IS Study 1
Marcus
9/7/2023
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", "lavaan")
# install.packages(Mypackages) #you must remove the # in this comment if you need to install the packages!
lapply(Mypackages,
require,
character.only = TRUE)
options(knitr.kable.NA = '—')
set.seed(1) 1.1 Load Data
# read in data files
setwd("~/Desktop")
gjg_raw <-read.csv("/Users/mtrenfield17/Desktop/Research/Boston College Research/SISC Lab Research/I:S Project/Study 1 (Wildfire)/Scope Project Study 1 (Wildfire).csv")1.2 Functions
plot_cooker <- function(data, iv, dv) {
part1 <- ggplot(data, aes(x = {{iv}}, y = {{dv}}, fill = {{iv}})) +
geom_violin(alpha = 0.3, scale = "count") +
stat_summary(fun = "mean", geom = "point", size = 3, color = "black") +
stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2,
#change to make a data set from allEffects with mean, low CI, high CI
size = 1.5, color = "black") +
theme_classic() +
xlab("") +
ylab("")
ggpar(part1, legend = "none")
}
pol_line <- function(data, iv, dv) {
ggplot(data, aes(x = {{iv}}, y = {{dv}}, color = condition)) +
stat_summary(fun.data = "mean_cl_normal", geom = "line") +
geom_point(position = position_jitter(width = 0.1, height = 0.1), alpha = 0.5) +
labs(x = "Political Leaning", color = "Condition")
}
lizy_cooker <- function(dv, iv, Title, x_axis_labs, y_label, sample_size, coln, rown) {
part1 <- ggviolin(gjg, x = dv, y = iv, color = dv,
alpha = 0.1, fill = dv, xlab = "Motive",
trim = TRUE, ylab = y_label) +
stat_summary(fun.data = "mean_cl_normal", geom = "crossbar", fatten = 1) +
scale_y_continuous(breaks = c(1:7)) +
labs(title = paste0(Title, " (n = ", sample_size, ")")) +
theme(panel.background = element_rect(fill = "transparent"),
legend.position = "right", ## Consider “gray97” for fill
plot.title = element_text(face = "bold", hjust = 0.5, size = 16),
plot.subtitle = element_text(hjust = 0.5),
panel.grid.major.y = element_line(color='grey75'),
axis.text.x = element_text(face = "plain", size = 13, color = "black"),
axis.text.y = element_text(face = "plain", size = 13, color = "black"),
axis.title.y = element_text(face = "plain", size = 13, color = "black",
margin = margin(t = 0, r = 10, b = 0, l = 0)), ## lower X axis title
panel.border = element_rect(color = "black", fill = NA, size = 1)) +
scale_color_discrete(name = "Condition") +
facet_wrap(~ vignette, ncol = coln, nrow = rown, scales = "free", as.table = TRUE)
ggpar(part1, legend = "none")
}
#POL_gjg_long <- filter(gjg_long, political_overall %in% c("Democrat", "Republican"))
#gjg_long$political_overall
#ggplot(gjg_long, aes(x = condition, y = p_approve, color = condition)) +
#geom_point(stat="summary", fun="mean", size = 2) +
#facet_wrap(~political_overall) +
#scale_x_discrete(labels = NULL)1.3 Reshaping data
#### filtering people who failed the attn check ####
gjg <- gjg_raw %>% filter(attentionCheck == 3)
## changing condition to factor and reordering ##
gjg$condition <- as.factor(gjg$condition)
gjg$condition <- factor(gjg$condition, levels = c("individualScope", "populationScope", "mergedScope"))
# changing numeric DVs to numeric
gjg <- gjg %>% mutate_at(vars(policyDV, donation, helpDV_1, helpDV_2, helpDV_3, helpDV_4, helpDV_5, preventDV_1, preventDV_2, preventDV_3, preventDV_4, preventDV_5, feelingsMeasures_1, feelingsMeasures_2, feelingsMeasures_3, moral_1, moral_2, moral_3, blame_1, blame_2, efficacy_1, efficacy_2, efficacy_3, efficacy_4, age, pol, pid, edu, inc), as.numeric)
## renaming matrix variables
names(gjg)[names(gjg) == 'helpDV_1'] <-'usGovHelp'
names(gjg)[names(gjg) == 'helpDV_2'] <-'otherCountriesGovHelp'
names(gjg)[names(gjg) == 'helpDV_3'] <-'companiesHelp'
names(gjg)[names(gjg) == 'helpDV_4'] <-'homeownersHelp'
names(gjg)[names(gjg) == 'helpDV_5'] <-'youHelp'
names(gjg)[names(gjg) == 'preventDV_1'] <-'usGovPrevent'
names(gjg)[names(gjg) == 'preventDV_2'] <-'otherCountriesGovPrevent'
names(gjg)[names(gjg) == 'preventDV_3'] <-'companiesPrevent'
names(gjg)[names(gjg) == 'preventDV_4'] <-'homeownersPrevent'
names(gjg)[names(gjg) == 'preventDV_5'] <-'youPrevent'
names(gjg)[names(gjg) == 'feelingsMeasures_1'] <-'upset'
names(gjg)[names(gjg) == 'feelingsMeasures_2'] <-'sympathetic'
names(gjg)[names(gjg) == 'feelingsMeasures_3'] <-'touched'
names(gjg)[names(gjg) == 'moral_1'] <-'donatingMoral'
names(gjg)[names(gjg) == 'moral_2'] <-'takingPrecautionsMoral'
names(gjg)[names(gjg) == 'moral_3'] <-'companiesEmissionReductionMoral'
names(gjg)[names(gjg) == 'blame_1'] <-'companiesBlame'
names(gjg)[names(gjg) == 'blame_2'] <-'individualsBlame'
names(gjg)[names(gjg) == 'efficacy_1'] <-'studyDonationEfficacy'
names(gjg)[names(gjg) == 'efficacy_2'] <-'studyPolicyEfficacy'
names(gjg)[names(gjg) == 'efficacy_3'] <-'donationEfficacy'
names(gjg)[names(gjg) == 'efficacy_4'] <-'punishingIndividualsEfficacy'2 Attention Check
gjg_raw <- gjg_raw %>% filter(consent == 5)
gjg_raw %>%
group_by(attentionCheck) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = n / sum(n))0 participants failed the attention check.
3 Demographics
# Subset your data frame to include only the demographic columns
demo_gjg <- gjg[, c("gen", "race", "inc_TEXT", "edu_TEXT", "pol_TEXT", "pid_TEXT", "area")]
# Age
mean(gjg$age, na.rm=TRUE)## [1] 39.40787sd(gjg$age, na.rm=TRUE)## [1] 13.36321# 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 :
## Var1 Freq Percent
## 1 1 589 49.33
## 2 2 586 49.08
## 3 3 19 1.59
##
## Table of frequencies for race :
## Var1 Freq Percent
## 1 3 0.25
## 2 1 2 0.17
## 3 1,2 3 0.25
## 4 1,3,8 1 0.08
## 5 1,5 3 0.25
## 6 1,5,8 1 0.08
## 7 1,6,8 1 0.08
## 8 1,8 11 0.92
## 9 2 132 11.06
## 10 2,3,5,8 1 0.08
## 11 2,5 6 0.50
## 12 2,5,8 3 0.25
## 13 2,8 12 1.01
## 14 2,9,5,8 1 0.08
## 15 3 51 4.27
## 16 3,5 1 0.08
## 17 3,7 1 0.08
## 18 3,7,8 2 0.17
## 19 3,8 21 1.76
## 20 4 12 1.01
## 21 4,5 3 0.25
## 22 4,8 3 0.25
## 23 5 72 6.03
## 24 5,8 42 3.52
## 25 6 4 0.34
## 26 6,8 3 0.25
## 27 8 769 64.41
## 28 9 24 2.01
## 29 9,5 1 0.08
## 30 9,7,5 1 0.08
## 31 9,7,8 1 0.08
## 32 9,8 3 0.25
##
## Table of frequencies for inc_TEXT :
## Var1 Freq Percent
## 1 $100,000 - $149,999 152 12.73
## 2 $150,000 - $199,999 57 4.77
## 3 $25,000 - $49,999 299 25.04
## 4 $50,000 - $74,999 270 22.61
## 5 $75,000 - $99,999 183 15.33
## 6 less than $25,000 194 16.25
## 7 more than $200,000 39 3.27
##
## Table of frequencies for edu_TEXT :
## Var1 Freq Percent
## 1 Bachelor's degree 464 38.86
## 2 Graduate degree (Masters, PhD, etc) 160 13.40
## 3 High school diploma or GED 173 14.49
## 4 Some college, Technical degree, or Associates degree 388 32.50
## 5 Some schooling, but no high school diploma or degree 9 0.75
##
## Table of frequencies for pol_TEXT :
## Var1 Freq Percent
## 1 2 0.17
## 2 Conservative 102 8.54
## 3 Liberal 325 27.22
## 4 Moderate 244 20.44
## 5 Somewhat Conservative 107 8.96
## 6 Somewhat Liberal 137 11.47
## 7 Very Conservative 38 3.18
## 8 Very Liberal 239 20.02
##
## Table of frequencies for pid_TEXT :
## Var1 Freq Percent
## 1 Democrat 612 51.26
## 2 Independent / Other 377 31.57
## 3 Republican 205 17.17
##
## Table of frequencies for area :
## Var1 Freq Percent
## 1 1 385 32.24
## 2 2 597 50.00
## 3 3 212 17.764 Correlations
DVs <- gjg[c("policyDV", "donation", "usGovHelp", "otherCountriesGovHelp", "companiesHelp", "homeownersHelp", "youHelp", "usGovPrevent", "otherCountriesGovPrevent", "companiesPrevent", "homeownersPrevent", "youPrevent", "upset", "sympathetic", "touched", "donatingMoral", "takingPrecautionsMoral", "companiesEmissionReductionMoral", "companiesBlame", "individualsBlame", "studyDonationEfficacy", "studyPolicyEfficacy", "donationEfficacy", "punishingIndividualsEfficacy", "pol", "edu", "inc", "age")]
# Compute pairwise correlations
corr_DVs <- cor(DVs, use = "complete.obs")
# Plot the correlation matrix
corrplot(corr_DVs, is.corr = TRUE, type = "lower", lower = "circle", tl.cex = 0.7, insig = "label_sig", diag = TRUE)
5 EFA
5.1 Evaluating the correlation matrix
5.2 Determining number of factors
Scree plot suggests 3 or 5 factors
eigenvalue method suggests 6
Parallel Analysis suggests 8
5.3 5 Factors
6 CFA
7 Behavioral Outcomes
percep_plot_list <- list(plot_cooker(gjg, condition, donation),
plot_cooker(gjg, condition, policyDV))
# Adding titles to each plot
percep_plot_list[[1]] <- percep_plot_list[[1]] +
ggtitle("Donations")
percep_plot_list[[2]] <- percep_plot_list[[2]] +
ggtitle("Support for Policy")
percep_plot_arranged <- ggarrange(plotlist = percep_plot_list, ncol = 2, nrow = 1)
overall_percep_title <- ggdraw() +
draw_label("Behavioral DVs", fontface = "bold")
plot_grid(overall_percep_title, percep_plot_arranged, ncol = 1, rel_heights = c(0.1, 0.9))
pol_line(gjg, pol, policyDV)
pol_line(gjg, pol, donation)
7.1 Inferential Stats
gjg$condition <- relevel(gjg$condition, ref = "mergedScope")
gjg_I <- gjg
gjg_I$condition <- relevel(gjg_I$condition, ref = "individualScope")
mod_donation<- lm(donation ~ condition + pol, data = gjg)
summary(mod_donation)##
## Call:
## lm(formula = donation ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4049 -2.6948 -0.9789 2.0211 7.7955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.38159 0.30147 7.900 6.32e-15 ***
## conditionindividualScope 0.02921 0.22232 0.131 0.89548
## conditionpopulationScope -0.31911 0.22173 -1.439 0.15036
## pol 0.14202 0.05325 2.667 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.134 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.008208, Adjusted R-squared: 0.005703
## F-statistic: 3.277 on 3 and 1188 DF, p-value: 0.02039mod_policy<- lm(policyDV ~ condition + pol, data = gjg)
summary(mod_policy)##
## Call:
## lm(formula = policyDV ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7659 -0.7778 0.2110 0.8493 3.9969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.39908 0.13023 18.422 <2e-16 ***
## conditionindividualScope -0.01124 0.09604 -0.117 0.907
## conditionpopulationScope -0.02309 0.09578 -0.241 0.810
## pol 0.62714 0.02300 27.266 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.3851, Adjusted R-squared: 0.3835
## F-statistic: 248 on 3 and 1188 DF, p-value: < 2.2e-16mod_donationI <- lm(donation ~ condition + pol, data = gjg_I)
summary(mod_donationI)##
## Call:
## lm(formula = donation ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4049 -2.6948 -0.9789 2.0211 7.7955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41081 0.30449 7.918 5.53e-15 ***
## conditionmergedScope -0.02921 0.22232 -0.131 0.89548
## conditionpopulationScope -0.34833 0.22320 -1.561 0.11889
## pol 0.14202 0.05325 2.667 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.134 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.008208, Adjusted R-squared: 0.005703
## F-statistic: 3.277 on 3 and 1188 DF, p-value: 0.02039mod_policyI <- lm(policyDV ~ condition + pol, data = gjg_I)
summary(mod_policyI)##
## Call:
## lm(formula = policyDV ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7659 -0.7778 0.2110 0.8493 3.9969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.38784 0.13153 18.154 <2e-16 ***
## conditionmergedScope 0.01124 0.09604 0.117 0.907
## conditionpopulationScope -0.01185 0.09642 -0.123 0.902
## pol 0.62714 0.02300 27.266 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.3851, Adjusted R-squared: 0.3835
## F-statistic: 248 on 3 and 1188 DF, p-value: < 2.2e-168 Responsibility (Help and Prevent)
gjg_long<-gjg %>% gather(stim, resp, "usGovHelp":"punishingIndividualsEfficacy")
responsibilityHelpLong <- gjg_long %>%
filter(grepl("Help", stim))
facet_cooker <- function(data, iv, dv, coln, rown) {
part1 <- ggplot(data, aes(x = {{iv}}, y = {{dv}}, fill = {{iv}})) +
geom_violin(alpha = 0.3, scale = "count") +
stat_summary(fun = "mean", geom = "point", size = 3, color = "black") +
stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2,
#change to make a data set from allEffects with mean, low CI, high CI
size = 1.5, color = "black") +
theme_classic() +
xlab("") +
ylab("") +
facet_wrap(~ stim, ncol = coln, nrow = rown, scales = "free", as.table = TRUE)
ggpar(part1, legend = "none")
}
facet_cooker(responsibilityHelpLong, condition, resp, coln = 3, rown = 2)
responsibilityPreventLong <- gjg_long %>%
filter(grepl("Prevent", stim))
facet_cooker(responsibilityPreventLong, condition, resp, coln = 3, rown = 2)
8.1 Inferential Stats
pop v merged v individuals other countries help
pop v merged v individuals companies help
merged v pop you help marginal
pop v merged v individuals other countries prevent
pop v merged v individuals other countries help
mod_usGovHelp <- lm(usGovHelp ~ condition + pol, data = gjg)
summary(mod_usGovHelp)##
## Call:
## lm(formula = usGovHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7967 -0.5786 0.2033 0.4214 1.5122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.30063 0.08123 40.632 <2e-16 ***
## conditionindividualScope -0.02454 0.05991 -0.410 0.682
## conditionpopulationScope -0.03099 0.05975 -0.519 0.604
## pol 0.21816 0.01435 15.205 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8444 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.163, Adjusted R-squared: 0.1609
## F-statistic: 77.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_usGovHelpI <- lm(usGovHelp ~ condition + pol, data = gjg_I)
summary(mod_usGovHelpI)##
## Call:
## lm(formula = usGovHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7967 -0.5786 0.2033 0.4214 1.5122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.27609 0.08205 39.930 <2e-16 ***
## conditionmergedScope 0.02454 0.05990 0.410 0.682
## conditionpopulationScope -0.00645 0.06014 -0.107 0.915
## pol 0.21816 0.01435 15.205 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8444 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.163, Adjusted R-squared: 0.1609
## F-statistic: 77.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_otherCountriesGovHelp <- lm(otherCountriesGovHelp ~ condition + pol, data = gjg)
summary(mod_otherCountriesGovHelp)##
## Call:
## lm(formula = otherCountriesGovHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.757 -1.312 0.243 1.286 2.770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30732 0.13888 16.613 < 2e-16 ***
## conditionindividualScope -0.27854 0.10242 -2.720 0.00663 **
## conditionpopulationScope 0.04337 0.10215 0.425 0.67120
## pol 0.20090 0.02453 8.190 6.69e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.444 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.06265, Adjusted R-squared: 0.06028
## F-statistic: 26.47 on 3 and 1188 DF, p-value: < 2.2e-16mod_otherCountriesGovHelpI <- lm(otherCountriesGovHelp ~ condition + pol, data = gjg_I)
summary(mod_otherCountriesGovHelpI)##
## Call:
## lm(formula = otherCountriesGovHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.757 -1.312 0.243 1.286 2.770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.02879 0.14027 14.463 < 2e-16 ***
## conditionmergedScope 0.27854 0.10242 2.720 0.00663 **
## conditionpopulationScope 0.32191 0.10283 3.131 0.00179 **
## pol 0.20090 0.02453 8.190 6.69e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.444 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.06265, Adjusted R-squared: 0.06028
## F-statistic: 26.47 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesHelp <- lm(companiesHelp ~ condition + pol, data = gjg)
summary(mod_companiesHelp)##
## Call:
## lm(formula = companiesHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7248 -0.7075 0.2752 0.6689 2.5511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07250 0.10793 19.202 <2e-16 ***
## conditionindividualScope -0.16397 0.07959 -2.060 0.0396 *
## conditionpopulationScope 0.01737 0.07938 0.219 0.8269
## pol 0.37642 0.01906 19.747 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.122 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2509, Adjusted R-squared: 0.249
## F-statistic: 132.6 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesHelpI <- lm(companiesHelp ~ condition + pol, data = gjg_I)
summary(mod_companiesHelpI)##
## Call:
## lm(formula = companiesHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7248 -0.7075 0.2752 0.6689 2.5511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.90854 0.10901 17.508 <2e-16 ***
## conditionmergedScope 0.16397 0.07959 2.060 0.0396 *
## conditionpopulationScope 0.18133 0.07991 2.269 0.0234 *
## pol 0.37642 0.01906 19.747 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.122 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2509, Adjusted R-squared: 0.249
## F-statistic: 132.6 on 3 and 1188 DF, p-value: < 2.2e-16mod_homeownersHelp <- lm(homeownersHelp ~ condition + pol, data = gjg)
summary(mod_homeownersHelp)##
## Call:
## lm(formula = homeownersHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6100 -0.5711 -0.4587 0.5025 2.5996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.45182 0.10336 23.722 <2e-16 ***
## conditionindividualScope 0.02217 0.07622 0.291 0.771
## conditionpopulationScope -0.09026 0.07602 -1.187 0.235
## pol 0.01942 0.01825 1.064 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.074 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002906, Adjusted R-squared: 0.0003877
## F-statistic: 1.154 on 3 and 1188 DF, p-value: 0.3262mod_homeownersHelpI <- lm(homeownersHelp ~ condition + pol, data = gjg_I)
summary(mod_homeownersHelpI)##
## Call:
## lm(formula = homeownersHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6100 -0.5711 -0.4587 0.5025 2.5996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.47399 0.10439 23.699 <2e-16 ***
## conditionmergedScope -0.02217 0.07622 -0.291 0.771
## conditionpopulationScope -0.11242 0.07652 -1.469 0.142
## pol 0.01942 0.01825 1.064 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.074 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002906, Adjusted R-squared: 0.0003877
## F-statistic: 1.154 on 3 and 1188 DF, p-value: 0.3262mod_youHelp <- lm(youHelp ~ condition + pol, data = gjg)
summary(mod_youHelp)##
## Call:
## lm(formula = youHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4936 -0.4534 -0.3184 0.6414 2.8681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.21228 0.09947 22.240 <2e-16 ***
## conditionindividualScope -0.09484 0.07336 -1.293 0.1963
## conditionpopulationScope -0.12053 0.07316 -1.648 0.0997 .
## pol 0.04019 0.01757 2.288 0.0223 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.034 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.006705, Adjusted R-squared: 0.004197
## F-statistic: 2.673 on 3 and 1188 DF, p-value: 0.0461mod_youHelpI <- lm(youHelp ~ condition + pol, data = gjg_I)
summary(mod_youHelpI)##
## Call:
## lm(formula = youHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4936 -0.4534 -0.3184 0.6414 2.8681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.11743 0.10047 21.076 <2e-16 ***
## conditionmergedScope 0.09484 0.07336 1.293 0.1963
## conditionpopulationScope -0.02569 0.07365 -0.349 0.7273
## pol 0.04019 0.01757 2.288 0.0223 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.034 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.006705, Adjusted R-squared: 0.004197
## F-statistic: 2.673 on 3 and 1188 DF, p-value: 0.0461mod_usGovPrevent <- lm(usGovPrevent ~ condition + pol, data = gjg)
summary(mod_usGovPrevent)##
## Call:
## lm(formula = usGovPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3652 -0.5725 0.1705 0.4506 1.7123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.08038 0.08363 36.834 <2e-16 ***
## conditionindividualScope -0.04492 0.06167 -0.728 0.467
## conditionpopulationScope -0.04965 0.06151 -0.807 0.420
## pol 0.25697 0.01477 17.398 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8693 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.2012
## F-statistic: 101 on 3 and 1188 DF, p-value: < 2.2e-16mod_usGovPreventI <- lm(usGovPrevent ~ condition + pol, data = gjg_I)
summary(mod_usGovPreventI)##
## Call:
## lm(formula = usGovPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3652 -0.5725 0.1705 0.4506 1.7123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.035461 0.084466 35.937 <2e-16 ***
## conditionmergedScope 0.044918 0.061672 0.728 0.467
## conditionpopulationScope -0.004737 0.061917 -0.076 0.939
## pol 0.256971 0.014771 17.398 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8693 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2032, Adjusted R-squared: 0.2012
## F-statistic: 101 on 3 and 1188 DF, p-value: < 2.2e-16mod_otherCountriesGovPrevent <- lm(otherCountriesGovPrevent ~ condition + pol, data = gjg)
summary(mod_otherCountriesGovPrevent)##
## Call:
## lm(formula = otherCountriesGovPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2497 -0.9642 0.2634 1.0358 2.6909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.23249 0.12561 17.773 <2e-16 ***
## conditionindividualScope -0.20887 0.09263 -2.255 0.0243 *
## conditionpopulationScope 0.01864 0.09239 0.202 0.8401
## pol 0.28551 0.02219 12.869 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1277, Adjusted R-squared: 0.1255
## F-statistic: 57.98 on 3 and 1188 DF, p-value: < 2.2e-16mod_otherCountriesGovPreventI <- lm(otherCountriesGovPrevent ~ condition + pol, data = gjg_I)
summary(mod_otherCountriesGovPreventI)##
## Call:
## lm(formula = otherCountriesGovPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2497 -0.9642 0.2634 1.0358 2.6909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.02361 0.12687 15.950 <2e-16 ***
## conditionmergedScope 0.20887 0.09263 2.255 0.0243 *
## conditionpopulationScope 0.22752 0.09300 2.446 0.0146 *
## pol 0.28551 0.02219 12.869 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.306 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1277, Adjusted R-squared: 0.1255
## F-statistic: 57.98 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesPrevent <- lm(companiesPrevent ~ condition + pol, data = gjg)
summary(mod_companiesPrevent)##
## Call:
## lm(formula = companiesPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8824 -0.5128 0.1265 0.4961 2.3442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.36101 0.09941 23.750 <2e-16 ***
## conditionindividualScope -0.07488 0.07331 -1.021 0.307
## conditionpopulationScope -0.06595 0.07312 -0.902 0.367
## pol 0.36963 0.01756 21.052 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.033 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.272, Adjusted R-squared: 0.2702
## F-statistic: 148 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesPreventI <- lm(companiesPrevent ~ condition + pol, data = gjg_I)
summary(mod_companiesPreventI)##
## Call:
## lm(formula = companiesPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8824 -0.5128 0.1265 0.4961 2.3442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.286131 0.100404 22.769 <2e-16 ***
## conditionmergedScope 0.074879 0.073310 1.021 0.307
## conditionpopulationScope 0.008926 0.073600 0.121 0.903
## pol 0.369628 0.017558 21.052 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.033 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.272, Adjusted R-squared: 0.2702
## F-statistic: 148 on 3 and 1188 DF, p-value: < 2.2e-16mod_homeownersPrevent <- lm(homeownersPrevent ~ condition + pol, data = gjg)
summary(mod_homeownersPrevent)##
## Call:
## lm(formula = homeownersPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4247 -0.9668 -0.1479 0.8587 2.3397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.56804 0.11050 23.240 < 2e-16 ***
## conditionindividualScope 0.21087 0.08149 2.588 0.00978 **
## conditionpopulationScope 0.02979 0.08127 0.366 0.71406
## pol 0.09225 0.01952 4.727 2.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.149 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02469, Adjusted R-squared: 0.02223
## F-statistic: 10.03 on 3 and 1188 DF, p-value: 1.583e-06mod_homeownersPreventI <- lm(homeownersPrevent ~ condition + pol, data = gjg_I)
summary(mod_homeownersPreventI)##
## Call:
## lm(formula = homeownersPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4247 -0.9668 -0.1479 0.8587 2.3397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.77891 0.11161 24.899 < 2e-16 ***
## conditionmergedScope -0.21087 0.08149 -2.588 0.00978 **
## conditionpopulationScope -0.18109 0.08181 -2.213 0.02706 *
## pol 0.09225 0.01952 4.727 2.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.149 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02469, Adjusted R-squared: 0.02223
## F-statistic: 10.03 on 3 and 1188 DF, p-value: 1.583e-06mod_youPrevent <- lm(youPrevent ~ condition + pol, data = gjg)
summary(mod_youPrevent)##
## Call:
## lm(formula = youPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1962 -0.9895 -0.1221 0.9917 2.6734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.19403 0.11703 18.747 < 2e-16 ***
## conditionindividualScope 0.07408 0.08631 0.858 0.391
## conditionpopulationScope 0.01877 0.08608 0.218 0.827
## pol 0.13259 0.02067 6.414 2.04e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.217 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03414, Adjusted R-squared: 0.0317
## F-statistic: 14 on 3 and 1188 DF, p-value: 5.686e-09mod_youPreventI <- lm(youPrevent ~ condition + pol, data = gjg_I)
summary(mod_youPreventI)##
## Call:
## lm(formula = youPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1962 -0.9895 -0.1221 0.9917 2.6734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26811 0.11820 19.188 < 2e-16 ***
## conditionmergedScope -0.07408 0.08631 -0.858 0.391
## conditionpopulationScope -0.05531 0.08665 -0.638 0.523
## pol 0.13259 0.02067 6.414 2.04e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.217 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03414, Adjusted R-squared: 0.0317
## F-statistic: 14 on 3 and 1188 DF, p-value: 5.686e-099 Affect Help
affectLong <- gjg_long %>%
filter(stim %in% c("upset", "sympathetic", "touched"))
facet_cooker(affectLong, condition, resp, coln = 2, rown = 2)
9.1 Inferential Stats
- merged v pop Sympathetic, touched significant
mod_upset <- lm(upset ~ condition + pol, data = gjg)
summary(mod_upset)##
## Call:
## lm(formula = upset ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33857 -0.98112 0.01888 0.81120 2.60550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.311984 0.116328 19.875 < 2e-16 ***
## conditionindividualScope -0.064138 0.085787 -0.748 0.455
## conditionpopulationScope -0.003109 0.085559 -0.036 0.971
## pol 0.146655 0.020546 7.138 1.65e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.209 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04178, Adjusted R-squared: 0.03936
## F-statistic: 17.27 on 3 and 1188 DF, p-value: 5.601e-11mod_upsetI <- lm(upset ~ condition + pol, data = gjg_I)
summary(mod_upsetI)##
## Call:
## lm(formula = upset ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33857 -0.98112 0.01888 0.81120 2.60550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.24785 0.11749 19.132 < 2e-16 ***
## conditionmergedScope 0.06414 0.08579 0.748 0.455
## conditionpopulationScope 0.06103 0.08613 0.709 0.479
## pol 0.14665 0.02055 7.138 1.65e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.209 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04178, Adjusted R-squared: 0.03936
## F-statistic: 17.27 on 3 and 1188 DF, p-value: 5.601e-11mod_sympathetic <- lm(sympathetic ~ condition + pol, data = gjg)
summary(mod_sympathetic)##
## Call:
## lm(formula = sympathetic ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9735 -0.6966 0.1373 0.8576 1.9684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.19765 0.10192 31.375 < 2e-16 ***
## conditionindividualScope 0.22264 0.07516 2.962 0.003115 **
## conditionpopulationScope -0.27687 0.07496 -3.694 0.000231 ***
## pol 0.11083 0.01800 6.157 1.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.059 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.063, Adjusted R-squared: 0.06063
## F-statistic: 26.62 on 3 and 1188 DF, p-value: < 2.2e-16mod_sympatheticI <- lm(sympathetic ~ condition + pol, data = gjg_I)
summary(mod_sympatheticI)##
## Call:
## lm(formula = sympathetic ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9735 -0.6966 0.1373 0.8576 1.9684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.42029 0.10294 33.226 < 2e-16 ***
## conditionmergedScope -0.22264 0.07516 -2.962 0.00312 **
## conditionpopulationScope -0.49951 0.07546 -6.620 5.44e-11 ***
## pol 0.11083 0.01800 6.157 1.01e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.059 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.063, Adjusted R-squared: 0.06063
## F-statistic: 26.62 on 3 and 1188 DF, p-value: < 2.2e-16mod_touched <- lm(touched ~ condition + pol, data = gjg)
summary(mod_touched)##
## Call:
## lm(formula = touched ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.32744 -1.02778 -0.02778 0.97222 2.53098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.71243 0.12041 22.527 < 2e-16 ***
## conditionindividualScope 0.06315 0.08880 0.711 0.477128
## conditionpopulationScope -0.32225 0.08856 -3.639 0.000286 ***
## pol 0.07884 0.02127 3.707 0.000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.252 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02803, Adjusted R-squared: 0.02558
## F-statistic: 11.42 on 3 and 1188 DF, p-value: 2.19e-07mod_touchedI <- lm(touched ~ condition + pol, data = gjg_I)
summary(mod_touchedI)##
## Call:
## lm(formula = touched ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.32744 -1.02778 -0.02778 0.97222 2.53098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.77557 0.12161 22.823 < 2e-16 ***
## conditionmergedScope -0.06315 0.08880 -0.711 0.477128
## conditionpopulationScope -0.38540 0.08915 -4.323 1.67e-05 ***
## pol 0.07884 0.02127 3.707 0.000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.252 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02803, Adjusted R-squared: 0.02558
## F-statistic: 11.42 on 3 and 1188 DF, p-value: 2.19e-0710 Efficacy
efficacyHelpLong <- gjg_long %>%
filter(grepl("Efficacy", stim))
facet_cooker(efficacyHelpLong, condition, resp, coln = 2, rown = 2)
10.1 Inferential Stats
merged v pop study donation efficacy marginal
merged v ind. punishing individuals significant
mod_studyDonationEfficacy <- lm(studyDonationEfficacy ~ condition + pol, data = gjg)
summary(mod_studyDonationEfficacy)##
## Call:
## lm(formula = studyDonationEfficacy ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8851 -0.6513 0.2740 0.6670 1.8261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23243 0.10745 30.084 < 2e-16 ***
## conditionindividualScope 0.09567 0.07924 1.207 0.2275
## conditionpopulationScope -0.13812 0.07903 -1.748 0.0808 .
## pol 0.07957 0.01898 4.193 2.96e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.117 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02116, Adjusted R-squared: 0.01869
## F-statistic: 8.56 on 3 and 1188 DF, p-value: 1.264e-05mod_studyDonationEfficacyI <- lm(studyDonationEfficacy ~ condition + pol, data = gjg_I)
summary(mod_studyDonationEfficacyI)##
## Call:
## lm(formula = studyDonationEfficacy ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8851 -0.6513 0.2740 0.6670 1.8261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32810 0.10852 30.667 < 2e-16 ***
## conditionmergedScope -0.09567 0.07924 -1.207 0.22754
## conditionpopulationScope -0.23379 0.07955 -2.939 0.00336 **
## pol 0.07957 0.01898 4.193 2.96e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.117 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02116, Adjusted R-squared: 0.01869
## F-statistic: 8.56 on 3 and 1188 DF, p-value: 1.264e-05mod_studyPolicyEfficacy <- lm(studyPolicyEfficacy ~ condition + pol, data = gjg)
summary(mod_studyPolicyEfficacy)##
## Call:
## lm(formula = studyPolicyEfficacy ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35469 -0.78194 -0.09137 0.74875 2.69772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.23550 0.10403 21.490 <2e-16 ***
## conditionindividualScope -0.09310 0.07671 -1.214 0.225
## conditionpopulationScope -0.10344 0.07651 -1.352 0.177
## pol 0.15988 0.01837 8.702 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.081 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.06111, Adjusted R-squared: 0.05874
## F-statistic: 25.77 on 3 and 1188 DF, p-value: 3.731e-16mod_studyPolicyEfficacyI <- lm(studyPolicyEfficacy ~ condition + pol, data = gjg_I)
summary(mod_studyPolicyEfficacyI)##
## Call:
## lm(formula = studyPolicyEfficacy ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.35469 -0.78194 -0.09137 0.74875 2.69772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.14240 0.10507 20.391 <2e-16 ***
## conditionmergedScope 0.09310 0.07671 1.214 0.225
## conditionpopulationScope -0.01034 0.07702 -0.134 0.893
## pol 0.15988 0.01837 8.702 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.081 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.06111, Adjusted R-squared: 0.05874
## F-statistic: 25.77 on 3 and 1188 DF, p-value: 3.731e-16mod_donationEfficacy <- lm(donationEfficacy ~ condition + pol, data = gjg)
summary(mod_donationEfficacy)##
## Call:
## lm(formula = donationEfficacy ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3004 -1.2127 -0.2198 0.7802 2.8320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29337 0.11835 19.378 <2e-16 ***
## conditionindividualScope -0.03130 0.08728 -0.359 0.720
## conditionpopulationScope 0.02049 0.08705 0.235 0.814
## pol -0.01344 0.02090 -0.643 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.23 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0006326, Adjusted R-squared: -0.001891
## F-statistic: 0.2507 on 3 and 1188 DF, p-value: 0.8609mod_donationEfficacyI <- lm(donationEfficacy ~ condition + pol, data = gjg_I)
summary(mod_donationEfficacyI)##
## Call:
## lm(formula = donationEfficacy ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3004 -1.2127 -0.2198 0.7802 2.8320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26207 0.11953 18.924 <2e-16 ***
## conditionmergedScope 0.03130 0.08728 0.359 0.720
## conditionpopulationScope 0.05180 0.08762 0.591 0.555
## pol -0.01344 0.02090 -0.643 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.23 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0006326, Adjusted R-squared: -0.001891
## F-statistic: 0.2507 on 3 and 1188 DF, p-value: 0.8609mod_punishingIndividualsEfficacy <- lm(punishingIndividualsEfficacy ~ condition + pol, data = gjg)
summary(mod_punishingIndividualsEfficacy)##
## Call:
## lm(formula = punishingIndividualsEfficacy ~ condition + pol,
## data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4277 -1.1229 -0.1727 0.8024 1.9519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.02322 0.11469 26.361 < 2e-16 ***
## conditionindividualScope 0.23004 0.08458 2.720 0.00662 **
## conditionpopulationScope 0.12706 0.08435 1.506 0.13226
## pol 0.02492 0.02026 1.230 0.21888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.192 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.007545, Adjusted R-squared: 0.005038
## F-statistic: 3.01 on 3 and 1188 DF, p-value: 0.02928mod_punishingIndividualsEfficacyI <- lm(punishingIndividualsEfficacy ~ condition + pol, data = gjg_I)
summary(mod_punishingIndividualsEfficacyI)##
## Call:
## lm(formula = punishingIndividualsEfficacy ~ condition + pol,
## data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4277 -1.1229 -0.1727 0.8024 1.9519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.25327 0.11583 28.086 < 2e-16 ***
## conditionmergedScope -0.23004 0.08458 -2.720 0.00662 **
## conditionpopulationScope -0.10299 0.08491 -1.213 0.22542
## pol 0.02492 0.02026 1.230 0.21888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.192 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.007545, Adjusted R-squared: 0.005038
## F-statistic: 3.01 on 3 and 1188 DF, p-value: 0.0292811 Morality
moralIssueLong <- gjg_long %>%
filter(grepl("Moral", stim))
facet_cooker(moralIssueLong, condition, resp, coln = 2, rown = 2)
blameLong <- gjg_long %>%
filter(grepl("Blame", stim))
facet_cooker(blameLong, condition, resp, coln = 2, rown = 2)
11.1 Inferential Stats
ind. v pop. Company emission reduction marginal
pop v merged v ind. company blame
pop v merged v ind. individual blame significant
mod_donatingMoral <- lm(donatingMoral ~ condition + pol, data = gjg)
summary(mod_donatingMoral)##
## Call:
## lm(formula = donatingMoral ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11391 -0.95447 -0.00135 0.97882 2.16543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.80121 0.11595 24.159 <2e-16 ***
## conditionindividualScope 0.07920 0.08551 0.926 0.354
## conditionpopulationScope 0.01983 0.08528 0.233 0.816
## pol 0.03336 0.02048 1.629 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.003007, Adjusted R-squared: 0.0004893
## F-statistic: 1.194 on 3 and 1188 DF, p-value: 0.3106mod_donatingMoralI <- lm(donatingMoral ~ condition + pol, data = gjg_I)
summary(mod_donatingMoralI)##
## Call:
## lm(formula = donatingMoral ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11391 -0.95447 -0.00135 0.97882 2.16543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.88042 0.11711 24.596 <2e-16 ***
## conditionmergedScope -0.07920 0.08551 -0.926 0.354
## conditionpopulationScope -0.05937 0.08584 -0.692 0.489
## pol 0.03336 0.02048 1.629 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.205 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.003007, Adjusted R-squared: 0.0004893
## F-statistic: 1.194 on 3 and 1188 DF, p-value: 0.3106mod_takingPrecautionsMoral <- lm(takingPrecautionsMoral ~ condition + pol, data = gjg)
summary(mod_takingPrecautionsMoral)##
## Call:
## lm(formula = takingPrecautionsMoral ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0134 -0.8207 0.1793 1.0724 1.4578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.47794 0.11025 31.547 < 2e-16 ***
## conditionindividualScope 0.08580 0.08130 1.055 0.291486
## conditionpopulationScope 0.06315 0.08109 0.779 0.436282
## pol 0.06424 0.01947 3.299 0.000999 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.146 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01019, Adjusted R-squared: 0.007693
## F-statistic: 4.078 on 3 and 1188 DF, p-value: 0.006803mod_takingPrecautionsMoralI <- lm(takingPrecautionsMoral ~ condition + pol, data = gjg_I)
summary(mod_takingPrecautionsMoralI)##
## Call:
## lm(formula = takingPrecautionsMoral ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0134 -0.8207 0.1793 1.0724 1.4578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.56374 0.11135 32.005 < 2e-16 ***
## conditionmergedScope -0.08580 0.08130 -1.055 0.291486
## conditionpopulationScope -0.02266 0.08162 -0.278 0.781405
## pol 0.06424 0.01947 3.299 0.000999 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.146 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01019, Adjusted R-squared: 0.007693
## F-statistic: 4.078 on 3 and 1188 DF, p-value: 0.006803mod_companiesEmissionReductionMoral <- lm(companiesEmissionReductionMoral ~ condition + pol, data = gjg)
summary(mod_companiesEmissionReductionMoral)##
## Call:
## lm(formula = companiesEmissionReductionMoral ~ condition + pol,
## data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4122 -0.7122 0.0894 0.7894 2.8393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.86563 0.11209 16.644 <2e-16 ***
## conditionindividualScope -0.05492 0.08266 -0.664 0.507
## conditionpopulationScope 0.09671 0.08244 1.173 0.241
## pol 0.34998 0.01980 17.678 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.165 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2112, Adjusted R-squared: 0.2093
## F-statistic: 106.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesEmissionReductionMoralI <- lm(companiesEmissionReductionMoral ~ condition + pol, data = gjg_I)
summary(mod_companiesEmissionReductionMoralI)##
## Call:
## lm(formula = companiesEmissionReductionMoral ~ condition + pol,
## data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4122 -0.7122 0.0894 0.7894 2.8393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.81071 0.11321 15.994 <2e-16 ***
## conditionmergedScope 0.05492 0.08266 0.664 0.5066
## conditionpopulationScope 0.15163 0.08299 1.827 0.0679 .
## pol 0.34998 0.01980 17.678 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.165 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2112, Adjusted R-squared: 0.2093
## F-statistic: 106.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesBlame <- lm(companiesBlame ~ condition + pol, data = gjg)
summary(mod_companiesBlame)##
## Call:
## lm(formula = companiesBlame ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6430 -0.6434 0.1716 0.7516 3.0177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.85561 0.10583 17.535 < 2e-16 ***
## conditionindividualScope -0.26783 0.07804 -3.432 0.00062 ***
## conditionpopulationScope 0.02541 0.07783 0.326 0.74418
## pol 0.39456 0.01869 21.110 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2814, Adjusted R-squared: 0.2796
## F-statistic: 155.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_companiesBlameI <- lm(companiesBlame ~ condition + pol, data = gjg_I)
summary(mod_companiesBlameI)##
## Call:
## lm(formula = companiesBlame ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6430 -0.6434 0.1716 0.7516 3.0177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.58778 0.10689 14.855 < 2e-16 ***
## conditionmergedScope 0.26783 0.07804 3.432 0.000620 ***
## conditionpopulationScope 0.29324 0.07835 3.743 0.000191 ***
## pol 0.39456 0.01869 21.110 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2814, Adjusted R-squared: 0.2796
## F-statistic: 155.1 on 3 and 1188 DF, p-value: < 2.2e-16mod_individualsBlame <- lm(individualsBlame ~ condition + pol, data = gjg)
summary(mod_individualsBlame)##
## Call:
## lm(formula = individualsBlame ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6999 -0.6175 0.3619 0.6442 1.6647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.47936 0.10798 32.223 < 2e-16 ***
## conditionindividualScope 0.24110 0.07963 3.028 0.00252 **
## conditionpopulationScope 0.20013 0.07942 2.520 0.01187 *
## pol -0.02058 0.01907 -1.079 0.28066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.122 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.009646, Adjusted R-squared: 0.007145
## F-statistic: 3.857 on 3 and 1188 DF, p-value: 0.009224mod_individualsBlameI <- lm(individualsBlame ~ condition + pol, data = gjg_I)
summary(mod_companiesBlameI)##
## Call:
## lm(formula = companiesBlame ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6430 -0.6434 0.1716 0.7516 3.0177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.58778 0.10689 14.855 < 2e-16 ***
## conditionmergedScope 0.26783 0.07804 3.432 0.000620 ***
## conditionpopulationScope 0.29324 0.07835 3.743 0.000191 ***
## pol 0.39456 0.01869 21.110 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1188 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2814, Adjusted R-squared: 0.2796
## F-statistic: 155.1 on 3 and 1188 DF, p-value: < 2.2e-16