -0.02421 0.07507 2393.93787 -0.322 0.7471
##
## (Intercept) ***
## companyLeaningRight ***
## actionI **
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.277
## actionI -0.276 0.497
## pol -0.787 0.000 0.001
## cmpnyLnnR:I 0.196 -0.705 -0.709 0.000# Other Competence
mod_pol_otherComp <- lmer(OComp ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_otherComp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OComp ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10486.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5081 -0.4419 0.1125 0.5942 3.0093
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.60060 0.7750
## social_issue (Intercept) 0.01207 0.1099
## Residual 1.16891 1.0812
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.88527 0.10320 29.15063 47.338 < 2e-16
## companyLeaningRight -0.32941 0.05409 2389.52437 -6.090 1.31e-09
## actionI -0.18691 0.05450 2394.35733 -3.429 0.000615
## pol 0.09579 0.01797 796.54376 5.331 1.27e-07
## companyLeaningRight:actionI -0.01132 0.07677 2393.66291 -0.147 0.882833
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.262
## actionI -0.261 0.496
## pol -0.713 0.000 0.001
## cmpnyLnnR:I 0.185 -0.705 -0.710 0.0006.3.3 Factor 3: Importance of the Cause
plot_list <- list(
plot_cooker(gjg_long, condition, SImport, "I think cause is important"),
plot_cooker(gjg_long, condition, SRelImport, "I think cause is important relative to other causes"),
plot_cooker(gjg_long, condition, staticNorm, "Percent of support for issue"),
plot_cooker(gjg_long, condition, noisyMin, "Motive to cater to noisy minority")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 2)
plot_arranged
6.3.3.1 Factor 3 Items Tests
Issue Importance: No main effects, Marginal interaction effect (less decrease in perceived issue importance when a conservative company acts inconsistently)
Issue Relative Importance: Main effect of consistency (acting inconsistent INCREASES perceived issue importance), Marginal interaction effect (less decrease in perceived issue importance when a conservative company acts inconsistently)
Static Norm: No effects
Noisy Min: Main effects – perceived as moreso catering to noisy minority if conservative or inconsistent
# Importance of cause
mod_import <- lmer(SImport ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_import)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SImport ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11928.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6568 -0.4790 0.0186 0.5512 3.1553
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 2.5335 1.5917
## social_issue (Intercept) 0.3892 0.6239
## Residual 1.4399 1.1999
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.93049 0.31982 3.28369 15.417 0.000355
## companyLeaningRight 0.05470 0.06003 2390.77325 0.911 0.362278
## actionI 0.08561 0.06056 2391.78503 1.414 0.157565
## companyLeaningRight:actionI -0.15517 0.08525 2391.25731 -1.820 0.068850
##
## (Intercept) ***
## companyLeaningRight
## actionI
## companyLeaningRight:actionI .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.094
## actionI -0.093 0.496
## cmpnyLnnR:I 0.066 -0.704 -0.710# Relative importance of cause
mod_relImport <- lmer(SRelImport ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_relImport)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SRelImport ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11350.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1108 -0.5733 -0.0035 0.5267 3.9263
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.8941 1.3763
## social_issue (Intercept) 0.5043 0.7101
## Residual 1.2382 1.1127
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.94371 0.36054 3.17122 10.938 0.00127
## companyLeaningRight 0.03863 0.05567 2390.66316 0.694 0.48784
## actionI 0.12142 0.05615 2391.61997 2.162 0.03070
## companyLeaningRight:actionI -0.14857 0.07905 2391.11914 -1.879 0.06031
##
## (Intercept) **
## companyLeaningRight
## actionI *
## companyLeaningRight:actionI .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.077
## actionI -0.077 0.496
## cmpnyLnnR:I 0.054 -0.704 -0.710# Static Norm
mod_staticNorm <- lmer(staticNorm ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_staticNorm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: staticNorm ~ companyLeaning * action + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 27299.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0044 -0.5492 0.0148 0.5491 3.6201
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 168.9 13.00
## social_issue (Intercept) 106.3 10.31
## Residual 208.4 14.43
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.0514 5.2010 3.0925 10.777 0.00149 **
## companyLeaningRight -0.1413 0.7222 2390.4205 -0.196 0.84491
## actionI 0.7087 0.7284 2391.7706 0.973 0.33067
## companyLeaningRight:actionI -1.2599 1.0255 2391.0627 -1.229 0.21934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.069
## actionI -0.069 0.496
## cmpnyLnnR:I 0.049 -0.704 -0.710mod_staticNorm <- lmer(staticNorm ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_staticNorm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## staticNorm ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 27101.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0617 -0.5609 0.0026 0.5747 3.6886
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 131.4 11.46
## social_issue (Intercept) 100.3 10.01
## Residual 204.4 14.30
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 36.0946 5.2291 3.5497 6.903 0.00354 **
## companyLeaningRight 1.3799 0.7313 2433.7159 1.887 0.05930 .
## actionI 2.3948 0.7410 2444.2885 3.232 0.00125 **
## pol 2.7592 0.2569 791.9105 10.741 < 2e-16 ***
## SGen 1.8835 0.1880 3183.1620 10.019 < 2e-16 ***
## companyLeaningRight:actionI -1.2113 1.0158 2385.9511 -1.192 0.23322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.101
## actionI -0.104 0.519
## pol -0.201 -0.001 0.000
## SGen -0.165 0.208 0.228 -0.003
## cmpnyLnnR:I 0.048 -0.688 -0.690 0.000 0.004# Catering to Noisy Minority
mod_noisyMin <- lmer(noisyMin ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_noisyMin)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: noisyMin ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11737.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8458 -0.5723 0.0511 0.5800 3.4278
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.6731 1.2935
## social_issue (Intercept) 0.1741 0.4173
## Residual 1.4938 1.2222
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.17683 0.21793 3.49110 19.166 0.000116
## companyLeaningRight 0.17605 0.06115 2390.60658 2.879 0.004025
## actionI 0.25917 0.06167 2392.38651 4.202 2.74e-05
## companyLeaningRight:actionI -0.04380 0.08683 2391.47053 -0.504 0.614027
##
## (Intercept) ***
## companyLeaningRight **
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.140
## actionI -0.139 0.496
## cmpnyLnnR:I 0.099 -0.704 -0.7106.3.4 Factor 4: Perception of Companies
plot_list <- list(
plot_cooker(gjg_long, condition, peerPress, "Pressured by peer companies"),
plot_cooker(gjg_long, condition, pubPress, "Pressured by public"),
plot_cooker(gjg_long, condition, riskInact, "Risky NOT to send signal")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 2)
plot_arranged
6.3.4.1 Factor 4 Items Tests
All main effects no interaction effects aside from noted below
Risky to not send signal: No main effect of on company political leaning, but interaction effect (conservative companies that act inconsistent experience a lower increase in risk for NOT signaling)
# Motive is pressure from peer institutions
mod_peerPress <- lmer(peerPress ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_peerPress)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: peerPress ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11668.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5953 -0.5587 0.0836 0.6200 3.0851
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.12485 1.0606
## social_issue (Intercept) 0.02671 0.1634
## Residual 1.60459 1.2667
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.099e+00 1.005e-01 5.827e+00 40.803 2.19e-08 ***
## companyLeaningRight 2.177e-01 6.338e-02 2.391e+03 3.435 0.000603 ***
## actionI 4.146e-01 6.388e-02 2.395e+03 6.489 1.04e-10 ***
## companyLeaningRight:actionI 8.411e-02 8.996e-02 2.394e+03 0.935 0.349896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.315
## actionI -0.313 0.496
## cmpnyLnnR:I 0.222 -0.704 -0.710# Motive is pressure from general public
mod_pubPress <- lmer(pubPress ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pubPress)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pubPress ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11594.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9762 -0.6241 0.0376 0.6139 3.4002
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.15220 1.0734
## social_issue (Intercept) 0.03784 0.1945
## Residual 1.78416 1.3357
## Number of obs: 3095, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.79900 0.11461 5.08373 33.148 3.88e-07
## companyLeaningRight 0.25490 0.06683 2286.20317 3.814 0.00014
## actionI 0.54640 0.06743 2291.44633 8.103 8.60e-16
## companyLeaningRight:actionI -0.10002 0.09668 2304.03074 -1.035 0.30099
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.292
## actionI -0.289 0.495
## cmpnyLnnR:I 0.202 -0.691 -0.688# Risky to NOT send signal
mod_riskInact <- lmer(riskInact ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_riskInact)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: riskInact ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12023.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2288 -0.6400 -0.0196 0.6371 3.1790
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.11979 1.0582
## social_issue (Intercept) 0.08174 0.2859
## Residual 1.83782 1.3557
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.61771 0.15536 3.96490 23.286 2.17e-05
## companyLeaningRight -0.02588 0.06783 2390.35744 -0.382 0.70279
## actionI 0.41643 0.06839 2393.67766 6.089 1.32e-09
## companyLeaningRight:actionI -0.25382 0.09630 2392.05482 -2.636 0.00845
##
## (Intercept) ***
## companyLeaningRight
## actionI ***
## companyLeaningRight:actionI **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.218
## actionI -0.217 0.496
## cmpnyLnnR:I 0.154 -0.704 -0.7106.3.5 Factor 5: Selfish/Profit Motives
plot_list <- list(
plot_cooker(gjg_long, condition, Rep, "Reputation motive"),
plot_cooker(gjg_long, condition, Profit, "Profit motive")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 1)
plot_arranged
6.3.5.1 Factor 5 Item Tests
Motivated by reputational gain: Main effect and interaction effect (less increase in selfish motive when acting inconsistently if company is conservative)
Motivated by profit: Main effect, no interaction effect
# Motivated by reputational gain
mod_rep <- lmer(Rep ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Rep ~ companyLeaning * action + (1 | participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10556.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2296 -0.4267 0.1317 0.5753 3.1785
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.74129 0.8610
## social_issue (Intercept) 0.01971 0.1404
## Residual 1.15010 1.0724
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.20642 0.08541 5.65570 60.960 3.45e-09
## companyLeaningRight 0.16743 0.05365 2390.31376 3.121 0.001827
## actionI 0.45229 0.05408 2394.99275 8.363 < 2e-16
## companyLeaningRight:actionI -0.25631 0.07617 2393.28711 -3.365 0.000777
##
## (Intercept) ***
## companyLeaningRight **
## actionI ***
## companyLeaningRight:actionI ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.314
## actionI -0.312 0.496
## cmpnyLnnR:I 0.221 -0.704 -0.710# Motivated by profit
mod_Profit <- lmer(Profit ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_Profit)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Profit ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11291.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8780 -0.5054 0.1093 0.5959 2.7708
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.97973 0.9898
## social_issue (Intercept) 0.01028 0.1014
## Residual 1.43364 1.1973
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.80807 0.07477 9.98388 64.307 2.10e-14
## companyLeaningRight 0.15015 0.05990 2390.52571 2.506 0.0123
## actionI 0.44581 0.06034 2391.18631 7.388 2.04e-13
## companyLeaningRight:actionI -0.09938 0.08501 2394.41637 -1.169 0.2425
##
## (Intercept) ***
## companyLeaningRight *
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.401
## actionI -0.398 0.496
## cmpnyLnnR:I 0.282 -0.705 -0.710# Competence
mod_comp <- lmer(SComp ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_comp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SComp ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 11912.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0950 -0.5663 0.1048 0.6211 3.2020
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.97727 0.9886
## social_issue (Intercept) 0.04371 0.2091
## Residual 1.81414 1.3469
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.902e+00 1.201e-01 4.663e+00 40.822 3.88e-07
## companyLeaningRight -5.221e-01 6.739e-02 2.391e+03 -7.747 1.38e-14
## actionI -3.571e-01 6.794e-02 2.395e+03 -5.257 1.59e-07
## companyLeaningRight:actionI -7.286e-03 9.567e-02 2.393e+03 -0.076 0.939
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.281
## actionI -0.278 0.496
## cmpnyLnnR:I 0.198 -0.704 -0.710# Genuinely care about cause
mod_gen <- lmer(SGen ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## SGen ~ companyLeaning * action + (1 | participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12007.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10336 -0.65538 -0.01925 0.65306 3.03902
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.05521 1.0272
## social_issue (Intercept) 0.02946 0.1716
## Residual 1.85185 1.3608
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.59481 0.10490 5.72893 43.803 1.85e-08
## companyLeaningRight -0.80761 0.06808 2390.82664 -11.862 < 2e-16
## actionI -0.89976 0.06862 2395.22969 -13.111 < 2e-16
## companyLeaningRight:actionI -0.02131 0.09665 2393.77087 -0.221 0.825
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.325
## actionI -0.322 0.496
## cmpnyLnnR:I 0.229 -0.704 -0.7106.3.6 Factor 6: Motivating people to act
plot_list <- list(
plot_cooker(gjg_long, condition, dyNorm, "Change in support for issue"),
plot_cooker(gjg_long, condition, OMotivate, "Message motivates others to act"),
plot_cooker(gjg_long, condition, SGen, "I think company is genuine")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 2, nrow = 2)
plot_arranged
by_line(gjg_long, pol, OMotivate, "political leaning", " ", "Condition", "Motivate others to act")
# Assuming your dataset is named "your_data", and "pol" is the column with political scores
# Create a new column "pol_name" based on the values in the "pol" column
gjg_long$pol_name <- ifelse(gjg_long$pol > 4, "lib", ifelse(gjg_long$pol == 4, "mod", "con"))
# Print the first few rows to check the result
head(gjg_long$pol_name)## [1] "lib" "mod" "con" "mod" "con" "lib"wrapped_plot_cooker(gjg_long, condition, SGen, "I think company is genuine", pol_name)
wrapped_plot_cooker(gjg_long, condition, SRelImport, "I think cause is important relative to other causes", pol_name)
mod_Profit <- lmer(SRelImport ~ companyLeaning*action + pol_name + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_Profit)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## SRelImport ~ companyLeaning * action + pol_name + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10963.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8100 -0.5858 -0.0169 0.5263 4.0706
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.0406 1.0201
## social_issue (Intercept) 0.5006 0.7075
## Residual 1.2383 1.1128
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.89560 0.36172 3.25753 8.005 0.0030
## companyLeaningRight 0.03863 0.05567 2390.38811 0.694 0.4878
## actionI 0.12037 0.05615 2391.76356 2.143 0.0322
## pol_namelib 2.10964 0.09397 795.54308 22.450 <2e-16
## pol_namemod 0.93591 0.10840 795.55111 8.634 <2e-16
## companyLeaningRight:actionI -0.14755 0.07905 2391.04290 -1.866 0.0621
##
## (Intercept) **
## companyLeaningRight
## actionI *
## pol_namelib ***
## pol_namemod ***
## companyLeaningRight:actionI .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pl_nml pl_nmm
## cmpnyLnngRg -0.077
## actionI -0.076 0.496
## pol_namelib -0.133 0.000 0.000
## pol_namemod -0.116 0.000 0.001 0.445
## cmpnyLnnR:I 0.054 -0.704 -0.710 0.000 0.0006.3.6.1 Tests
Dynamic Norm: Marginal
Driving others to support cause: Significant main and interaction effects (The cost of inconsistency is lower if a company is conservative), significant main effect of political leaning (liberals think messages are more effective at motivating others to act)
# Dynamic Norm
mod_dyNorm <- lmer(dyNorm ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_dyNorm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dyNorm ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10558.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8861 -0.4786 0.0680 0.5927 3.6063
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.92445 0.9615
## social_issue (Intercept) 0.05652 0.2377
## Residual 1.09266 1.0453
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.03582 0.12906 3.96345 39.021 2.83e-06
## companyLeaningRight -0.04305 0.05230 2390.46237 -0.823 0.4105
## actionI -0.09505 0.05274 2393.37112 -1.802 0.0716
## companyLeaningRight:actionI -0.05954 0.07425 2391.93529 -0.802 0.4227
##
## (Intercept) ***
## companyLeaningRight
## actionI .
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.203
## actionI -0.201 0.496
## cmpnyLnnR:I 0.143 -0.704 -0.710mod_pol_dyNorm <- lmer(dyNorm ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_dyNorm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dyNorm ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10563.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8990 -0.4750 0.0638 0.5926 3.6060
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.92469 0.9616
## social_issue (Intercept) 0.05648 0.2377
## Residual 1.09266 1.0453
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.11359 0.15464 8.16969 33.068 5.42e-10
## companyLeaningRight -0.04305 0.05230 2390.45245 -0.823 0.4105
## actionI -0.09508 0.05274 2393.36207 -1.803 0.0715
## pol -0.01898 0.02081 796.81051 -0.912 0.3620
## companyLeaningRight:actionI -0.05951 0.07425 2391.92592 -0.801 0.4230
##
## (Intercept) ***
## companyLeaningRight
## actionI .
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.169
## actionI -0.168 0.496
## pol -0.551 0.000 0.001
## cmpnyLnnR:I 0.119 -0.704 -0.710 0.000mod_gen_dyNorm <- lmer(dyNorm ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_dyNorm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dyNorm ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10563.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8990 -0.4750 0.0638 0.5926 3.6060
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.92469 0.9616
## social_issue (Intercept) 0.05648 0.2377
## Residual 1.09266 1.0453
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.11359 0.15464 8.16969 33.068 5.42e-10
## companyLeaningRight -0.04305 0.05230 2390.45245 -0.823 0.4105
## actionI -0.09508 0.05274 2393.36207 -1.803 0.0715
## pol -0.01898 0.02081 796.81051 -0.912 0.3620
## companyLeaningRight:actionI -0.05951 0.07425 2391.92592 -0.801 0.4230
##
## (Intercept) ***
## companyLeaningRight
## actionI .
## pol
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.169
## actionI -0.168 0.496
## pol -0.551 0.000 0.001
## cmpnyLnnR:I 0.119 -0.704 -0.710 0.000# Driving others to support cause
mod_OMotivate <- lmer(OMotivate ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_OMotivate)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OMotivate ~ companyLeaning * action + (1 | participantID) + (1 |
## social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10878.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9198 -0.4763 0.0642 0.5619 3.1120
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.98247 0.9912
## social_issue (Intercept) 0.02822 0.1680
## Residual 1.22035 1.1047
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.96223 0.09905 5.17358 50.099 3.75e-08
## companyLeaningRight -0.26187 0.05527 2390.55174 -4.738 2.28e-06
## actionI -0.33245 0.05572 2394.68401 -5.966 2.79e-09
## companyLeaningRight:actionI -0.28226 0.07846 2392.94760 -3.597 0.000328
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.279
## actionI -0.277 0.496
## cmpnyLnnR:I 0.197 -0.704 -0.710mod_pol_OMotivate <- lmer(OMotivate ~ companyLeaning*action + pol + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_OMotivate)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: OMotivate ~ companyLeaning * action + pol + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10860.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9150 -0.4795 0.0758 0.5634 3.1472
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.94537 0.9723
## social_issue (Intercept) 0.02835 0.1684
## Residual 1.22034 1.1047
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.52944 0.13185 16.00797 34.353 < 2e-16
## companyLeaningRight -0.26187 0.05527 2390.59448 -4.738 2.28e-06
## actionI -0.33232 0.05572 2394.72783 -5.964 2.82e-09
## pol 0.10565 0.02126 796.94458 4.969 8.26e-07
## companyLeaningRight:actionI -0.28239 0.07846 2392.98643 -3.599 0.000326
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## pol ***
## companyLeaningRight:actionI ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol
## cmpnyLnngRg -0.210
## actionI -0.208 0.496
## pol -0.661 0.000 0.001
## cmpnyLnnR:I 0.148 -0.704 -0.710 0.000mod_pol_OMotivate <- lmer(OMotivate ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_pol_OMotivate)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## OMotivate ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 10248.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2073 -0.5151 0.0401 0.5678 3.9412
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.65929 0.8120
## social_issue (Intercept) 0.01412 0.1188
## Residual 1.04479 1.0221
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.91452 0.12256 43.83925 23.780 < 2e-16
## companyLeaningRight 0.02289 0.05227 2427.86756 0.438 0.661555
## actionI -0.01700 0.05294 2442.25465 -0.321 0.748143
## pol 0.10441 0.01824 786.02234 5.723 1.49e-08
## SGen 0.35258 0.01341 3186.96582 26.291 < 2e-16
## companyLeaningRight:actionI -0.27301 0.07259 2382.12076 -3.761 0.000173
##
## (Intercept) ***
## companyLeaningRight
## actionI
## pol ***
## SGen ***
## companyLeaningRight:actionI ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.308
## actionI -0.316 0.520
## pol -0.608 -0.001 0.000
## SGen -0.501 0.207 0.228 -0.003
## cmpnyLnnR:I 0.145 -0.688 -0.690 0.000 0.004# Genuinely care about cause
mod_gen <- lmer(SGen ~ companyLeaning*action + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## SGen ~ companyLeaning * action + (1 | participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 12007.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.10336 -0.65538 -0.01925 0.65306 3.03902
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 1.05521 1.0272
## social_issue (Intercept) 0.02946 0.1716
## Residual 1.85185 1.3608
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.59481 0.10490 5.72893 43.803 1.85e-08
## companyLeaningRight -0.80761 0.06808 2390.82664 -11.862 < 2e-16
## actionI -0.89976 0.06862 2395.22969 -13.111 < 2e-16
## companyLeaningRight:actionI -0.02131 0.09665 2393.77087 -0.221 0.825
##
## (Intercept) ***
## companyLeaningRight ***
## actionI ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.325
## actionI -0.322 0.496
## cmpnyLnnR:I 0.229 -0.704 -0.710gjg_mod <- gjg_long %>% filter(pol == 4)
mod_pol_gen <- lmer(SRelImport ~ companyLeaning + action*pol + action*SRelImport + (1 | participantID) + (1 | social_issue) + (1 | company), data = gjg_mod)
summary(mod_pol_gen)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SRelImport ~ companyLeaning + action * pol + action * SRelImport +
## (1 | participantID) + (1 | social_issue) + (1 | company)
## Data: gjg_mod
##
## REML criterion at convergence: 2305.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.62878 -0.44995 0.05967 0.43721 2.83223
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.28237 0.5314
## company (Intercept) 0.02814 0.1678
## social_issue (Intercept) 0.22776 0.4772
## Residual 1.01738 1.0087
## Number of obs: 748, groups: participantID, 187; company, 13; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.84561 0.25842 3.80420 14.881 0.000164 ***
## companyLeaningRight 0.03665 0.12151 2.95633 0.302 0.782895
## actionI -3.00363 0.15277 683.74921 -19.661 < 2e-16 ***
## SRelImport:actionI 0.79150 0.03466 705.73222 22.839 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI
## cmpnyLnngRg -0.223
## actionI -0.065 -0.008
## SRlImprt:cI -0.003 -0.002 -0.871
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients6.4 Genuine Line
6.4.1 Each Factor
plot_list <- list(plot_cooker(gjg_long, condition, selfPercep_z, "Perception of company"),
plot_cooker(gjg_long, condition, otherPercep_z, "Others' perception of company"),
plot_cooker(gjg_long, condition, issueImport_z, "Importance of cause"),
plot_cooker(gjg_long, condition, externalMotive_z, "External Motives"),
plot_cooker(gjg_long, condition, selfishMotive_z, "Selfish Motives"),
plot_cooker(gjg_long, condition, driveAction_z, "Motivate Others to Act")
)
plot_arranged <- ggarrange(plotlist = plot_list, ncol = 3, nrow = 2)
plot_arranged
by_line(gjg_long, SGen, selfPercep_z, "Genuine", "", "Condition", "Perception of company")
by_line(gjg_long, SGen, otherPercep_z, "Genuine", "", "Condition", "Others' perception of company")
by_line(gjg_long, SGen, issueImport_z, "Genuine", "", "Condition", "Importance of cause")
by_line(gjg_long, SGen, externalMotive_z, "Genuine", "", "Condition", "External Motives")
by_line(gjg_long, SGen, selfishMotive_z, "Genuine", "", "Condition", "Selfish Motives")
by_line(gjg_long, SGen, driveAction_z, "Genuine", "", "Condition", "Motivate Others to Act")
# Factor 1
mod_gen_selfPercep <- lmer(selfPercep_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_selfPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## selfPercep_z ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 4402.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1349 -0.5994 0.0479 0.5979 3.9222
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.044527 0.21101
## social_issue (Intercept) 0.001702 0.04126
## Residual 0.194742 0.44130
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.582e+00 4.332e-02 4.250e+01 -36.514 < 2e-16
## companyLeaningRight -1.127e-02 2.249e-02 2.433e+03 -0.501 0.61633
## actionI 6.997e-02 2.275e-02 2.451e+03 3.076 0.00212
## pol 1.775e-02 5.806e-03 7.764e+02 3.058 0.00231
## SGen 3.978e-01 5.287e-03 2.977e+03 75.247 < 2e-16
## companyLeaningRight:actionI -4.831e-03 3.133e-02 2.374e+03 -0.154 0.87748
##
## (Intercept) ***
## companyLeaningRight
## actionI **
## pol **
## SGen ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.356
## actionI -0.365 0.516
## pol -0.547 -0.001 0.000
## SGen -0.559 0.190 0.210 -0.003
## cmpnyLnnR:I 0.178 -0.691 -0.693 -0.001 0.003# Factor 2
mod_gen_otherPercep <- lmer(otherPercep_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_otherPercep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## otherPercep_z ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7504.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3296 -0.4565 0.0856 0.5765 3.8493
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.23203 0.48169
## social_issue (Intercept) 0.00284 0.05329
## Residual 0.45974 0.67804
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.95147 0.07264 108.38126 -13.098 < 2e-16
## companyLeaningRight -0.08052 0.03465 2435.59235 -2.324 0.0202
## actionI 0.05901 0.03506 2449.56888 1.683 0.0924
## pol 0.06740 0.01120 789.84313 6.018 2.71e-09
## SGen 0.18629 0.00872 3180.89076 21.362 < 2e-16
## companyLeaningRight:actionI -0.02179 0.04813 2386.93793 -0.453 0.6509
##
## (Intercept) ***
## companyLeaningRight *
## actionI .
## pol ***
## SGen ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.340
## actionI -0.350 0.519
## pol -0.630 -0.001 0.000
## SGen -0.550 0.203 0.225 -0.003
## cmpnyLnnR:I 0.163 -0.689 -0.691 0.000 0.003# Factor 3
mod_gen_issueImport <- lmer(issueImport_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_issueImport)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## issueImport_z ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 4964.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3066 -0.5714 -0.0009 0.5522 4.2118
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.1408 0.3752
## social_issue (Intercept) 0.1029 0.3208
## Residual 0.1937 0.4401
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.396e+00 1.674e-01 3.539e+00 -8.341 0.00189
## companyLeaningRight 7.291e-02 2.252e-02 2.420e+03 3.238 0.00122
## actionI 9.581e-02 2.282e-02 2.430e+03 4.199 2.78e-05
## pol 2.297e-01 8.271e-03 7.809e+02 27.765 < 2e-16
## SGen 1.077e-01 5.845e-03 3.170e+03 18.422 < 2e-16
## companyLeaningRight:actionI -4.485e-02 3.126e-02 2.375e+03 -1.434 0.15157
##
## (Intercept) **
## companyLeaningRight **
## actionI ***
## pol ***
## SGen ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.098
## actionI -0.100 0.520
## pol -0.202 -0.001 0.000
## SGen -0.160 0.210 0.230 -0.002
## cmpnyLnnR:I 0.046 -0.688 -0.690 0.000 0.004# Factor 4
mod_gen_externalMotive <- lmer(externalMotive_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_externalMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: externalMotive_z ~ companyLeaning * action + pol + SGen + (1 |
## participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 6530.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3488 -0.5940 0.0071 0.6000 3.9775
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.28435 0.5332
## social_issue (Intercept) 0.01283 0.1133
## Residual 0.30174 0.5493
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.332e-01 8.537e-02 1.457e+01 5.075 0.00015
## companyLeaningRight -1.777e-02 2.813e-02 2.425e+03 -0.632 0.52774
## actionI 1.510e-01 2.851e-02 2.435e+03 5.296 1.29e-07
## pol -4.335e-04 1.141e-02 7.904e+02 -0.038 0.96970
## SGen -1.298e-01 7.444e-03 3.121e+03 -17.437 < 2e-16
## companyLeaningRight:actionI -4.698e-02 3.902e-02 2.385e+03 -1.204 0.22874
##
## (Intercept) ***
## companyLeaningRight
## actionI ***
## pol
## SGen ***
## companyLeaningRight:actionI
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.243
## actionI -0.249 0.521
## pol -0.546 0.000 0.000
## SGen -0.399 0.214 0.234 -0.002
## cmpnyLnnR:I 0.112 -0.687 -0.689 0.000 0.004# Factor 5
mod_gen_selfishMotive <- lmer(selfishMotive_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_selfishMotive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: selfishMotive_z ~ companyLeaning * action + pol + SGen + (1 |
## participantID) + (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 7129
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0591 -0.4639 0.0854 0.5461 3.8055
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.29951 0.5473
## social_issue (Intercept) 0.01159 0.1077
## Residual 0.37616 0.6133
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.762e-01 8.685e-02 1.854e+01 8.938 3.82e-08
## companyLeaningRight -6.274e-02 3.139e-02 2.434e+03 -1.999 0.045767
## actionI 1.167e-01 3.180e-02 2.445e+03 3.671 0.000247
## pol 6.305e-03 1.193e-02 7.962e+02 0.529 0.597263
## SGen -2.116e-01 8.204e-03 3.159e+03 -25.795 < 2e-16
## companyLeaningRight:actionI -1.305e-01 4.356e-02 2.391e+03 -2.996 0.002760
##
## (Intercept) ***
## companyLeaningRight *
## actionI ***
## pol
## SGen ***
## companyLeaningRight:actionI **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.264
## actionI -0.271 0.520
## pol -0.562 -0.001 0.000
## SGen -0.433 0.211 0.232 -0.002
## cmpnyLnnR:I 0.123 -0.688 -0.690 0.000 0.004gjg_long$SRelImport## [1] 7 7 6 3 2 6 5 1 1 4 5 6 4 5 5 6 2 5 4 6 2 6 6 6 5 6 5 1 6 4 4 6 2 4 6 7 4
## [38] 5 6 7 5 5 6 5 4 4 3 2 6 5 5 7 4 5 4 4 4 4 2 3 7 7 5 5 4 6 7 4 6 6 2 5 3 4
## [75] 7 4 4 1 4 4 1 3 5 5 6 6 5 2 3 6 4 4 4 4 4 3 3 5 4 4 6 4 4 5 1 5 5 1 1 3 6
## [112] 6 3 4 2 5 1 1 2 6 2 4 5 6 5 5 4 1 1 4 4 6 2 4 6 7 4 3 5 6 1 4 6 5 3 4 1 1
## [149] 6 5 4 4 3 6 4 4 3 4 1 2 1 6 3 4 1 4 1 4 5 6 3 4 3 3 5 4 4 1 4 2 1 1 4 4 3
## [186] 3 6 1 1 1 1 2 4 4 2 3 1 3 4 6 5 4 3 5 1 5 3 1 1 3 5 6 3 3 2 6 1 1 1 5 1 3
## [223] 5 5 6 6 4 1 3 3 4 5 1 3 6 6 3 5 4 6 1 4 6 4 3 2 1 1 5 5 4 5 4 5 4 4 1 4 1
## [260] 1 1 6 6 3 4 4 1 2 5 6 1 4 2 3 5 4 4 1 2 2 1 2 5 4 1 2 6 1 1 6 1 1 3 4 2 1
## [297] 1 2 4 5 7 4 6 4 5 5 7 1 5 4 7 6 7 7 6 7 2 5 5 7 1 6 7 6 4 5 7 1 4 4 4 7 2
## [334] 5 5 7 4 7 4 7 2 6 7 5 6 6 2 1 7 7 6 7 6 3 5 6 5 3 2 3 2 3 5 6 4 6 6 6 6 7
## [371] 1 4 6 5 6 1 7 1 6 3 1 5 5 4 1 1 6 1 5 7 1 2 4 4 2 3 1 5 7 5 5 4 6 7 6 5 1
## [408] 5 6 6 6 4 5 4 6 4 5 5 7 5 4 4 6 4 4 2 4 5 4 6 4 6 6 4 2 6 6 4 4 6 4 5 6 1
## [445] 4 5 6 6 4 1 6 4 5 7 6 4 3 6 7 6 6 3 3 3 7 4 6 4 1 4 7 4 1 2 5 7 6 7 4 5 3
## [482] 5 6 3 5 4 1 5 7 6 7 2 5 4 5 4 4 6 6 6 5 2 5 6 5 4 1 4 6 3 6 3 2 4 5 4 4 3
## [519] 6 5 1 4 5 4 2 1 1 5 4 6 1 6 1 4 4 4 5 1 1 2 4 3 6 1 2 3 6 6 4 1 1 3 4 7 4
## [556] 3 2 4 7 5 5 2 4 3 4 2 5 3 1 1 6 2 1 1 2 1 4 3 4 4 3 5 2 1 4 2 4 4 4 1 2 1
## [593] 5 2 1 3 1 6 5 4 5 1 4 6 5 4 1 5 5 4 6 2 2 2 5 4 3 3 6 5 1 4 1 4 1 1 1 4 4
## [630] 6 1 5 1 4 1 4 1 2 2 3 4 2 7 1 3 3 6 5 4 1 1 1 4 4 4 3 1 4 7 4 3 2 6 2 4 2
## [667] 5 3 1 1 6 2 1 1 2 1 6 1 3 3 3 4 1 1 4 2 1 4 4 1 3 1 4 1 1 3 1 5 6 5 4 5 7
## [704] 6 6 6 1 4 5 6 7 4 5 7 7 5 6 5 7 5 3 2 6 6 1 1 4 6 6 6 5 7 4 5 6 7 7 2 4 7
## [741] 5 5 7 1 4 5 6 6 5 4 2 7 5 7 5 4 4 6 7 6 6 3 7 5 3 4 7 3 1 4 7 4 2 2 5 5 6
## [778] 4 2 7 3 6 7 1 4 3 1 7 6 2 5 2 6 2 1 4 6 4 5 7 2 1 7 4 4 5 5 3 2 4 6 6 4 5
## [815] 6 2 6 1 5 6 7 4 7 6 3 4 7 6 7 6 7 6 4 5 4 7 7 4 6 5 5 5 4 5 6 4 7 6 6 6 5
## [852] 5 6 5 4 2 5 5 4 2 3 6 5 4 4 2 5 6 7 4 7 7 4 2 7 4 4 1 4 2 5 4 5 6 4 7 3 3
## [889] 5 1 4 6 5 6 4 5 4 2 4 4 4 1 3 5 4 1 4 4 1 1 4 6 5 4 4 5 1 5 1 3 6 6 4 5 7
## [926] 3 6 6 6 6 1 4 6 4 4 2 6 5 3 3 6 5 5 4 2 5 3 7 1 5 5 1 4 3 6 4 1 5 4 4 1 2
## [963] 3 4 1 4 1 4 3 3 4 4 1 1 4 5 2 2 1 1 1 5 1 5 5 4 1 3 3 2 1 4 4 3 1 1 5 3 2
## [1000] 1 1 4 1 1 4 4 1 4 4 1 1 4 6 3 4 4 5 1 5 1 2 5 6 4 5 7 1 6 6 6 7 1 4 6 4 5
## [1037] 1 6 5 1 3 4 4 4 4 1 4 3 7 1 5 4 1 1 2 4 2 1 5 4 4 1 3 6 4 1 4 1 4 3 2 4 3
## [1074] 1 1 4 4 1 2 1 1 1 4 1 5 4 4 1 3 3 1 1 4 4 2 1 1 5 2 1 1 1 1 1 4 7 7 3 5 6
## [1111] 5 5 4 7 7 4 6 7 1 4 5 3 5 6 7 3 5 5 4 7 7 6 5 7 6 6 6 4 7 5 3 2 4 7 6 5 5
## [1148] 7 4 7 5 7 6 5 1 6 4 3 2 7 3 4 1 7 5 4 3 6 1 5 6 6 6 6 4 4 1 7 7 6 6 3 1 7
## [1185] 2 6 6 4 2 2 4 1 1 3 4 3 6 1 6 2 2 7 7 6 6 5 6 5 7 7 5 6 6 4 4 4 5 5 5 7 1
## [1222] 5 7 4 7 5 4 6 6 4 4 6 5 4 4 5 2 3 6 6 5 4 6 4 4 6 1 7 4 7 5 5 4 4 7 3 4 5
## [1259] 3 6 1 1 6 3 6 4 1 5 6 4 4 6 5 4 5 2 5 4 1 1 4 3 4 3 4 4 6 5 4 6 4 6 5 3 3
## [1296] 5 4 5 1 1 3 1 4 4 3 6 4 5 3 7 6 5 4 5 4 1 5 5 4 3 2 5 4 4 4 6 2 3 6 5 4 4
## [1333] 6 4 1 4 6 1 3 6 5 4 4 6 1 2 5 1 6 2 4 2 5 4 1 6 2 4 5 2 3 1 1 5 1 7 1 1 4
## [1370] 2 1 4 4 4 1 4 1 2 2 1 1 3 1 4 2 4 4 4 6 4 5 4 2 3 3 4 5 1 4 1 1 1 1 4 4 1
## [1407] 5 4 3 3 7 7 4 4 6 4 1 5 5 1 1 4 6 4 4 4 6 1 1 6 5 4 4 6 5 1 2 4 1 3 6 4 4
## [1444] 2 6 1 3 4 1 6 2 4 1 5 4 1 4 1 4 7 1 1 1 1 5 1 5 1 1 3 1 1 4 4 4 1 4 1 2 2
## [1481] 1 1 2 1 2 1 4 4 4 6 4 6 4 1 3 4 1 4 1 2 1 1 1 1 1 4 1 6 7 6 1 6 7 5 6 7 2
## [1518] 5 7 7 1 5 4 1 7 7 4 6 6 1 6 6 6 5 7 4 3 4 7 3 4 7 5 5 7 6 7 5 5 1 7 5 6 3
## [1555] 6 4 3 6 4 4 3 2 7 1 5 4 2 7 4 1 4 7 2 7 6 5 7 5 2 6 4 4 1 3 1 5 3 1 4 5 3
## [1592] 5 5 5 7 4 4 5 7 2 5 4 2 3 2 5 6 5 4 6 7 7 5 4 5 7 4 4 5 5 6 4 2 5 6 4 6 6
## [1629] 7 6 4 5 4 4 1 6 7 6 6 6 6 5 7 6 5 5 5 7 4 5 7 5 7 3 4 3 5 5 4 5 3 5 2 6 6
## [1666] 1 5 1 5 1 5 7 2 1 1 2 2 4 4 5 5 6 1 4 3 5 4 4 4 4 7 2 5 1 4 4 4 7 7 7 4 5
## [1703] 5 4 2 5 6 4 5 5 4 2 4 4 7 4 4 4 4 6 2 1 4 6 4 4 6 6 4 4 4 4 2 1 5 7 5 6 4
## [1740] 4 4 6 6 6 3 4 6 4 3 7 5 4 1 4 2 5 4 4 4 4 4 2 4 5 1 2 2 4 1 1 4 1 1 1 1 1
## [1777] 4 4 4 4 6 1 2 1 5 4 1 3 3 4 6 4 1 2 4 4 6 5 4 3 4 5 1 1 3 5 4 6 6 4 1 4 2
## [1814] 5 4 4 4 4 6 2 1 4 6 3 5 5 3 4 3 3 4 2 1 5 6 5 5 4 7 4 4 6 5 3 4 6 4 1 6 5
## [1851] 4 1 4 1 4 4 4 3 4 4 1 2 4 1 1 1 4 1 2 4 1 1 1 1 1 4 4 4 3 5 1 2 1 3 4 1 3
## [1888] 2 4 1 4 1 2 4 4 6 5 1 3 2 6 3 1 2 5 3 6 7 7 7 4 6 6 5 4 5 6 7 6 7 7 7 7 7
## [1925] 4 5 7 3 6 5 3 2 6 7 7 7 4 6 6 5 7 6 4 7 6 4 6 6 3 7 6 4 7 7 5 7 5 5 5 6 7
## [1962] 6 1 6 2 5 1 3 6 6 1 1 6 2 5 6 6 5 6 1 5 3 6 5 3 7 4 6 7 3 1 4 7 5 6 6 3 6
## [1999] 5 2 3 1 4 5 2 5 4 4 3 3 6 4 4 5 4 3 6 5 6 6 6 5 5 5 4 2 2 7 7 5 5 6 5 4 2
## [2036] 5 6 5 5 5 5 4 1 3 4 5 4 3 1 3 6 4 6 4 5 6 5 4 4 2 3 4 4 3 3 1 6 5 4 4 2 5
## [2073] 4 4 4 6 4 3 4 5 3 4 3 4 4 6 4 4 1 6 4 2 6 7 4 4 1 5 4 6 2 3 4 3 3 1 2 6 4
## [2110] 3 1 6 4 4 4 1 2 5 1 3 4 6 4 5 4 3 1 2 6 7 1 5 4 2 2 2 3 6 4 4 2 1 4 1 1 4
## [2147] 4 6 2 1 3 2 4 4 4 4 5 5 4 2 2 1 4 3 3 3 1 5 4 4 4 2 1 6 3 2 2 4 2 3 4 1 4
## [2184] 2 2 4 4 4 3 1 1 1 2 4 5 4 1 1 3 3 6 1 3 4 4 3 1 1 4 4 4 1 7 4 4 4 2 2 4 5
## [2221] 1 3 5 4 5 4 5 1 1 5 7 1 5 4 1 1 1 2 6 4 4 1 1 4 1 1 3 4 3 1 1 1 3 4 4 4 4
## [2258] 5 5 4 2 2 1 4 3 1 3 1 2 3 4 3 2 1 5 3 2 1 4 2 2 1 1 2 2 1 4 5 4 5 1 1 1 1
## [2295] 4 5 4 1 1 3 2 6 1 1 4 4 2 1 3 6 4 7 1 6 5 4 7 2 1 7 7 2 6 7 6 5 4 5 3 3 6
## [2332] 7 4 7 7 4 4 4 4 5 5 5 6 4 5 7 4 7 6 7 6 1 2 6 5 7 5 6 7 6 7 4 2 3 7 4 4 5
## [2369] 1 6 5 4 7 2 4 5 4 4 4 5 5 4 3 1 6 7 5 7 4 4 3 2 6 3 4 6 5 4 4 3 3 6 4 3 3
## [2406] 5 5 6 7 4 4 3 4 7 4 5 5 5 3 6 4 6 2 5 5 5 6 6 6 4 4 6 7 1 6 2 1 4 5 6 4 4
## [2443] 4 5 5 4 3 4 5 7 5 4 4 3 5 6 2 6 4 6 4 4 7 5 4 5 6 6 1 4 4 3 3 4 5 4 4 1 1
## [2480] 6 7 6 4 3 5 4 4 6 5 4 1 2 3 6 1 5 5 5 1 4 4 4 3 7 1 3 4 5 1 3 7 4 5 4 5 4
## [2517] 3 1 6 1 7 2 4 6 6 4 4 3 6 1 1 6 1 1 4 5 6 4 4 2 5 6 4 1 1 3 6 4 4 1 2 4 4
## [2554] 1 2 4 5 4 4 6 3 4 4 4 6 1 3 3 4 3 2 5 4 4 1 1 1 6 6 4 2 5 1 5 5 4 3 1 1 5
## [2591] 2 1 4 4 7 2 1 4 4 1 4 1 3 2 3 1 3 7 4 5 4 4 4 2 1 6 1 7 3 3 6 6 4 4 3 5 1
## [2628] 1 6 1 1 4 4 6 4 4 2 6 6 4 1 1 3 4 3 4 1 2 4 3 1 1 4 5 4 4 6 3 4 4 4 6 1 3
## [2665] 3 1 3 2 6 3 4 1 1 1 6 5 4 1 5 1 5 3 4 4 1 1 5 1 1 4 4 4 1 4 4 4 1 4 2 7 4
## [2702] 6 2 4 7 4 5 7 5 7 6 1 6 1 1 3 4 7 7 4 7 4 7 5 1 7 2 2 4 6 5 5 7 2 7 5 4 1
## [2739] 1 6 6 5 5 3 3 6 6 1 6 5 7 4 5 7 7 4 6 7 6 1 4 6 1 7 4 7 7 4 4 2 6 5 6 7 5
## [2776] 5 2 3 7 6 4 3 2 6 6 1 6 7 6 1 4 4 2 1 7 1 6 5 6 4 2 5 3 4 4 6 4 6 7 3 4 4
## [2813] 6 1 5 6 6 5 6 4 4 3 4 4 3 6 4 4 7 5 6 1 6 5 1 3 6 6 5 4 5 6 1 3 7 4 5 4 5
## [2850] 2 4 5 4 6 5 5 5 4 1 2 7 4 6 6 4 1 5 3 5 2 4 6 4 1 6 4 5 4 1 6 3 5 1 5 4 1
## [2887] 3 4 4 2 5 4 4 3 6 1 5 7 5 4 1 3 4 4 5 5 1 6 5 1 4 5 4 1 4 5 6 5 6 4 4 3 4
## [2924] 4 1 6 4 4 6 4 5 1 6 4 1 3 6 5 4 3 3 6 3 3 7 4 3 5 5 1 3 3 1 3 3 4 6 1 4 2
## [2961] 4 1 4 7 4 1 5 3 4 1 1 6 4 1 5 4 4 4 1 4 1 4 1 5 5 1 2 4 4 1 1 1 4 2 5 1 5
## [2998] 7 5 4 1 4 3 2 3 5 1 6 5 1 4 5 4 1 4 5 4 6 6 4 4 2 4 4 1 5 4 4 7 4 4 1 6 4
## [3035] 1 1 6 4 4 3 3 5 1 3 4 4 2 1 5 1 2 2 1 3 2 4 6 1 2 1 4 2 4 7 2 1 5 1 4 1 1
## [3072] 6 4 1 5 4 3 3 1 3 1 3 1 4 5 1 5 4 4 1 1 1 3 2 5 1 5 5 7 4 5 5 2 4 7 5 1 6
## [3109] 7 1 6 6 7 4 6 6 7 4 6 5 6 7 5 4 1 7 6 7 6 7 5 1 7 6 1 3 5 6 5 4 6 7 5 4 5
## [3146] 5 4 5 7 2 6 4 6 6 5 5 6 4 4 3 4 7 5 6 3 6 7 7 7 2 3 7 5 2 6 5 7 6 6 5 2 3
## [3183] 4 6 4 1 1 7 4 3 2 4 4 6 5 1# Factor 6
mod_gen_driveAction <- lmer(driveAction_z ~ companyLeaning*action + pol + SGen + (1 | participantID) + (1 | social_issue), data = gjg_long)
summary(mod_gen_driveAction)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## driveAction_z ~ companyLeaning * action + pol + SGen + (1 | participantID) +
## (1 | social_issue)
## Data: gjg_long
##
## REML criterion at convergence: 3550
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7559 -0.4990 0.0297 0.5630 3.9194
##
## Random effects:
## Groups Name Variance Std.Dev.
## participantID (Intercept) 0.106840 0.3269
## social_issue (Intercept) 0.006006 0.0775
## Residual 0.119794 0.3461
## Number of obs: 3196, groups: participantID, 799; social_issue, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.168e+00 5.543e-02 1.188e+01 -21.061 8.93e-11
## companyLeaningRight 2.317e-02 1.772e-02 2.422e+03 1.307 0.19124
## actionI 6.114e-03 1.796e-02 2.432e+03 0.340 0.73356
## pol 1.833e-02 7.033e-03 7.868e+02 2.606 0.00933
## SGen 2.955e-01 4.671e-03 3.134e+03 63.272 < 2e-16
## companyLeaningRight:actionI -7.338e-02 2.459e-02 2.381e+03 -2.985 0.00287
##
## (Intercept) ***
## companyLeaningRight
## actionI
## pol **
## SGen ***
## companyLeaningRight:actionI **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cmpnLR actinI pol SGen
## cmpnyLnngRg -0.235
## actionI -0.241 0.521
## pol -0.519 0.000 0.000
## SGen -0.386 0.213 0.233 -0.002
## cmpnyLnnR:I 0.109 -0.687 -0.689 0.000 0.004