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 (Texting While Driving)/Scope Project Study 1 (Text and Drive).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 == 4)
## 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, preventDV_1, preventDV_2, preventDV_3, preventDV_4, 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'] <-'manufacturerHelp'
names(gjg)[names(gjg) == 'helpDV_3'] <-'driverHelp'
names(gjg)[names(gjg) == 'helpDV_4'] <-'youHelp'
names(gjg)[names(gjg) == 'preventDV_1'] <-'usGovPrevent'
names(gjg)[names(gjg) == 'preventDV_2'] <-'manufacturerPrevent'
names(gjg)[names(gjg) == 'preventDV_3'] <-'driverPrevent'
names(gjg)[names(gjg) == 'preventDV_4'] <-'youPrevent'
names(gjg)[names(gjg) == 'feelingsMeasures_1'] <-'upset'
names(gjg)[names(gjg) == 'feelingsMeasures_2'] <-'sympathetic'
names(gjg)[names(gjg) == 'feelingsMeasures_3'] <-'touched'
names(gjg)[names(gjg) == 'moral_1'] <-'donatingMoral'
names(gjg)[names(gjg) == 'moral_2'] <-'takingPrecautionsMoral'
names(gjg)[names(gjg) == 'moral_3'] <-'manufacturersMoral'
names(gjg)[names(gjg) == 'blame_1'] <-'manufacturerBlame'
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 == 8)
gjg_raw %>%
group_by(attentionCheck) %>%
dplyr::summarise(n = n()) %>%
mutate(freq = n / sum(n))1 participant 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$age, na.rm=TRUE)## [1] 43.47957sd(gjg$age, na.rm=TRUE)## [1] 13.88894# 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 1 608 50.71
## 2 2 573 47.79
## 3 3 18 1.50
##
## Table of frequencies for race :
## Var1 Freq Percent
## 1 1 0.08
## 2 1 1 0.08
## 3 1,2 2 0.17
## 4 1,2,3,4,9,7,5,6,8 1 0.08
## 5 1,2,5 1 0.08
## 6 1,2,5,8 1 0.08
## 7 1,2,8 1 0.08
## 8 1,2,9,6,8 1 0.08
## 9 1,5 1 0.08
## 10 1,8 6 0.50
## 11 2 159 13.26
## 12 2,4 1 0.08
## 13 2,5 2 0.17
## 14 2,5,8 2 0.17
## 15 2,8 12 1.00
## 16 2,9 1 0.08
## 17 2,9,8 1 0.08
## 18 3 29 2.42
## 19 3,4,8 1 0.08
## 20 3,6,8 1 0.08
## 21 3,8 5 0.42
## 22 4 10 0.83
## 23 4,5 1 0.08
## 24 4,8 1 0.08
## 25 5 46 3.84
## 26 5,8 22 1.83
## 27 6 1 0.08
## 28 6,8 4 0.33
## 29 7 1 0.08
## 30 7,8 2 0.17
## 31 8 861 71.81
## 32 9 18 1.50
## 33 9,8 2 0.17
##
## Table of frequencies for inc_TEXT :
## Var1 Freq Percent
## 1 $100,000 - $149,999 216 18.02
## 2 $150,000 - $199,999 64 5.34
## 3 $25,000 - $49,999 260 21.68
## 4 $50,000 - $74,999 250 20.85
## 5 $75,000 - $99,999 205 17.10
## 6 less than $25,000 164 13.68
## 7 more than $200,000 40 3.34
##
## Table of frequencies for edu_TEXT :
## Var1 Freq Percent
## 1 1 0.08
## 2 Bachelor's degree 487 40.62
## 3 Graduate degree (Masters, PhD, etc) 196 16.35
## 4 High school diploma or GED 142 11.84
## 5 Some college, Technical degree, or Associates degree 362 30.19
## 6 Some schooling, but no high school diploma or degree 11 0.92
##
## Table of frequencies for pol_TEXT :
## Var1 Freq Percent
## 1 Conservative 145 12.09
## 2 Liberal 273 22.77
## 3 Moderate 217 18.10
## 4 Somewhat Conservative 101 8.42
## 5 Somewhat Liberal 155 12.93
## 6 Very Conservative 81 6.76
## 7 Very Liberal 227 18.93
##
## Table of frequencies for pid_TEXT :
## Var1 Freq Percent
## 1 Democrat 623 51.96
## 2 Independent / Other 299 24.94
## 3 Republican 277 23.10
##
## Table of frequencies for area :
## Var1 Freq Percent
## 1 1 352 29.36
## 2 2 638 53.21
## 3 3 209 17.434 Correlations
DVs <- gjg[c("policyDV", "donation", "usGovHelp", "manufacturerHelp", "driverHelp", "youHelp", "usGovPrevent", "manufacturerPrevent", "driverPrevent", "youPrevent", "upset", "sympathetic", "touched", "donatingMoral", "takingPrecautionsMoral", "manufacturersMoral", "manufacturerBlame", "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, data = gjg)
summary(mod_donation)##
## Call:
## lm(formula = donation ~ condition, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.237 -3.005 -1.005 1.832 6.995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.16790 0.15217 20.818 <2e-16 ***
## conditionindividualScope 0.06887 0.21628 0.318 0.750
## conditionpopulationScope -0.16286 0.21628 -0.753 0.452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.062 on 1196 degrees of freedom
## Multiple R-squared: 0.001002, Adjusted R-squared: -0.0006684
## F-statistic: 0.5999 on 2 and 1196 DF, p-value: 0.549mod_policy<- lm(policyDV ~ condition + pol, data = gjg)
summary(mod_policy)##
## Call:
## lm(formula = policyDV ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.792 -1.663 0.341 1.599 2.663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.340775 0.177270 24.487 <2e-16 ***
## conditionindividualScope -0.002155 0.142500 -0.015 0.988
## conditionpopulationScope -0.068707 0.142654 -0.482 0.630
## pol 0.064492 0.031049 2.077 0.038 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.018 on 1195 degrees of freedom
## Multiple R-squared: 0.003949, Adjusted R-squared: 0.001448
## F-statistic: 1.579 on 3 and 1195 DF, p-value: 0.1927mod_donationI <- lm(donation ~ condition, data = gjg_I)
summary(mod_donationI)##
## Call:
## lm(formula = donation ~ condition, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.237 -3.005 -1.005 1.832 6.995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23678 0.15370 21.060 <2e-16 ***
## conditionmergedScope -0.06887 0.21628 -0.318 0.750
## conditionpopulationScope -0.23174 0.21736 -1.066 0.287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.062 on 1196 degrees of freedom
## Multiple R-squared: 0.001002, Adjusted R-squared: -0.0006684
## F-statistic: 0.5999 on 2 and 1196 DF, p-value: 0.549mod_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
## -3.792 -1.663 0.341 1.599 2.663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.338620 0.176856 24.532 <2e-16 ***
## conditionmergedScope 0.002155 0.142500 0.015 0.988
## conditionpopulationScope -0.066552 0.143311 -0.464 0.642
## pol 0.064492 0.031049 2.077 0.038 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.018 on 1195 degrees of freedom
## Multiple R-squared: 0.003949, Adjusted R-squared: 0.001448
## F-statistic: 1.579 on 3 and 1195 DF, p-value: 0.19278 Responsibility (Help and Prevent)
gjg_long<-gjg %>% gather(stim, resp, "usGovHelp":"punishingIndividualsEfficacy")
gjg_long$condition <- factor(gjg_long$condition, levels = c("individualScope", "populationScope", "mergedScope"))
responsibilityHelpLong <- gjg_long %>%
filter(grepl("Help", stim))
facet_cooker <- function(data, iv, dv, coln, rown) {
part1 <- ggplot(data, aes(x = {{iv}}, y = {{dv}}, fill = {{iv}})) +
geom_violin(alpha = 0.3, scale = "count") +
stat_summary(fun = "mean", geom = "point", size = 3, color = "black") +
stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2,
#change to make a data set from allEffects with mean, low CI, high CI
size = 1.5, color = "black") +
theme_classic() +
xlab("") +
ylab("") +
facet_wrap(~ stim, ncol = coln, nrow = rown, scales = "free", as.table = TRUE)
ggpar(part1, legend = "none")
}
facet_cooker(responsibilityHelpLong, condition, resp, coln = 2, rown = 2)
responsibilityPreventLong <- gjg_long %>%
filter(grepl("Prevent", stim))
facet_cooker(responsibilityPreventLong, condition, resp, coln = 2, rown = 2)
8.1 Inferential Stats
merged vs individual marginal us gov help
merged vs pop individual you help
prevent significant
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
## -2.7855 -1.0986 0.2145 1.2145 2.4171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.41115 0.11603 20.780 <2e-16 ***
## conditionindividualScope 0.17227 0.09327 1.847 0.065 .
## conditionpopulationScope 0.02414 0.09337 0.259 0.796
## pol 0.17173 0.02032 8.450 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 1195 degrees of freedom
## Multiple R-squared: 0.05975, Adjusted R-squared: 0.05739
## F-statistic: 25.31 on 3 and 1195 DF, p-value: 7.038e-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
## -2.7855 -1.0986 0.2145 1.2145 2.4171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.58342 0.11576 22.317 <2e-16 ***
## conditionmergedScope -0.17227 0.09327 -1.847 0.065 .
## conditionpopulationScope -0.14813 0.09380 -1.579 0.115
## pol 0.17173 0.02032 8.450 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.321 on 1195 degrees of freedom
## Multiple R-squared: 0.05975, Adjusted R-squared: 0.05739
## F-statistic: 25.31 on 3 and 1195 DF, p-value: 7.038e-16mod_manufacturerHelp <- lm(manufacturerHelp ~ condition + pol, data = gjg)
summary(mod_manufacturerHelp)##
## Call:
## lm(formula = manufacturerHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31830 -1.16922 -0.04774 1.16761 2.45937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.44035 0.12336 19.782 < 2e-16 ***
## conditionindividualScope 0.02761 0.09916 0.278 0.781
## conditionpopulationScope -0.02119 0.09927 -0.213 0.831
## pol 0.12148 0.02161 5.622 2.34e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 1195 degrees of freedom
## Multiple R-squared: 0.02618, Adjusted R-squared: 0.02374
## F-statistic: 10.71 on 3 and 1195 DF, p-value: 5.992e-07mod_manufacturerHelpI <- lm(manufacturerHelp ~ condition + pol, data = gjg_I)
summary(mod_manufacturerHelpI)##
## Call:
## lm(formula = manufacturerHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.31830 -1.16922 -0.04774 1.16761 2.45937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.46796 0.12307 20.053 < 2e-16 ***
## conditionmergedScope -0.02761 0.09916 -0.278 0.781
## conditionpopulationScope -0.04880 0.09973 -0.489 0.625
## pol 0.12148 0.02161 5.622 2.34e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 1195 degrees of freedom
## Multiple R-squared: 0.02618, Adjusted R-squared: 0.02374
## F-statistic: 10.71 on 3 and 1195 DF, p-value: 5.992e-07mod_driverHelp <- lm(driverHelp ~ condition + pol, data = gjg)
summary(mod_driverHelp)##
## Call:
## lm(formula = driverHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2532 -0.2771 0.7468 0.8284 0.9872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.97762 0.10267 38.743 <2e-16 ***
## conditionindividualScope 0.02943 0.08253 0.357 0.7214
## conditionpopulationScope 0.05335 0.08262 0.646 0.5185
## pol 0.03517 0.01798 1.956 0.0507 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.169 on 1195 degrees of freedom
## Multiple R-squared: 0.00345, Adjusted R-squared: 0.0009484
## F-statistic: 1.379 on 3 and 1195 DF, p-value: 0.2476mod_driverHelpI <- lm(driverHelp ~ condition + pol, data = gjg_I)
summary(mod_driverHelpI)##
## Call:
## lm(formula = driverHelp ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2532 -0.2771 0.7468 0.8284 0.9872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.00705 0.10243 39.121 <2e-16 ***
## conditionmergedScope -0.02943 0.08253 -0.357 0.7214
## conditionpopulationScope 0.02392 0.08300 0.288 0.7732
## pol 0.03517 0.01798 1.956 0.0507 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.169 on 1195 degrees of freedom
## Multiple R-squared: 0.00345, Adjusted R-squared: 0.0009484
## F-statistic: 1.379 on 3 and 1195 DF, p-value: 0.2476mod_youHelp <- lm(youHelp ~ condition + pol, data = gjg)
summary(mod_youHelp)##
## Call:
## lm(formula = youHelp ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.06696 -1.01701 -0.03366 1.18496 2.25681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.72654 0.12481 21.846 <2e-16 ***
## conditionindividualScope 0.02190 0.10033 0.218 0.827
## conditionpopulationScope 0.22387 0.10043 2.229 0.026 *
## pol 0.01665 0.02186 0.762 0.446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 1195 degrees of freedom
## Multiple R-squared: 0.005339, Adjusted R-squared: 0.002842
## F-statistic: 2.138 on 3 and 1195 DF, p-value: 0.09371mod_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
## -2.06696 -1.01701 -0.03366 1.18496 2.25681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.74844 0.12451 22.073 <2e-16 ***
## conditionmergedScope -0.02190 0.10033 -0.218 0.8272
## conditionpopulationScope 0.20197 0.10090 2.002 0.0455 *
## pol 0.01665 0.02186 0.762 0.4464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 1195 degrees of freedom
## Multiple R-squared: 0.005339, Adjusted R-squared: 0.002842
## F-statistic: 2.138 on 3 and 1195 DF, p-value: 0.09371mod_usGovPrevent <- lm(usGovPrevent ~ condition + pol, data = gjg)
summary(mod_usGovPrevent)##
## Call:
## lm(formula = usGovPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.11922 -0.93666 0.08875 0.97161 1.97161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.877223 0.106051 27.130 <2e-16 ***
## conditionindividualScope 0.005985 0.085250 0.070 0.944
## conditionpopulationScope -0.025408 0.085343 -0.298 0.766
## pol 0.176573 0.018575 9.506 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.207 on 1195 degrees of freedom
## Multiple R-squared: 0.07083, Adjusted R-squared: 0.0685
## F-statistic: 30.37 on 3 and 1195 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
## -3.11922 -0.93666 0.08875 0.97161 1.97161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.883208 0.105804 27.251 <2e-16 ***
## conditionmergedScope -0.005985 0.085250 -0.070 0.944
## conditionpopulationScope -0.031394 0.085736 -0.366 0.714
## pol 0.176573 0.018575 9.506 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.207 on 1195 degrees of freedom
## Multiple R-squared: 0.07083, Adjusted R-squared: 0.0685
## F-statistic: 30.37 on 3 and 1195 DF, p-value: < 2.2e-16mod_manufacturerPrevent <- lm(manufacturerPrevent ~ condition + pol, data = gjg)
summary(mod_manufacturerPrevent)##
## Call:
## lm(formula = manufacturerPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8020 -1.1588 0.2835 1.2835 1.9870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.98126 0.11978 24.889 < 2e-16 ***
## conditionindividualScope -0.08548 0.09629 -0.888 0.375
## conditionpopulationScope -0.05696 0.09639 -0.591 0.555
## pol 0.11725 0.02098 5.589 2.83e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 1195 degrees of freedom
## Multiple R-squared: 0.02633, Adjusted R-squared: 0.02388
## F-statistic: 10.77 on 3 and 1195 DF, p-value: 5.507e-07mod_manufacturerPreventI <- lm(manufacturerPrevent ~ condition + pol, data = gjg_I)
summary(mod_manufacturerPreventI)##
## Call:
## lm(formula = manufacturerPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8020 -1.1588 0.2835 1.2835 1.9870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.89578 0.11950 24.232 < 2e-16 ***
## conditionmergedScope 0.08548 0.09629 0.888 0.375
## conditionpopulationScope 0.02853 0.09684 0.295 0.768
## pol 0.11725 0.02098 5.589 2.83e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 1195 degrees of freedom
## Multiple R-squared: 0.02633, Adjusted R-squared: 0.02388
## F-statistic: 10.77 on 3 and 1195 DF, p-value: 5.507e-07mod_driverPrevent <- lm(driverPrevent ~ condition + pol, data = gjg)
summary(mod_driverPrevent)##
## Call:
## lm(formula = driverPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5536 -0.4669 0.4155 0.4774 0.6013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.36775 0.07751 56.348 <2e-16 ***
## conditionindividualScope 0.03170 0.06231 0.509 0.6111
## conditionpopulationScope 0.03724 0.06238 0.597 0.5507
## pol 0.03097 0.01358 2.281 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8822 on 1195 degrees of freedom
## Multiple R-squared: 0.004602, Adjusted R-squared: 0.002103
## F-statistic: 1.842 on 3 and 1195 DF, p-value: 0.1378mod_driverPreventI <- lm(driverPrevent ~ condition + pol, data = gjg_I)
summary(mod_driverPreventI)##
## Call:
## lm(formula = driverPrevent ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5536 -0.4669 0.4155 0.4774 0.6013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.399442 0.077333 56.890 <2e-16 ***
## conditionmergedScope -0.031696 0.062310 -0.509 0.6111
## conditionpopulationScope 0.005539 0.062665 0.088 0.9296
## pol 0.030972 0.013576 2.281 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8822 on 1195 degrees of freedom
## Multiple R-squared: 0.004602, Adjusted R-squared: 0.002103
## F-statistic: 1.842 on 3 and 1195 DF, p-value: 0.1378mod_youPrevent <- lm(youPrevent ~ condition + pol, data = gjg)
summary(mod_youPrevent)##
## Call:
## lm(formula = youPrevent ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8447 -1.4308 0.3631 1.3115 1.6208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32769 0.12408 26.819 <2e-16 ***
## conditionindividualScope 0.06386 0.09974 0.640 0.5221
## conditionpopulationScope 0.15620 0.09985 1.564 0.1180
## pol 0.05154 0.02173 2.372 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 1195 degrees of freedom
## Multiple R-squared: 0.006444, Adjusted R-squared: 0.00395
## F-statistic: 2.584 on 3 and 1195 DF, p-value: 0.05196mod_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
## -2.8447 -1.4308 0.3631 1.3115 1.6208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.39155 0.12379 27.398 <2e-16 ***
## conditionmergedScope -0.06386 0.09974 -0.640 0.5221
## conditionpopulationScope 0.09234 0.10031 0.921 0.3575
## pol 0.05154 0.02173 2.372 0.0179 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 1195 degrees of freedom
## Multiple R-squared: 0.006444, Adjusted R-squared: 0.00395
## F-statistic: 2.584 on 3 and 1195 DF, p-value: 0.051969 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 vs pop all 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
## -2.45322 -1.02890 -0.00675 0.97110 2.05970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.298175 0.110756 29.779 < 2e-16 ***
## conditionindividualScope -0.008667 0.089032 -0.097 0.922
## conditionpopulationScope -0.380020 0.089129 -4.264 2.17e-05 ***
## pol 0.022150 0.019399 1.142 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 1195 degrees of freedom
## Multiple R-squared: 0.02086, Adjusted R-squared: 0.0184
## F-statistic: 8.487 on 3 and 1195 DF, p-value: 1.399e-05mod_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
## -2.45322 -1.02890 -0.00675 0.97110 2.05970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.289508 0.110498 29.770 < 2e-16 ***
## conditionmergedScope 0.008667 0.089032 0.097 0.922
## conditionpopulationScope -0.371354 0.089539 -4.147 3.6e-05 ***
## pol 0.022150 0.019399 1.142 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 1195 degrees of freedom
## Multiple R-squared: 0.02086, Adjusted R-squared: 0.0184
## F-statistic: 8.487 on 3 and 1195 DF, p-value: 1.399e-05mod_sympathetic <- lm(sympathetic ~ condition + pol, data = gjg)
summary(mod_sympathetic)##
## Call:
## lm(formula = sympathetic ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0682 -0.8270 0.0799 0.9403 1.7746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.733922 0.099241 37.625 < 2e-16 ***
## conditionindividualScope 0.008409 0.079776 0.105 0.91607
## conditionpopulationScope -0.555055 0.079862 -6.950 5.99e-12 ***
## pol 0.046545 0.017382 2.678 0.00751 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.13 on 1195 degrees of freedom
## Multiple R-squared: 0.05864, Adjusted R-squared: 0.05627
## F-statistic: 24.81 on 3 and 1195 DF, p-value: 1.414e-15mod_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
## -3.0682 -0.8270 0.0799 0.9403 1.7746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.742331 0.099010 37.798 < 2e-16 ***
## conditionmergedScope -0.008409 0.079776 -0.105 0.91607
## conditionpopulationScope -0.563464 0.080230 -7.023 3.63e-12 ***
## pol 0.046545 0.017382 2.678 0.00751 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.13 on 1195 degrees of freedom
## Multiple R-squared: 0.05864, Adjusted R-squared: 0.05627
## F-statistic: 24.81 on 3 and 1195 DF, p-value: 1.414e-15mod_touched <- lm(touched ~ condition + pol, data = gjg)
summary(mod_touched)##
## Call:
## lm(formula = touched ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.30222 -1.22125 0.06044 1.05376 2.12726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.20869 0.11632 27.584 < 2e-16 ***
## conditionindividualScope -0.01416 0.09351 -0.151 0.879644
## conditionpopulationScope -0.34930 0.09361 -3.732 0.000199 ***
## pol 0.01336 0.02037 0.656 0.512038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.324 on 1195 degrees of freedom
## Multiple R-squared: 0.01523, Adjusted R-squared: 0.01276
## F-statistic: 6.16 on 3 and 1195 DF, p-value: 0.0003729mod_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
## -2.30222 -1.22125 0.06044 1.05376 2.12726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.19452 0.11605 27.527 < 2e-16 ***
## conditionmergedScope 0.01416 0.09351 0.151 0.87964
## conditionpopulationScope -0.33514 0.09404 -3.564 0.00038 ***
## pol 0.01336 0.02037 0.656 0.51204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.324 on 1195 degrees of freedom
## Multiple R-squared: 0.01523, Adjusted R-squared: 0.01276
## F-statistic: 6.16 on 3 and 1195 DF, p-value: 0.000372910 Efficacy
efficacyHelpLong <- gjg_long %>%
filter(grepl("Efficacy", stim))
facet_cooker(efficacyHelpLong, condition, resp, coln = 2, rown = 2)
10.1 Inferential Stats
merged vs pop donation efficacy
individual vs pop policy efficacy (ind higher)
individusl vs pop punishing individuals
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
## -2.9895 -0.7259 0.1556 1.0106 1.7560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.48888 0.09530 36.610 < 2e-16 ***
## conditionindividualScope 0.08577 0.07661 1.120 0.2631
## conditionpopulationScope -0.30413 0.07669 -3.966 7.75e-05 ***
## pol 0.05926 0.01669 3.550 0.0004 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.085 on 1195 degrees of freedom
## Multiple R-squared: 0.03456, Adjusted R-squared: 0.03213
## F-statistic: 14.26 on 3 and 1195 DF, p-value: 3.924e-09mod_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
## -2.9895 -0.7259 0.1556 1.0106 1.7560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57465 0.09508 37.597 < 2e-16 ***
## conditionmergedScope -0.08577 0.07661 -1.120 0.2631
## conditionpopulationScope -0.38991 0.07704 -5.061 4.83e-07 ***
## pol 0.05926 0.01669 3.550 0.0004 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.085 on 1195 degrees of freedom
## Multiple R-squared: 0.03456, Adjusted R-squared: 0.03213
## F-statistic: 14.26 on 3 and 1195 DF, p-value: 3.924e-09mod_studyPolicyEfficacy <- lm(studyPolicyEfficacy ~ condition + pol, data = gjg)
summary(mod_studyPolicyEfficacy)##
## Call:
## lm(formula = studyPolicyEfficacy ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5922 -0.5456 -0.2074 0.7460 1.8429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.16075 0.10255 30.823 < 2e-16 ***
## conditionindividualScope 0.10518 0.08243 1.276 0.20221
## conditionpopulationScope -0.05021 0.08252 -0.608 0.54300
## pol 0.04662 0.01796 2.595 0.00956 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.167 on 1195 degrees of freedom
## Multiple R-squared: 0.008903, Adjusted R-squared: 0.006414
## F-statistic: 3.578 on 3 and 1195 DF, p-value: 0.01352mod_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
## -2.5922 -0.5456 -0.2074 0.7460 1.8429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.26593 0.10231 31.923 < 2e-16 ***
## conditionmergedScope -0.10518 0.08243 -1.276 0.20221
## conditionpopulationScope -0.15539 0.08290 -1.874 0.06112 .
## pol 0.04662 0.01796 2.595 0.00956 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.167 on 1195 degrees of freedom
## Multiple R-squared: 0.008903, Adjusted R-squared: 0.006414
## F-statistic: 3.578 on 3 and 1195 DF, p-value: 0.01352mod_donationEfficacy <- lm(donationEfficacy ~ condition + pol, data = gjg)
summary(mod_donationEfficacy)##
## Call:
## lm(formula = donationEfficacy ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3118 -1.1526 -0.2172 0.8474 2.9184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.24718 0.11681 19.237 <2e-16 ***
## conditionindividualScope 0.02197 0.09390 0.234 0.815
## conditionpopulationScope 0.08827 0.09400 0.939 0.348
## pol -0.02365 0.02046 -1.156 0.248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 1195 degrees of freedom
## Multiple R-squared: 0.002011, Adjusted R-squared: -0.0004941
## F-statistic: 0.8028 on 3 and 1195 DF, p-value: 0.4923mod_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
## -1.3118 -1.1526 -0.2172 0.8474 2.9184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26916 0.11654 19.471 <2e-16 ***
## conditionmergedScope -0.02197 0.09390 -0.234 0.815
## conditionpopulationScope 0.06630 0.09444 0.702 0.483
## pol -0.02365 0.02046 -1.156 0.248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.33 on 1195 degrees of freedom
## Multiple R-squared: 0.002011, Adjusted R-squared: -0.0004941
## F-statistic: 0.8028 on 3 and 1195 DF, p-value: 0.4923mod_punishingIndividualsEfficacy <- lm(punishingIndividualsEfficacy ~ condition + pol, data = gjg)
summary(mod_punishingIndividualsEfficacy)##
## Call:
## lm(formula = punishingIndividualsEfficacy ~ condition + pol,
## data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9437 -0.7803 0.1759 1.0862 1.3991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.66663 0.09350 39.213 <2e-16 ***
## conditionindividualScope 0.06780 0.07516 0.902 0.3672
## conditionpopulationScope -0.09562 0.07525 -1.271 0.2041
## pol 0.02990 0.01638 1.826 0.0682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.064 on 1195 degrees of freedom
## Multiple R-squared: 0.006979, Adjusted R-squared: 0.004486
## F-statistic: 2.8 on 3 and 1195 DF, p-value: 0.0389mod_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
## -2.9437 -0.7803 0.1759 1.0862 1.3991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.73444 0.09329 40.032 <2e-16 ***
## conditionmergedScope -0.06780 0.07516 -0.902 0.3672
## conditionpopulationScope -0.16342 0.07559 -2.162 0.0308 *
## pol 0.02990 0.01638 1.826 0.0682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.064 on 1195 degrees of freedom
## Multiple R-squared: 0.006979, Adjusted R-squared: 0.004486
## F-statistic: 2.8 on 3 and 1195 DF, p-value: 0.038911 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
- Nothing
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
## -2.11258 -0.96637 0.03745 1.07979 2.31439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.63946 0.11523 22.906 <2e-16 ***
## conditionindividualScope 0.15003 0.09263 1.620 0.1056
## conditionpopulationScope 0.04997 0.09273 0.539 0.5901
## pol 0.04616 0.02018 2.287 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.312 on 1195 degrees of freedom
## Multiple R-squared: 0.006621, Adjusted R-squared: 0.004127
## F-statistic: 2.655 on 3 and 1195 DF, p-value: 0.04724mod_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
## -2.11258 -0.96637 0.03745 1.07979 2.31439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.78949 0.11496 24.264 <2e-16 ***
## conditionmergedScope -0.15003 0.09263 -1.620 0.1056
## conditionpopulationScope -0.10006 0.09316 -1.074 0.2830
## pol 0.04616 0.02018 2.287 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.312 on 1195 degrees of freedom
## Multiple R-squared: 0.006621, Adjusted R-squared: 0.004127
## F-statistic: 2.655 on 3 and 1195 DF, p-value: 0.04724mod_takingPrecautionsMoral <- lm(takingPrecautionsMoral ~ condition + pol, data = gjg)
summary(mod_takingPrecautionsMoral)##
## Call:
## lm(formula = takingPrecautionsMoral ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.15604 -0.88322 0.05017 0.92945 1.15680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.83775 0.09578 40.069 < 2e-16 ***
## conditionindividualScope -0.04002 0.07699 -0.520 0.60330
## conditionpopulationScope -0.02433 0.07708 -0.316 0.75229
## pol 0.04547 0.01678 2.710 0.00681 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 1195 degrees of freedom
## Multiple R-squared: 0.006388, Adjusted R-squared: 0.003893
## F-statistic: 2.561 on 3 and 1195 DF, p-value: 0.05357mod_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
## -3.15604 -0.88322 0.05017 0.92945 1.15680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.79773 0.09555 39.744 < 2e-16 ***
## conditionmergedScope 0.04002 0.07699 0.520 0.60330
## conditionpopulationScope 0.01569 0.07743 0.203 0.83947
## pol 0.04547 0.01678 2.710 0.00681 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 1195 degrees of freedom
## Multiple R-squared: 0.006388, Adjusted R-squared: 0.003893
## F-statistic: 2.561 on 3 and 1195 DF, p-value: 0.05357mod_manufacturersMoral <- lm(manufacturersMoral ~ condition + pol, data = gjg)
summary(mod_manufacturersMoral)##
## Call:
## lm(formula = manufacturersMoral ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.34289 -1.07900 -0.03829 1.03244 2.33704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.56143 0.11726 21.844 < 2e-16 ***
## conditionindividualScope 0.01983 0.09426 0.210 0.833
## conditionpopulationScope 0.07073 0.09436 0.750 0.454
## pol 0.10153 0.02054 4.944 8.77e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.335 on 1195 degrees of freedom
## Multiple R-squared: 0.02026, Adjusted R-squared: 0.0178
## F-statistic: 8.238 on 3 and 1195 DF, p-value: 1.99e-05mod_manufacturersMoralI <- lm(manufacturersMoral ~ condition + pol, data = gjg_I)
summary(mod_manufacturersMoralI)##
## Call:
## lm(formula = manufacturersMoral ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.34289 -1.07900 -0.03829 1.03244 2.33704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.58126 0.11699 22.064 < 2e-16 ***
## conditionmergedScope -0.01983 0.09426 -0.210 0.833
## conditionpopulationScope 0.05090 0.09480 0.537 0.591
## pol 0.10153 0.02054 4.944 8.77e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.335 on 1195 degrees of freedom
## Multiple R-squared: 0.02026, Adjusted R-squared: 0.0178
## F-statistic: 8.238 on 3 and 1195 DF, p-value: 1.99e-05mod_manufacturerBlame <- lm(manufacturerBlame ~ condition + pol, data = gjg)
summary(mod_manufacturerBlame)##
## Call:
## lm(formula = manufacturerBlame ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2830 -1.0143 -0.1621 0.8379 3.2252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.71875 0.10380 16.558 < 2e-16 ***
## conditionindividualScope 0.04702 0.08344 0.563 0.573
## conditionpopulationScope -0.01785 0.08353 -0.214 0.831
## pol 0.07389 0.01818 4.064 5.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.181 on 1195 degrees of freedom
## Multiple R-squared: 0.01434, Adjusted R-squared: 0.01187
## F-statistic: 5.797 on 3 and 1195 DF, p-value: 0.0006214mod_manufacturerBlameI <- lm(manufacturerBlame ~ condition + pol, data = gjg_I)
summary(mod_manufacturerBlameI)##
## Call:
## lm(formula = manufacturerBlame ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2830 -1.0143 -0.1621 0.8379 3.2252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.76577 0.10356 17.051 < 2e-16 ***
## conditionmergedScope -0.04702 0.08344 -0.563 0.573
## conditionpopulationScope -0.06487 0.08392 -0.773 0.440
## pol 0.07389 0.01818 4.064 5.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.181 on 1195 degrees of freedom
## Multiple R-squared: 0.01434, Adjusted R-squared: 0.01187
## F-statistic: 5.797 on 3 and 1195 DF, p-value: 0.0006214mod_individualsBlame <- lm(individualsBlame ~ condition + pol, data = gjg)
summary(mod_individualsBlame)##
## Call:
## lm(formula = individualsBlame ~ condition + pol, data = gjg)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7753 0.2140 0.2245 0.2427 0.2701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.730077 0.046744 101.190 <2e-16 ***
## conditionindividualScope -0.009265 0.037576 -0.247 0.805
## conditionpopulationScope -0.007640 0.037617 -0.203 0.839
## pol 0.009082 0.008187 1.109 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.532 on 1195 degrees of freedom
## Multiple R-squared: 0.001102, Adjusted R-squared: -0.001406
## F-statistic: 0.4394 on 3 and 1195 DF, p-value: 0.7249mod_individualsBlameI <- lm(individualsBlame ~ condition + pol, data = gjg_I)
summary(mod_manufacturerBlameI)##
## Call:
## lm(formula = manufacturerBlame ~ condition + pol, data = gjg_I)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2830 -1.0143 -0.1621 0.8379 3.2252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.76577 0.10356 17.051 < 2e-16 ***
## conditionmergedScope -0.04702 0.08344 -0.563 0.573
## conditionpopulationScope -0.06487 0.08392 -0.773 0.440
## pol 0.07389 0.01818 4.064 5.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.181 on 1195 degrees of freedom
## Multiple R-squared: 0.01434, Adjusted R-squared: 0.01187
## F-statistic: 5.797 on 3 and 1195 DF, p-value: 0.0006214