Feelings of Being Devalued and Democratically Corrosive Sentiment in the US, UK, France and Poland (Paper RMarkdown)
Julien Labarre, Bruce Bimber, Inmaculada GuttiƩrrez, Karolina Koc-Michalska
Version of September 21, 2024
Packages and Data Preparation
Below are the packages necessary for the analysis.
Specify Models
Populism Models
PopUS_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=US_data)
PopPL_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=PL_data)
PopFR_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=FR_data)
PopGB_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=GB_data)PopUS_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=US_data)
PopPL_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=PL_data)
PopFR_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=FR_data)
PopGB_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=GB_data)Conspiracy Models
ConspUS_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=US_data)
ConspPL_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=PL_data)
ConspFR_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=FR_data)
ConspGB_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=GB_data)ConspUS_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=US_data)
ConspPL_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=PL_data)
ConspFR_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=FR_data)
ConspGB_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=GB_data)Correlation Matrices
## corrplot 0.92 loadedAll Countries
all_data_numeric_only = subset(all_data, select = c(devalued_std, populism_rw_std, populism_main_std, conspiratorial_orientation_std, big5_openness_std, big5_conscientiousness_std, big5_extraversion_std, big5_agreeableness_std, big5_stability_std, rwa_std, sdo_std, ethnic_majority, income_num_std, gender, age_std, ideology_std))
correlation_matrix_allcountries= cor(all_data_numeric_only, method="pearson", use="pairwise.complete.obs")
corrplot(correlation_matrix_allcountries, method="color", order="alphabet", tl.col = 'steelblue4', tl.pos="lt", tl.cex=0.7, tl.srt=45, number.cex=0.7, addgrid.col = 'white', addCoef.col = 'black')
US only
US_data_numeric_only = subset(US_data, select = c(devalued_std, populism_rw_std, populism_main_std, conspiratorial_orientation_std, big5_openness_std, big5_conscientiousness_std, big5_extraversion_std, big5_agreeableness_std, big5_stability_std, rwa_std, sdo_std, ethnic_majority, income_num_std, gender, age_std, ideology_std))
correlation_matrix_US= cor(US_data_numeric_only, method="pearson", use="pairwise.complete.obs")
corrplot(correlation_matrix_US, method="color", order="alphabet", tl.col = 'steelblue4', tl.pos="lt", tl.cex=0.7, tl.srt=45, number.cex=0.7, addgrid.col = 'white', addCoef.col = 'black')
Poland only
PL_data_numeric_only = subset(PL_data, select = c(devalued_std, populism_rw_std, populism_main_std, conspiratorial_orientation_std, big5_openness_std, big5_conscientiousness_std, big5_extraversion_std, big5_agreeableness_std, big5_stability_std, rwa_std, sdo_std, ethnic_majority, income_num_std, gender, age_std, ideology_std))
correlation_matrix_PL= cor(PL_data_numeric_only, method="pearson", use="pairwise.complete.obs")
corrplot(correlation_matrix_PL, method="color", order="alphabet", tl.col = 'steelblue4', tl.pos="lt", tl.cex=0.7, tl.srt=45, number.cex=0.7, addgrid.col = 'white', addCoef.col = 'black')
France only
FR_data_numeric_only = subset(FR_data, select = c(devalued_std, populism_rw_std, populism_main_std, conspiratorial_orientation_std, big5_openness_std, big5_conscientiousness_std, big5_extraversion_std, big5_agreeableness_std, big5_stability_std, rwa_std, sdo_std, ethnic_majority, income_num_std, gender, age_std, ideology_std))
correlation_matrix_FR= cor(FR_data_numeric_only, method="pearson", use="pairwise.complete.obs")
corrplot(correlation_matrix_FR, method="color", order="alphabet", tl.col = 'steelblue4', tl.pos="lt", tl.cex=0.7, tl.srt=45, number.cex=0.7, addgrid.col = 'white', addCoef.col = 'black')
United Kingdom only
GB_data_numeric_only = subset(GB_data, select = c(devalued_std, populism_rw_std, populism_main_std, conspiratorial_orientation_std, big5_openness_std, big5_conscientiousness_std, big5_extraversion_std, big5_agreeableness_std, big5_stability_std, rwa_std, sdo_std, ethnic_majority, income_num_std, gender, age_std, ideology_std))
correlation_matrix_GB= cor(GB_data_numeric_only, method="pearson", use="pairwise.complete.obs")
corrplot(correlation_matrix_GB, method="color", order="alphabet", tl.col = 'steelblue4', tl.pos="lt", tl.cex=0.7, tl.srt=45, number.cex=0.7, addgrid.col = 'white', addCoef.col = 'black')
Paper plots
All respondents
All_Pop_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Pop_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Cons_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Cons_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+country.GB
+country.PL
+country.US,
data=all_data_dum)
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(All_Pop_full, conf.int = TRUE) %>% mutate(Model = "Full Model")
df3 <- tidy(All_Pop_Main, conf.int = TRUE) %>% mutate(Model = "Main Effects")
# Extracting 99% confidence intervals
ci99_df1 <- confint(All_Pop_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Full Model")
ci99_df3 <- confint(All_Pop_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Main Effects")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df3)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df3)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Model"))
# Filter out the intercept, country variables, and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)" &
term != "country.US" &
term != "country.PL" &
term != "country.GB")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "devalued_std" ~ "Feelings of Being Devalued",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
term == "country.PL" ~ "Country: Poland",
term == "country.US" ~ "Country: United States",
term == "country.GB" ~ "Country: United Kingdom",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation","Feelings of Being Devalued")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Now redefine the Model factor to specify the order of the models
coefficients_df <- coefficients_df %>%
mutate(Model = factor(Model, levels = c("Full Model", "Main Effects")))
# Creating the plot with multi-level confidence intervals
plotall1 <- ggplot(coefficients_df, aes(x = estimate, y = Model, color = Model)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("Full Model" = "black", "Main Effects" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Populist Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(All_Cons_full, conf.int = TRUE) %>% mutate(Model = "Full Model")
df3 <- tidy(All_Cons_Main, conf.int = TRUE) %>% mutate(Model = "Main Effects")
# Extracting 99% confidence intervals
ci99_df1 <- confint(All_Cons_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Full Model")
ci99_df3 <- confint(All_Cons_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Main Effects")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df3)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df3)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Model"))
# Filter out the intercept, country variables, and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)" &
term != "country.US" &
term != "country.PL" &
term != "country.GB")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "devalued_std" ~ "Feelings of Being Devalued",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
term == "country.PL" ~ "Country: Poland",
term == "country.US" ~ "Country: United States",
term == "country.GB" ~ "Country: United Kingdom",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation", "Feelings of Being Devalued")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Now redefine the Model factor to specify the order of the models
coefficients_df <- coefficients_df %>%
mutate(Model = factor(Model, levels = c("Full Model", "Main Effects")))
# Creating the plot with multi-level confidence intervals
plotall2 <- ggplot(coefficients_df, aes(x = estimate, y = Model, color = Model)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray50", size = 0.25) + # Wider CI in gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "black", size = 0.25) + # Narrower CI in black
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("Full Model" = "black", "Main Effects" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Conspiratorial Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
# Create the first line of the bottom annotation
bottom_annotation_line1 <- textGrob(
bquote(
"\nAdjusted R"^2 ~ " is 0.064 for " ~ bold("Populism Main Effects") ~ " vs. 0.225 for " ~ bold("Populism Full Model,") ~
" and 0.123 for " ~ bold("Conspiracy Main Effects") ~ " vs. 0.245 for " ~ bold("Conspiracy Full Model.")
),
gp = gpar(fontsize = 12),
just = "center" # Centering the text in the grob
)
# Create the second line of the bottom annotation
bottom_annotation_line2 <- textGrob(
"Reference category is France. None of the country variables are significant.",
gp = gpar(fontsize = 12),
just = "center" # Centering the text in the grob
)
# Combine both lines into one grob with adjusted padding
bottom_annotation <- arrangeGrob(
bottom_annotation_line1,
bottom_annotation_line2,
ncol = 1, # Stack vertically
padding = unit(4) # Adjust the spacing between lines (smaller value reduces space)
)
# Create the combined plot object with the annotations
combined_plot <- arrangeGrob(
plotall1, plotall2,
padding = unit(2.3, "lines"),
ncol = 2, # Specify the number of columns for the plots
top = textGrob(
"Figure 2: Effect of Including FBD by Independent Variable\n",
gp = gpar(fontface = "bold", fontsize = 20),
vjust = 1
),
bottom = bottom_annotation
)
# Printing the combined plot with annotations
grid.draw(combined_plot)
Country specific
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(ConspUS_full, conf.int = TRUE) %>% mutate(Country = "United States")
df2 <- tidy(ConspFR_full, conf.int = TRUE) %>% mutate(Country = "France")
df3 <- tidy(ConspPL_full, conf.int = TRUE) %>% mutate(Country = "Poland")
df4 <- tidy(ConspGB_full, conf.int = TRUE) %>% mutate(Country = "United Kingdom")
# Extracting 99% confidence intervals
ci99_df1 <- confint(ConspUS_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United States")
ci99_df2 <- confint(ConspFR_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "France")
ci99_df3 <- confint(ConspPL_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "Poland")
ci99_df4 <- confint(ConspGB_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United Kingdom")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df2)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df4)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df2, ci99_df3, ci99_df4)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df2, df3, df4)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Country"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "devalued_std" ~ "Feelings of Being Devalued",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation", "Feelings of Being Devalued")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Creating the plot with multi-level confidence intervals
plot1 <- ggplot(coefficients_df, aes(x = estimate, y = Country, color = Country)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("United States" = "black", "France" = "black", "Poland" = "black", "United Kingdom" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Conspiratorial Orientation") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(PopUS_full, conf.int = TRUE) %>% mutate(Country = "United States")
df2 <- tidy(PopFR_full, conf.int = TRUE) %>% mutate(Country = "France")
df3 <- tidy(PopPL_full, conf.int = TRUE) %>% mutate(Country = "Poland")
df4 <- tidy(PopGB_full, conf.int = TRUE) %>% mutate(Country = "United Kingdom")
# Extracting 99% confidence intervals
ci99_df1 <- confint(PopUS_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United States")
ci99_df2 <- confint(PopFR_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "France")
ci99_df3 <- confint(PopPL_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "Poland")
ci99_df4 <- confint(PopGB_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United Kingdom")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df2)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df4)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df2, ci99_df3, ci99_df4)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df2, df3, df4)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Country"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "devalued_std" ~ "Feelings of Being Devalued",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation", "Feelings of Being Devalued")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Creating the plot with multi-level confidence intervals
plot2 <- ggplot(coefficients_df, aes(x = estimate, y = Country, color = Country)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("United States" = "black", "France" = "black", "Poland" = "black", "United Kingdom" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Populist Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
if (!require(grid)) install.packages("grid")
library(grid)
# Creating the combined plot object with arrangeGrob
combined_plot <- arrangeGrob(
plot2, plot1,
ncol = 2,
top = textGrob("Figure 3: By-Country Models\n",
gp = gpar(fontface = "bold", fontsize = 20), vjust = 1)
)
# Printing the combined plot with annotations
grid.draw(combined_plot)
Table 1: Descriptives Populist Attitudes & Conspiratorial Orientation
##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rows## The following object is masked from 'package:huxtable':
##
## add_footnote# Manually create the data frame
descriptives_manual <- data.frame(
Country = c("France", "UK", "Poland", "US", "F-Statistic", "Combined"),
`Populist Attitudes mean (sd)` = c("4.46 (1.03)", "4.57 (0.85)", "4.83 (0.95)", "4.73 (1.01)", "F=57.6, p<.001", "4.65 (0.99)"),
`Conspiracy Orientation mean (sd)` = c("3.96 (1.52)", "4.03 (1.40)", "4.51 (1.39)", "4.27 (1.58)", "F=57.0 (p<.001)", "4.19 (1.49)"),
`Populism : Conspiracy Correlation Pearson R (p)` = c(".58 (<.0001)", ".56 (<.0001)", ".52 (<.0001)", ".60 (<.0001)", "", ".58 (<.0001)")
)
# Formatting the table using kable
kable(descriptives_manual, align = 'c', booktabs = TRUE, col.names = c("Country", "Populist Attitudes mean (sd)", "Conspiracy Orientation mean (sd)", "Populism : Conspiracy correlation Pearson R (š)")) %>%
add_header_above(c("Table 1: Descriptives for Populist Attitudes and Conspiratorial Orientation" = 4), bold = TRUE) %>%
kable_styling(full_width = FALSE, position = "center", font_size = 13) %>%
column_spec(2, width = "9em") %>% # Adjust the width of the second column
column_spec(3, width = "11em") %>% # Adjust the width of the third column
column_spec(4, width = "12em") %>% # Adjust the width of the fourth column
column_spec(1, width = "11em", bold = TRUE) %>%
row_spec(0, extra_css = "border-bottom: 2px solid black;") %>% # Black line after the header
row_spec(5, extra_css = "border-bottom: 2px solid black;") %>% # Black line after the F-Statistic row
footnote(general = "Populist Attitudes and Conspiracy Orientation values have a range of 1-7.\nFrance, N=2004; UK N=2000; Poland N=2001; US N=2000.",
general_title = "Notes:",
footnote_as_chunk = TRUE, escape = FALSE)| Country | Populist Attitudes mean (sd) | Conspiracy Orientation mean (sd) | Populism : Conspiracy correlation Pearson R (š) |
|---|---|---|---|
| France | 4.46 (1.03) | 3.96 (1.52) | .58 (<.0001) |
| UK | 4.57 (0.85) | 4.03 (1.40) | .56 (<.0001) |
| Poland | 4.83 (0.95) | 4.51 (1.39) | .52 (<.0001) |
| US | 4.73 (1.01) | 4.27 (1.58) | .60 (<.0001) |
| F-Statistic | F=57.6, p<.001 | F=57.0 (p<.001) | |
| Combined | 4.65 (0.99) | 4.19 (1.49) | .58 (<.0001) |
| Notes: Populist Attitudes and Conspiracy Orientation values have a range of 1-7. France, N=2004; UK N=2000; Poland N=2001; US N=2000. |
Regression Tables
Table 2: Predicting Populist Attitudes and Conspiratorial Orientation (All Respondents)
# Format Regression Table
stargazer(All_Pop_Main, All_Pop_full, All_Cons_Main, All_Cons_full, report = "vc*", type = "html", star.char = c(".", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"), notes.append = FALSE, title = "Table 2: Predicting Populist Attitudes and Conspiratorial Orientation (All Respondents)", dep.var.labels = c("Populist Attitudes", "Conspiratorial Attitudes"), covariate.labels = c("Feelings of Being Devalued", "Ethnocentrism", "Right-Wing Authoritarianism", "Social Dominance Orientation", "Religiosity", "Ideology", "Ethnic Majority", "Age", "Education", "Political Interest", "Income", "Marital Status", "Country: United Kingdom", "Country: Poland", "Country: United States", "Constant"))| Dependent variable: | ||||
| Populist Attitudes | Conspiratorial Attitudes | |||
| (1) | (2) | (3) | (4) | |
| Feelings of Being Devalued | 0.418*** | 0.362*** | ||
| Ethnocentrism | 0.151*** | 0.091*** | 0.145*** | 0.094*** |
| Right-Wing Authoritarianism | -0.063*** | -0.039** | -0.010 | 0.011 |
| Social Dominance Orientation | -0.062*** | -0.067*** | 0.048*** | 0.044*** |
| Religiosity | -0.099*** | -0.072*** | -0.091*** | -0.067*** |
| Ideology | 0.107*** | 0.101*** | 0.126*** | 0.120*** |
| Ethnic Majority | -0.077* | -0.090** | -0.120*** | -0.131*** |
| Age | -0.055*** | 0.019. | -0.138*** | -0.074*** |
| Education | -0.056*** | -0.039*** | -0.074*** | -0.059*** |
| Political Interest | 0.100*** | 0.094*** | -0.016 | -0.021* |
| Income | -0.028* | 0.008 | -0.040*** | -0.010 |
| Marital Status | 0.112*** | 0.095*** | 0.111*** | 0.096*** |
| Country: United Kingdom | 0.019 | 0.014 | 0.021 | 0.016 |
| Country: Poland | -0.019 | -0.012 | -0.025 | -0.019 |
| Country: United States | 0.018 | 0.011 | 0.018 | 0.011 |
| Constant | 0.162** | 0.136** | 0.255*** | 0.233*** |
| Observations | 8,005 | 8,005 | 8,005 | 8,005 |
| R2 | 0.065 | 0.227 | 0.124 | 0.246 |
| Adjusted R2 | 0.064 | 0.225 | 0.123 | 0.245 |
| Residual Std. Error | 0.968 (df = 7990) | 0.880 (df = 7989) | 0.936 (df = 7990) | 0.869 (df = 7989) |
| F Statistic | 39.809*** (df = 14; 7990) | 156.307*** (df = 15; 7989) | 81.114*** (df = 14; 7990) | 173.694*** (df = 15; 7989) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 | |||
# Format Regression Table
stargazer(PopFR_full, PopUS_full, PopGB_full, PopPL_full, report = "vc*", type = "html", star.char = c(".", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"), dep.var.labels = c("Populist Attitudes"), column.labels = c("France", "United States", "United Kingdom", "Poland"), notes.append = FALSE, title = "Populist Attitudes By Country (Full Models)", covariate.labels = c("Feelings of Being Devalued", "Ethnocentrism", "Right-Wing Authoritarianism", "Social Dominance Orientation", "Religiosity", "Ideology", "Ethnic Majority", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Populist Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Feelings of Being Devalued | 0.419*** | 0.412*** | 0.452*** | 0.356*** |
| Ethnocentrism | 0.179*** | 0.019 | 0.130*** | 0.084*** |
| Right-Wing Authoritarianism | -0.061** | 0.053* | 0.010 | -0.136*** |
| Social Dominance Orientation | -0.071** | -0.020 | -0.077** | -0.119*** |
| Religiosity | -0.089*** | -0.051* | -0.076*** | -0.048* |
| Ideology | 0.043* | 0.248*** | 0.052* | 0.032 |
| Ethnic Majority | -0.083 | 0.050 | -0.229*** | -0.243* |
| Age | -0.052* | 0.022 | -0.032 | 0.097*** |
| Education | -0.075*** | -0.051** | -0.020 | -0.043* |
| Political Interest | 0.043* | 0.135*** | 0.102*** | 0.062** |
| Income | 0.001 | 0.039. | -0.005 | 0.011 |
| Marital Status | 0.048 | 0.068. | 0.084* | 0.134** |
| Constant | 0.266** | 0.088 | 0.216* | 0.255* |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.262 | 0.321 | 0.283 | 0.171 |
| Adjusted R2 | 0.258 | 0.317 | 0.279 | 0.166 |
| Residual Std. Error | 0.862 (df = 1991) | 0.826 (df = 1987) | 0.849 (df = 1987) | 0.913 (df = 1988) |
| F Statistic | 58.982*** (df = 12; 1991) | 78.438*** (df = 12; 1987) | 65.347*** (df = 12; 1987) | 34.136*** (df = 12; 1988) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 | |||
# Format Regression Table
stargazer(ConspFR_full, ConspUS_full, ConspGB_full, ConspPL_full, report = "vc*", type = "html", star.char = c(".", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"), dep.var.labels = c("Conspiratorial Attitudes"), column.labels = c("France", "United States", "United Kingdom", "Poland"), notes.append = FALSE, title = "Cosnpiratorial Attitudes By Country (Full Models)", covariate.labels = c("Feelings of Being Devalued", "Ethnocentrism", "Right-Wing Authoritarianism", "Social Dominance Orientation", "Religiosity", "Ideology", "Ethnic Majority", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Conspiratorial Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Feelings of Being Devalued | 0.334*** | 0.363*** | 0.391*** | 0.323*** |
| Ethnocentrism | 0.145*** | 0.020 | 0.083** | 0.121*** |
| Right-Wing Authoritarianism | -0.026 | 0.071*** | 0.003 | 0.019 |
| Social Dominance Orientation | 0.024 | 0.106*** | 0.081** | -0.015 |
| Religiosity | -0.070*** | -0.029 | -0.079*** | -0.052* |
| Ideology | 0.118*** | 0.271*** | 0.034 | 0.035 |
| Ethnic Majority | -0.184* | -0.028 | -0.245*** | -0.188. |
| Age | -0.105*** | -0.127*** | -0.083*** | 0.010 |
| Education | -0.084*** | -0.076*** | -0.066*** | -0.025 |
| Political Interest | -0.068*** | 0.012 | 0.002 | -0.056* |
| Income | -0.052* | 0.014 | 0.010 | -0.004 |
| Marital Status | 0.063 | 0.093* | 0.129** | 0.073 |
| Constant | 0.377*** | 0.217** | 0.352*** | 0.196. |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.261 | 0.391 | 0.265 | 0.156 |
| Adjusted R2 | 0.256 | 0.387 | 0.260 | 0.151 |
| Residual Std. Error | 0.862 (df = 1991) | 0.783 (df = 1987) | 0.860 (df = 1987) | 0.922 (df = 1988) |
| F Statistic | 58.498*** (df = 12; 1991) | 106.370*** (df = 12; 1987) | 59.597*** (df = 12; 1987) | 30.543*** (df = 12; 1988) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 | |||
# Format Regression Table
stargazer(PopFR_Main, PopUS_Main, PopGB_Main, PopPL_Main, report = "vc*", type = "html", star.char = c(".", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"), dep.var.labels = c("Populist Attitudes"), column.labels = c("France", "United States", "United Kingdom", "Poland"), notes.append = FALSE, title = "Populist Attitudes By Country (Main Effects Models)", covariate.labels = c("Ethnocentrism", "Right-Wing Authoritarianism", "Social Dominance Orientation", "Religiosity", "Ideology", "Ethnic Majority", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Populist Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Ethnocentrism | 0.248*** | 0.057* | 0.205*** | 0.128*** |
| Right-Wing Authoritarianism | -0.100*** | 0.028 | -0.001 | -0.136*** |
| Social Dominance Orientation | -0.063** | 0.001 | -0.105*** | -0.108*** |
| Religiosity | -0.125*** | -0.069** | -0.111*** | -0.055* |
| Ideology | 0.058* | 0.285*** | 0.041. | 0.022 |
| Ethnic Majority | -0.097 | 0.049 | -0.172* | -0.210. |
| Age | -0.092*** | -0.087*** | -0.150*** | 0.060** |
| Education | -0.106*** | -0.053* | -0.027 | -0.049* |
| Political Interest | 0.019 | 0.159*** | 0.117*** | 0.063** |
| Income | -0.042 | 0.014 | -0.066** | -0.014 |
| Marital Status | 0.081 | 0.070 | 0.116* | 0.142** |
| Constant | 0.352*** | 0.093 | 0.170. | 0.234. |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.100 | 0.172 | 0.101 | 0.049 |
| Adjusted R2 | 0.095 | 0.168 | 0.096 | 0.044 |
| Residual Std. Error | 0.951 (df = 1992) | 0.912 (df = 1988) | 0.951 (df = 1988) | 0.978 (df = 1989) |
| F Statistic | 20.156*** (df = 11; 1992) | 37.633*** (df = 11; 1988) | 20.260*** (df = 11; 1988) | 9.315*** (df = 11; 1989) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 | |||
# Format Regression Table
stargazer(ConspFR_Main, ConspUS_Main, ConspGB_Main, ConspPL_Main, report = "vc*", type = "html", star.char = c(".", "*", "**", "***"), star.cutoffs = c(0.1, 0.05, 0.01, 0.001), notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"), dep.var.labels = c("Conspiratorial Attitudes"), column.labels = c("France", "United States", "United Kingdom", "Poland"), notes.append = FALSE, title = "Cosnpiratorial Attitudes By Country (Main Effects Models)", covariate.labels = c("Ethnocentrism", "Right-Wing Authoritarianism", "Social Dominance Orientation", "Religiosity", "Ideology", "Ethnic Majority", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Conspiratorial Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Ethnocentrism | 0.199*** | 0.053* | 0.149*** | 0.161*** |
| Right-Wing Authoritarianism | -0.057* | 0.049* | -0.007 | 0.019 |
| Social Dominance Orientation | 0.030 | 0.125*** | 0.058* | -0.006 |
| Religiosity | -0.098*** | -0.044* | -0.109*** | -0.059* |
| Ideology | 0.130*** | 0.304*** | 0.024 | 0.027 |
| Ethnic Majority | -0.195* | -0.029 | -0.196** | -0.158 |
| Age | -0.137*** | -0.223*** | -0.185*** | -0.023 |
| Education | -0.109*** | -0.078*** | -0.072*** | -0.030 |
| Political Interest | -0.086*** | 0.033 | 0.015 | -0.055* |
| Income | -0.086*** | -0.008 | -0.043. | -0.026 |
| Marital Status | 0.089. | 0.094* | 0.156*** | 0.080. |
| Constant | 0.445*** | 0.222** | 0.312*** | 0.176 |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.158 | 0.276 | 0.128 | 0.055 |
| Adjusted R2 | 0.153 | 0.272 | 0.123 | 0.050 |
| Residual Std. Error | 0.920 (df = 1992) | 0.853 (df = 1988) | 0.936 (df = 1988) | 0.975 (df = 1989) |
| F Statistic | 33.932*** (df = 11; 1992) | 68.798*** (df = 11; 1988) | 26.561*** (df = 11; 1988) | 10.614*** (df = 11; 1989) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 | |||
Big Five and FBD
All_Pop_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Pop_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Cons_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std
+country.GB
+country.PL
+country.US,
data=all_data_dum)
All_Cons_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std
+country.GB
+country.PL
+country.US,
data=all_data_dum)
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(All_Pop_full, conf.int = TRUE) %>% mutate(Model = "With FBD")
df3 <- tidy(All_Pop_Main, conf.int = TRUE) %>% mutate(Model = "Without FBD")
# Extracting 99% confidence intervals
ci99_df1 <- confint(All_Pop_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "With FBD")
ci99_df3 <- confint(All_Pop_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Without FBD")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df3)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df3)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Model"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)" & term != "rwa_std" & term != "sdo_std" & term != "ethnocentrism_std" & term != "age_std" & term != "ideology_std" & term != "ethnic_majority" & term != "income_num_std" & term != "country.PL" & term != "country.US" & term != "country.GB" & term != "education_collapse" & term != "church_attendance_std" & term != "Q1_interest_in_politics_std" & term != "marital_status" & term != "devalued_std")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "big5_openness_std" ~ "Openness to Experience",
term == "big5_agreeableness_std" ~ "Agreeableness",
term == "big5_stability_std" ~ "Emotional Stability",
term == "big5_conscientiousness_std" ~ "Conscientiousness",
term == "big5_extraversion_std" ~ "Extraversion",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Openness to Experience", "Conscientiousness", "Extraversion",
"Agreeableness", "Emotional Stability")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Now redefine the Model factor to specify the order of the models
coefficients_df <- coefficients_df %>%
mutate(Model = factor(Model, levels = c("With FBD", "Without FBD")))
# Creating the plot with multi-level confidence intervals
plotall1 <- ggplot(coefficients_df, aes(x = estimate, y = Model, color = Model)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("With FBD" = "black", "Without FBD" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Populist Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(All_Cons_full, conf.int = TRUE) %>% mutate(Model = "With FBD")
df3 <- tidy(All_Cons_Main, conf.int = TRUE) %>% mutate(Model = "Without FBD")
# Extracting 99% confidence intervals
ci99_df1 <- confint(All_Cons_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "With FBD")
ci99_df3 <- confint(All_Cons_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Model = "Without FBD")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df3)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df3)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Model"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)" & term != "rwa_std" & term != "sdo_std" & term != "ethnocentrism_std" & term != "age_std" & term != "ideology_std" & term != "ethnic_majority" & term != "income_num_std" & term != "country.PL" & term != "country.US" & term != "country.GB" & term != "education_collapse" & term != "church_attendance_std" & term != "Q1_interest_in_politics_std" & term != "marital_status" & term != "devalued_std")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "big5_openness_std" ~ "Openness to Experience",
term == "big5_agreeableness_std" ~ "Agreeableness",
term == "big5_stability_std" ~ "Emotional Stability",
term == "big5_conscientiousness_std" ~ "Conscientiousness",
term == "big5_extraversion_std" ~ "Extraversion",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Openness to Experience", "Conscientiousness", "Extraversion",
"Agreeableness", "Emotional Stability")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Now redefine the Model factor to specify the order of the models
coefficients_df <- coefficients_df %>%
mutate(Model = factor(Model, levels = c("With FBD", "Without FBD")))
# Creating the plot with multi-level confidence intervals
plotall2 <- ggplot(coefficients_df, aes(x = estimate, y = Model, color = Model)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray50", size = 0.25) + # Wider CI in gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "black", size = 0.25) + # Narrower CI in black
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("With FBD" = "black", "Without FBD" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Conspiratorial Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
# Creating the combined plot object with arrangeGrob
combined_plot <- arrangeGrob(
plotall1, plotall2,
padding = unit(4, "lines"),
ncol = 2, # Specify the number of columns for the plots
top = textGrob(
"FBD and the Relationship of DVs With Big Five (All Respondents)\n",
gp = gpar(fontface = "bold", fontsize = 20),
vjust = 1
),
bottom = textGrob(bquote(~ bold("Not displayed:") ~ "FBD, age, income, marital status, education, political interest, religiosity, ethnocentrism, ideology, ethnic majority, RWA, SDO, and country."),
gp = gpar(fontsize = 10),
just = "center" # Centering the text in the grob
)
)
# Printing the combined plot with annotations
grid.draw(combined_plot)
RWP vote
Models
RWPUS_full = lm(vote_trump~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=US_data)
RWPPL_full = lm(vote_prawo~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=PL_data)
RWPFR_full = lm(vote_lepen~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status,
data=FR_data)# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(RWPUS_full, conf.int = TRUE) %>% mutate(Country = "United States")
df2 <- tidy(RWPFR_full, conf.int = TRUE) %>% mutate(Country = "France")
df3 <- tidy(RWPPL_full, conf.int = TRUE) %>% mutate(Country = "Poland")
# Extracting 99% confidence intervals
ci99_df1 <- confint(RWPUS_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United States")
ci99_df2 <- confint(RWPFR_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "France")
ci99_df3 <- confint(RWPPL_full, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "Poland")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df2)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df2, ci99_df3)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df2, df3)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Country"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "devalued_std" ~ "Feelings of Being Devalued",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation", "Feelings of Being Devalued")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Creating the plot with multi-level confidence intervals
plot1 <- ggplot(coefficients_df, aes(x = estimate, y = Country, color = Country)) +
facet_wrap(~ term, scales = "free_y", ncol = 2) + # Set ncol = 2 for two columns
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("United States" = "black", "France" = "black", "Poland" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Conspiratorial Orientation") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 11),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
plot1 + labs(title="Figure 4: Predicting Right-Wing Populist Vote in France, the US, and Poland") +
theme(plot.title = element_text(family = "sans", face = "bold"))
Big Five Per Country
Big Five and FBD
All_PopBig5PL_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=PL_data)
All_PopBig5PL_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=PL_data)
All_ConsBig5PL_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=PL_data)
All_ConsBig5PL_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=PL_data)
All_PopBig5US_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=US_data)
All_PopBig5US_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=US_data)
All_ConsBig5US_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=US_data)
All_ConsBig5US_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=US_data)
All_PopBig5FR_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=FR_data)
All_PopBig5FR_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=FR_data)
All_ConsBig5FR_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=FR_data)
All_ConsBig5FR_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=FR_data)
All_PopBig5GB_Main = lm(populism_rw_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=GB_data)
All_PopBig5GB_full = lm(populism_rw_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=GB_data)
All_ConsBig5GB_Main = lm(conspiratorial_orientation_std~ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=GB_data)
All_ConsBig5GB_full = lm(conspiratorial_orientation_std~devalued_std+ethnocentrism_std
+rwa_std
+sdo_std
+church_attendance_std
+ideology_std
+ethnic_majority
+age_std
+education_collapse
+Q1_interest_in_politics_std
+income_num_std
+marital_status
+big5_conscientiousness_std
+big5_agreeableness_std
+big5_extraversion_std
+big5_openness_std
+big5_stability_std,
data=GB_data)
summary(All_PopBig5GB_Main)##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + ethnic_majority +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = GB_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7185 -0.6000 -0.0186 0.5997 2.8984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20551 0.09572 2.147 0.03191 *
## ethnocentrism_std 0.20886 0.02871 7.276 4.94e-13 ***
## rwa_std -0.00366 0.02595 -0.141 0.88787
## sdo_std -0.09169 0.02936 -3.123 0.00181 **
## church_attendance_std -0.11165 0.02379 -4.693 2.87e-06 ***
## ideology_std 0.05125 0.02483 2.064 0.03912 *
## ethnic_majority -0.20441 0.06912 -2.957 0.00314 **
## age_std -0.10612 0.02500 -4.245 2.28e-05 ***
## education_collapse -0.02892 0.01892 -1.529 0.12643
## Q1_interest_in_politics_std 0.11441 0.02317 4.937 8.60e-07 ***
## income_num_std -0.05621 0.02437 -2.307 0.02118 *
## marital_status 0.11634 0.04642 2.506 0.01228 *
## big5_conscientiousness_std 0.02992 0.02544 1.176 0.23972
## big5_agreeableness_std -0.00901 0.02493 -0.361 0.71783
## big5_extraversion_std 0.01972 0.02313 0.853 0.39390
## big5_openness_std 0.07614 0.02392 3.183 0.00148 **
## big5_stability_std -0.12833 0.02511 -5.112 3.50e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9437 on 1983 degrees of freedom
## Multiple R-squared: 0.1166, Adjusted R-squared: 0.1095
## F-statistic: 16.36 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = GB_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0026 -0.5041 -0.0055 0.5291 3.0162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.221016 0.085684 2.579 0.009967 **
## devalued_std 0.463001 0.020859 22.197 < 2e-16 ***
## ethnocentrism_std 0.144059 0.025862 5.570 2.89e-08 ***
## rwa_std 0.002702 0.023236 0.116 0.907447
## sdo_std -0.068766 0.026298 -2.615 0.008995 **
## church_attendance_std -0.077639 0.021350 -3.637 0.000283 ***
## ideology_std 0.053611 0.022226 2.412 0.015950 *
## ethnic_majority -0.220277 0.061878 -3.560 0.000380 ***
## age_std -0.029899 0.022638 -1.321 0.186735
## education_collapse -0.022432 0.016937 -1.324 0.185501
## Q1_interest_in_politics_std 0.090368 0.020772 4.350 1.43e-05 ***
## income_num_std -0.009602 0.021915 -0.438 0.661340
## marital_status 0.077029 0.041590 1.852 0.064160 .
## big5_conscientiousness_std 0.064261 0.022829 2.815 0.004928 **
## big5_agreeableness_std -0.008066 0.022316 -0.361 0.717799
## big5_extraversion_std 0.045030 0.020735 2.172 0.029999 *
## big5_openness_std 0.058160 0.021425 2.715 0.006693 **
## big5_stability_std -0.015552 0.023041 -0.675 0.499764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8447 on 1982 degrees of freedom
## Multiple R-squared: 0.2925, Adjusted R-squared: 0.2864
## F-statistic: 48.19 on 17 and 1982 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = GB_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7661 -0.5944 -0.0439 0.6039 2.8662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.347527 0.094258 3.687 0.000233 ***
## ethnocentrism_std 0.148537 0.028269 5.254 1.64e-07 ***
## rwa_std 0.003883 0.025560 0.152 0.879260
## sdo_std 0.052278 0.028909 1.808 0.070699 .
## church_attendance_std -0.101819 0.023427 -4.346 1.45e-05 ***
## ideology_std 0.032226 0.024450 1.318 0.187644
## ethnic_majority -0.218410 0.068068 -3.209 0.001355 **
## age_std -0.130425 0.024616 -5.298 1.30e-07 ***
## education_collapse -0.076862 0.018629 -4.126 3.85e-05 ***
## Q1_interest_in_politics_std 0.009410 0.022821 0.412 0.680145
## income_num_std -0.034744 0.023999 -1.448 0.147838
## marital_status 0.155226 0.045712 3.396 0.000698 ***
## big5_conscientiousness_std -0.031275 0.025057 -1.248 0.212125
## big5_agreeableness_std -0.045419 0.024550 -1.850 0.064449 .
## big5_extraversion_std 0.024727 0.022777 1.086 0.277765
## big5_openness_std 0.087374 0.023553 3.710 0.000213 ***
## big5_stability_std -0.076453 0.024724 -3.092 0.002014 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9293 on 1983 degrees of freedom
## Multiple R-squared: 0.1433, Adjusted R-squared: 0.1364
## F-statistic: 20.73 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ devalued_std +
## ethnocentrism_std + rwa_std + sdo_std + church_attendance_std +
## ideology_std + ethnic_majority + age_std + education_collapse +
## Q1_interest_in_politics_std + income_num_std + marital_status +
## big5_conscientiousness_std + big5_agreeableness_std + big5_extraversion_std +
## big5_openness_std + big5_stability_std, data = GB_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4060 -0.5176 -0.0071 0.5279 3.1895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.360943 0.086742 4.161 3.30e-05 ***
## devalued_std 0.400489 0.021116 18.966 < 2e-16 ***
## ethnocentrism_std 0.092482 0.026181 3.532 0.000421 ***
## rwa_std 0.009386 0.023523 0.399 0.689916
## sdo_std 0.072106 0.026623 2.708 0.006819 **
## church_attendance_std -0.072402 0.021614 -3.350 0.000824 ***
## ideology_std 0.034266 0.022500 1.523 0.127934
## ethnic_majority -0.232133 0.062642 -3.706 0.000217 ***
## age_std -0.064498 0.022917 -2.814 0.004936 **
## education_collapse -0.071246 0.017146 -4.155 3.39e-05 ***
## Q1_interest_in_politics_std -0.011385 0.021029 -0.541 0.588306
## income_num_std 0.005571 0.022186 0.251 0.801762
## marital_status 0.121220 0.042104 2.879 0.004031 **
## big5_conscientiousness_std -0.001574 0.023111 -0.068 0.945717
## big5_agreeableness_std -0.044603 0.022591 -1.974 0.048482 *
## big5_extraversion_std 0.046618 0.020991 2.221 0.026477 *
## big5_openness_std 0.071822 0.021689 3.311 0.000945 ***
## big5_stability_std 0.021101 0.023326 0.905 0.365768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8552 on 1982 degrees of freedom
## Multiple R-squared: 0.2749, Adjusted R-squared: 0.2687
## F-statistic: 44.2 on 17 and 1982 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + ethnic_majority +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7752 -0.5823 0.0178 0.6391 2.7822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.353614 0.102090 3.464 0.000544 ***
## ethnocentrism_std 0.251132 0.026038 9.645 < 2e-16 ***
## rwa_std -0.099253 0.025020 -3.967 7.54e-05 ***
## sdo_std -0.056574 0.025308 -2.235 0.025504 *
## church_attendance_std -0.124556 0.022321 -5.580 2.73e-08 ***
## ideology_std 0.057845 0.024323 2.378 0.017492 *
## ethnic_majority -0.097902 0.078974 -1.240 0.215246
## age_std -0.090187 0.023218 -3.884 0.000106 ***
## education_collapse -0.106967 0.018528 -5.773 9.00e-09 ***
## Q1_interest_in_politics_std 0.017516 0.022565 0.776 0.437678
## income_num_std -0.040074 0.025847 -1.550 0.121202
## marital_status 0.081702 0.051767 1.578 0.114661
## big5_conscientiousness_std 0.003331 0.024804 0.134 0.893187
## big5_agreeableness_std 0.008731 0.024474 0.357 0.721329
## big5_extraversion_std -0.005338 0.022392 -0.238 0.811588
## big5_openness_std 0.035458 0.023712 1.495 0.134980
## big5_stability_std -0.028841 0.023200 -1.243 0.213951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9516 on 1987 degrees of freedom
## Multiple R-squared: 0.1017, Adjusted R-squared: 0.0945
## F-statistic: 14.06 on 16 and 1987 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0558 -0.5164 0.0165 0.5566 3.9906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.275830 0.092147 2.993 0.00279 **
## devalued_std 0.436133 0.020407 21.372 < 2e-16 ***
## ethnocentrism_std 0.184412 0.023690 7.784 1.12e-14 ***
## rwa_std -0.066658 0.022617 -2.947 0.00324 **
## sdo_std -0.049978 0.022828 -2.189 0.02869 *
## church_attendance_std -0.090850 0.020193 -4.499 7.22e-06 ***
## ideology_std 0.037126 0.021958 1.691 0.09104 .
## ethnic_majority -0.084989 0.071230 -1.193 0.23295
## age_std -0.063533 0.020977 -3.029 0.00249 **
## education_collapse -0.076270 0.016772 -4.548 5.75e-06 ***
## Q1_interest_in_politics_std 0.037222 0.020372 1.827 0.06783 .
## income_num_std -0.004457 0.023371 -0.191 0.84878
## marital_status 0.043468 0.046723 0.930 0.35231
## big5_conscientiousness_std 0.032844 0.022413 1.465 0.14298
## big5_agreeableness_std 0.029041 0.022093 1.314 0.18884
## big5_extraversion_std 0.027360 0.020253 1.351 0.17688
## big5_openness_std 0.035268 0.021386 1.649 0.09928 .
## big5_stability_std 0.033132 0.021124 1.568 0.11693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8582 on 1986 degrees of freedom
## Multiple R-squared: 0.2697, Adjusted R-squared: 0.2634
## F-statistic: 43.14 on 17 and 1986 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.70521 -0.57746 0.02145 0.58131 2.81750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44509 0.09816 4.535 6.12e-06 ***
## ethnocentrism_std 0.20400 0.02503 8.149 6.43e-16 ***
## rwa_std -0.04084 0.02406 -1.698 0.089745 .
## sdo_std 0.02134 0.02433 0.877 0.380663
## church_attendance_std -0.09311 0.02146 -4.338 1.51e-05 ***
## ideology_std 0.13054 0.02339 5.582 2.70e-08 ***
## ethnic_majority -0.18253 0.07593 -2.404 0.016315 *
## age_std -0.12199 0.02232 -5.465 5.22e-08 ***
## education_collapse -0.11314 0.01781 -6.351 2.64e-10 ***
## Q1_interest_in_politics_std -0.09629 0.02169 -4.438 9.57e-06 ***
## income_num_std -0.08425 0.02485 -3.390 0.000713 ***
## marital_status 0.08813 0.04977 1.771 0.076765 .
## big5_conscientiousness_std -0.02626 0.02385 -1.101 0.270926
## big5_agreeableness_std -0.03768 0.02353 -1.601 0.109433
## big5_extraversion_std 0.01916 0.02153 0.890 0.373607
## big5_openness_std 0.08731 0.02280 3.830 0.000132 ***
## big5_stability_std -0.06241 0.02231 -2.798 0.005190 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9149 on 1987 degrees of freedom
## Multiple R-squared: 0.1696, Adjusted R-squared: 0.1629
## F-statistic: 25.37 on 16 and 1987 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ devalued_std +
## ethnocentrism_std + rwa_std + sdo_std + church_attendance_std +
## ideology_std + ethnic_majority + age_std + education_collapse +
## Q1_interest_in_politics_std + income_num_std + marital_status +
## big5_conscientiousness_std + big5_agreeableness_std + big5_extraversion_std +
## big5_openness_std + big5_stability_std, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.89455 -0.54363 0.02098 0.54150 2.88638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.384733 0.092080 4.178 3.06e-05 ***
## devalued_std 0.338427 0.020392 16.596 < 2e-16 ***
## ethnocentrism_std 0.152223 0.023673 6.430 1.59e-10 ***
## rwa_std -0.015544 0.022601 -0.688 0.491676
## sdo_std 0.026455 0.022811 1.160 0.246298
## church_attendance_std -0.066953 0.020179 -3.318 0.000923 ***
## ideology_std 0.114468 0.021942 5.217 2.01e-07 ***
## ethnic_majority -0.172506 0.071177 -2.424 0.015456 *
## age_std -0.101309 0.020962 -4.833 1.45e-06 ***
## education_collapse -0.089324 0.016759 -5.330 1.10e-07 ***
## Q1_interest_in_politics_std -0.080997 0.020357 -3.979 7.17e-05 ***
## income_num_std -0.056608 0.023354 -2.424 0.015444 *
## marital_status 0.058462 0.046688 1.252 0.210655
## big5_conscientiousness_std -0.003361 0.022397 -0.150 0.880723
## big5_agreeableness_std -0.021923 0.022077 -0.993 0.320818
## big5_extraversion_std 0.044533 0.020238 2.200 0.027893 *
## big5_openness_std 0.087161 0.021370 4.079 4.71e-05 ***
## big5_stability_std -0.014324 0.021108 -0.679 0.497469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8576 on 1986 degrees of freedom
## Multiple R-squared: 0.2708, Adjusted R-squared: 0.2645
## F-statistic: 43.38 on 17 and 1986 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + ethnic_majority +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9667 -0.5843 -0.0026 0.5983 3.0863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09432 0.08292 1.138 0.255447
## ethnocentrism_std 0.04996 0.02622 1.905 0.056899 .
## rwa_std 0.03570 0.02486 1.436 0.151199
## sdo_std -0.01187 0.02787 -0.426 0.670072
## church_attendance_std -0.07683 0.02293 -3.351 0.000821 ***
## ideology_std 0.28050 0.02412 11.627 < 2e-16 ***
## ethnic_majority 0.04248 0.05463 0.778 0.436908
## age_std -0.07954 0.02485 -3.201 0.001392 **
## education_collapse -0.05207 0.02133 -2.441 0.014717 *
## Q1_interest_in_politics_std 0.16609 0.02215 7.497 9.78e-14 ***
## income_num_std 0.01647 0.02248 0.733 0.463948
## marital_status 0.07326 0.04428 1.654 0.098228 .
## big5_conscientiousness_std 0.03573 0.02555 1.399 0.162099
## big5_agreeableness_std -0.06176 0.02604 -2.371 0.017815 *
## big5_extraversion_std -0.02813 0.02195 -1.282 0.200044
## big5_openness_std -0.01191 0.02395 -0.497 0.619133
## big5_stability_std -0.02563 0.02479 -1.034 0.301396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9106 on 1983 degrees of freedom
## Multiple R-squared: 0.1774, Adjusted R-squared: 0.1707
## F-statistic: 26.72 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7309 -0.5019 0.0144 0.5405 3.3925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.106205 0.074688 1.422 0.155193
## devalued_std 0.445964 0.020742 21.501 < 2e-16 ***
## ethnocentrism_std 0.024301 0.023650 1.027 0.304315
## rwa_std 0.040885 0.022395 1.826 0.068060 .
## sdo_std -0.014988 0.025100 -0.597 0.550488
## church_attendance_std -0.057787 0.020670 -2.796 0.005229 **
## ideology_std 0.235582 0.021829 10.792 < 2e-16 ***
## ethnic_majority 0.057605 0.049209 1.171 0.241895
## age_std 0.001945 0.022702 0.086 0.931729
## education_collapse -0.058752 0.019214 -3.058 0.002260 **
## Q1_interest_in_politics_std 0.126154 0.020041 6.295 3.78e-10 ***
## income_num_std 0.030706 0.020262 1.515 0.129824
## marital_status 0.069170 0.039888 1.734 0.083055 .
## big5_conscientiousness_std 0.085281 0.023124 3.688 0.000232 ***
## big5_agreeableness_std -0.037762 0.023485 -1.608 0.108016
## big5_extraversion_std 0.007940 0.019837 0.400 0.689021
## big5_openness_std -0.006246 0.021573 -0.290 0.772214
## big5_stability_std 0.074081 0.022808 3.248 0.001182 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8202 on 1982 degrees of freedom
## Multiple R-squared: 0.3329, Adjusted R-squared: 0.3272
## F-statistic: 58.19 on 17 and 1982 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.87334 -0.52552 -0.01336 0.56381 2.66120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21245 0.07726 2.750 0.006017 **
## ethnocentrism_std 0.04263 0.02443 1.745 0.081209 .
## rwa_std 0.06961 0.02317 3.005 0.002689 **
## sdo_std 0.11528 0.02596 4.440 9.48e-06 ***
## church_attendance_std -0.04522 0.02136 -2.117 0.034410 *
## ideology_std 0.30898 0.02248 13.746 < 2e-16 ***
## ethnic_majority -0.03349 0.05090 -0.658 0.510631
## age_std -0.18525 0.02315 -8.001 2.09e-15 ***
## education_collapse -0.07397 0.01987 -3.722 0.000203 ***
## Q1_interest_in_politics_std 0.04034 0.02064 1.954 0.050792 .
## income_num_std 0.00105 0.02095 0.050 0.960028
## marital_status 0.09796 0.04126 2.374 0.017686 *
## big5_conscientiousness_std -0.03100 0.02380 -1.302 0.192940
## big5_agreeableness_std -0.04958 0.02427 -2.043 0.041172 *
## big5_extraversion_std -0.03777 0.02045 -1.847 0.064836 .
## big5_openness_std 0.04881 0.02231 2.187 0.028825 *
## big5_stability_std -0.06261 0.02310 -2.710 0.006780 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8485 on 1983 degrees of freedom
## Multiple R-squared: 0.2858, Adjusted R-squared: 0.2801
## F-statistic: 49.6 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ devalued_std +
## ethnocentrism_std + rwa_std + sdo_std + church_attendance_std +
## ideology_std + ethnic_majority + age_std + education_collapse +
## Q1_interest_in_politics_std + income_num_std + marital_status +
## big5_conscientiousness_std + big5_agreeableness_std + big5_extraversion_std +
## big5_openness_std + big5_stability_std, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.90515 -0.47731 0.01786 0.48859 2.57230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.222360 0.071180 3.124 0.001811 **
## devalued_std 0.372071 0.019768 18.822 < 2e-16 ***
## ethnocentrism_std 0.021218 0.022540 0.941 0.346631
## rwa_std 0.073939 0.021344 3.464 0.000543 ***
## sdo_std 0.112686 0.023921 4.711 2.64e-06 ***
## church_attendance_std -0.029333 0.019699 -1.489 0.136634
## ideology_std 0.271503 0.020804 13.050 < 2e-16 ***
## ethnic_majority -0.020870 0.046898 -0.445 0.656367
## age_std -0.117260 0.021635 -5.420 6.69e-08 ***
## education_collapse -0.079539 0.018312 -4.344 1.47e-05 ***
## Q1_interest_in_politics_std 0.007022 0.019099 0.368 0.713157
## income_num_std 0.012928 0.019311 0.669 0.503280
## marital_status 0.094548 0.038015 2.487 0.012959 *
## big5_conscientiousness_std 0.010344 0.022038 0.469 0.638843
## big5_agreeableness_std -0.029556 0.022382 -1.321 0.186812
## big5_extraversion_std -0.007681 0.018906 -0.406 0.684593
## big5_openness_std 0.053534 0.020559 2.604 0.009287 **
## big5_stability_std 0.020579 0.021737 0.947 0.343897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7817 on 1982 degrees of freedom
## Multiple R-squared: 0.3941, Adjusted R-squared: 0.3889
## F-statistic: 75.84 on 17 and 1982 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + ethnic_majority +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9916 -0.6146 -0.0174 0.6175 2.9306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25865 0.12509 2.068 0.038801 *
## ethnocentrism_std 0.11755 0.02591 4.536 6.07e-06 ***
## rwa_std -0.13592 0.02925 -4.646 3.60e-06 ***
## sdo_std -0.07884 0.02535 -3.110 0.001898 **
## church_attendance_std -0.04114 0.02477 -1.661 0.096903 .
## ideology_std 0.01723 0.02454 0.702 0.482606
## ethnic_majority -0.22605 0.11347 -1.992 0.046499 *
## age_std 0.03359 0.02384 1.409 0.159077
## education_collapse -0.04739 0.02069 -2.291 0.022093 *
## Q1_interest_in_politics_std 0.06141 0.02337 2.628 0.008661 **
## income_num_std -0.01887 0.02354 -0.802 0.422865
## marital_status 0.12169 0.04792 2.540 0.011175 *
## big5_conscientiousness_std 0.09460 0.02690 3.517 0.000446 ***
## big5_agreeableness_std 0.04415 0.02741 1.611 0.107380
## big5_extraversion_std 0.02609 0.02744 0.951 0.341880
## big5_openness_std -0.06570 0.02459 -2.672 0.007611 **
## big5_stability_std -0.02518 0.02507 -1.004 0.315334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9713 on 1984 degrees of freedom
## Multiple R-squared: 0.06421, Adjusted R-squared: 0.05666
## F-statistic: 8.508 on 16 and 1984 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4318 -0.5552 0.0010 0.5560 3.3179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.285671 0.116305 2.456 0.014126 *
## devalued_std 0.372967 0.021098 17.678 < 2e-16 ***
## ethnocentrism_std 0.069893 0.024243 2.883 0.003981 **
## rwa_std -0.128231 0.027200 -4.714 2.59e-06 ***
## sdo_std -0.086642 0.023572 -3.676 0.000244 ***
## church_attendance_std -0.032588 0.023034 -1.415 0.157284
## ideology_std 0.020382 0.022811 0.894 0.371681
## ethnic_majority -0.263381 0.105514 -2.496 0.012635 *
## age_std 0.058742 0.022214 2.644 0.008248 **
## education_collapse -0.041799 0.019239 -2.173 0.029925 *
## Q1_interest_in_politics_std 0.051343 0.021734 2.362 0.018258 *
## income_num_std -0.003556 0.021905 -0.162 0.871069
## marital_status 0.113514 0.044550 2.548 0.010909 *
## big5_conscientiousness_std 0.093093 0.025007 3.723 0.000203 ***
## big5_agreeableness_std 0.040122 0.025482 1.575 0.115524
## big5_extraversion_std 0.038641 0.025525 1.514 0.130224
## big5_openness_std -0.054393 0.022871 -2.378 0.017488 *
## big5_stability_std 0.050237 0.023694 2.120 0.034108 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.903 on 1983 degrees of freedom
## Multiple R-squared: 0.1916, Adjusted R-squared: 0.1847
## F-statistic: 27.65 on 17 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status + big5_conscientiousness_std +
## big5_agreeableness_std + big5_extraversion_std + big5_openness_std +
## big5_stability_std, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2420 -0.5828 -0.0071 0.6665 2.5303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19238 0.12505 1.538 0.12410
## ethnocentrism_std 0.15609 0.02591 6.026 2e-09 ***
## rwa_std 0.01837 0.02924 0.628 0.52999
## sdo_std 0.01337 0.02534 0.528 0.59788
## church_attendance_std -0.04954 0.02476 -2.001 0.04555 *
## ideology_std 0.02683 0.02453 1.094 0.27410
## ethnic_majority -0.16719 0.11343 -1.474 0.14066
## age_std -0.03337 0.02384 -1.400 0.16170
## education_collapse -0.02943 0.02068 -1.423 0.15498
## Q1_interest_in_politics_std -0.05414 0.02336 -2.317 0.02059 *
## income_num_std -0.02496 0.02353 -1.060 0.28906
## marital_status 0.06630 0.04790 1.384 0.16649
## big5_conscientiousness_std 0.07720 0.02689 2.871 0.00413 **
## big5_agreeableness_std 0.01815 0.02740 0.663 0.50766
## big5_extraversion_std 0.02168 0.02743 0.790 0.42949
## big5_openness_std -0.03400 0.02458 -1.383 0.16679
## big5_stability_std -0.06769 0.02506 -2.701 0.00697 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9709 on 1984 degrees of freedom
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.05737
## F-statistic: 8.607 on 16 and 1984 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ devalued_std +
## ethnocentrism_std + rwa_std + sdo_std + church_attendance_std +
## ideology_std + ethnic_majority + age_std + education_collapse +
## Q1_interest_in_politics_std + income_num_std + marital_status +
## big5_conscientiousness_std + big5_agreeableness_std + big5_extraversion_std +
## big5_openness_std + big5_stability_std, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3325 -0.5359 0.0192 0.6033 2.7259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.216179 0.118291 1.828 0.06777 .
## devalued_std 0.328530 0.021458 15.310 < 2e-16 ***
## ethnocentrism_std 0.114110 0.024657 4.628 3.93e-06 ***
## rwa_std 0.025145 0.027664 0.909 0.36350
## sdo_std 0.006496 0.023975 0.271 0.78647
## church_attendance_std -0.042010 0.023427 -1.793 0.07309 .
## ideology_std 0.029608 0.023200 1.276 0.20204
## ethnic_majority -0.200075 0.107317 -1.864 0.06242 .
## age_std -0.011213 0.022593 -0.496 0.61973
## education_collapse -0.024497 0.019567 -1.252 0.21073
## Q1_interest_in_politics_std -0.063005 0.022106 -2.850 0.00442 **
## income_num_std -0.011465 0.022279 -0.515 0.60688
## marital_status 0.059095 0.045311 1.304 0.19231
## big5_conscientiousness_std 0.075869 0.025434 2.983 0.00289 **
## big5_agreeableness_std 0.014606 0.025917 0.564 0.57310
## big5_extraversion_std 0.032734 0.025961 1.261 0.20750
## big5_openness_std -0.024042 0.023261 -1.034 0.30146
## big5_stability_std -0.001256 0.024098 -0.052 0.95843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9184 on 1983 degrees of freedom
## Multiple R-squared: 0.1638, Adjusted R-squared: 0.1566
## F-statistic: 22.84 on 17 and 1983 DF, p-value: < 2.2e-16Country specific without FBD
Country specific
# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(ConspUS_Main, conf.int = TRUE) %>% mutate(Country = "United States")
df2 <- tidy(ConspFR_Main, conf.int = TRUE) %>% mutate(Country = "France")
df3 <- tidy(ConspPL_Main, conf.int = TRUE) %>% mutate(Country = "Poland")
df4 <- tidy(ConspGB_Main, conf.int = TRUE) %>% mutate(Country = "United Kingdom")
# Extracting 99% confidence intervals
ci99_df1 <- confint(ConspUS_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United States")
ci99_df2 <- confint(ConspFR_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "France")
ci99_df3 <- confint(ConspPL_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "Poland")
ci99_df4 <- confint(ConspGB_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United Kingdom")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df2)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df4)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df2, ci99_df3, ci99_df4)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df2, df3, df4)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Country"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Creating the plot with multi-level confidence intervals
plot1 <- ggplot(coefficients_df, aes(x = estimate, y = Country, color = Country)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("United States" = "black", "France" = "black", "Poland" = "black", "United Kingdom" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Conspiratorial Orientation") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)# Extracting coefficients and creating a tidy data frame with confidence intervals
df1 <- tidy(PopUS_Main, conf.int = TRUE) %>% mutate(Country = "United States")
df2 <- tidy(PopFR_Main, conf.int = TRUE) %>% mutate(Country = "France")
df3 <- tidy(PopPL_Main, conf.int = TRUE) %>% mutate(Country = "Poland")
df4 <- tidy(PopGB_Main, conf.int = TRUE) %>% mutate(Country = "United Kingdom")
# Extracting 99% confidence intervals
ci99_df1 <- confint(PopUS_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United States")
ci99_df2 <- confint(PopFR_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "France")
ci99_df3 <- confint(PopPL_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "Poland")
ci99_df4 <- confint(PopGB_Main, level = 0.99) %>% as.data.frame() %>% mutate(term = rownames(.), Country = "United Kingdom")
# Renaming columns to match
colnames(ci99_df1)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df2)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df3)[1:2] <- c("conf.low2", "conf.high2")
colnames(ci99_df4)[1:2] <- c("conf.low2", "conf.high2")
# Combining the 99% CIs into one data frame
ci99_df <- bind_rows(ci99_df1, ci99_df2, ci99_df3, ci99_df4)
# Preparing the combined dataframe
coefficients_df <- bind_rows(df1, df2, df3, df4)
coefficients_df <- coefficients_df %>%
left_join(ci99_df, by = c("term", "Country"))
# Filter out the intercept and other unwanted coefficients
coefficients_df <- coefficients_df %>%
filter(term != "(Intercept)")
# Rename terms for readability
coefficients_df <- coefficients_df %>%
mutate(term = case_when(
term == "rwa_std" ~ "Right-Wing Authoritarianism",
term == "sdo_std" ~ "Social Dominance Orientation",
term == "ethnocentrism_std" ~ "Ethnocentrism",
term == "ideology_std" ~ "Ideology",
term == "ethnic_majority" ~ "Ethnic Majority",
term == "age_std" ~ "Age",
term == "church_attendance_std" ~ "Religiosity",
term == "income_num_std" ~ "Income",
term == "Q1_interest_in_politics_std" ~ "Political Interest",
term == "marital_status" ~ "Marital Status",
term == "education_collapse" ~ "Education",
TRUE ~ term
))
# Define the desired order for facets
facet_order <- c("Age", "Income", "Marital Status", "Education", "Political Interest", "Religiosity", "Ethnocentrism", "Ideology", "Ethnic Majority",
"Right-Wing Authoritarianism", "Social Dominance Orientation")
# Relevel the 'term' factor according to the desired facet order
coefficients_df <- coefficients_df %>%
mutate(term = factor(term, levels = facet_order))
# Creating the plot with multi-level confidence intervals
plot2 <- ggplot(coefficients_df, aes(x = estimate, y = Country, color = Country)) +
facet_wrap(~ term, scales = "free_y", ncol = 1) +
geom_point(size = 2, shape = 16) +
geom_linerange(aes(xmin = conf.low2, xmax = conf.high2), height = 0.2, color = "gray70", size = 0.25) + # Wider CI in lighter gray
geom_linerange(aes(xmin = conf.low, xmax = conf.high), height = 0.2, color = "gray20", size = 0.25) + # Narrower CI in darker gray
geom_vline(xintercept = 0, color = "black", size = .5, linetype = "solid") + # Add vertical line for intercept
scale_color_manual(values = c("United States" = "black", "France" = "black", "Poland" = "black", "United Kingdom" = "black")) +
labs(x = "Point estimates", y = NULL, title = "DV: Populist Attitudes") +
theme_minimal() +
theme(
legend.position = "none",
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size = 12),
plot.title = element_text(hjust = 0.5, size = 14),
strip.text = element_text(size = 12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(size = .5, color = "gray", linetype = "dotted")
)
if (!require(grid)) install.packages("grid")
library(grid)
# Creating the combined plot object with arrangeGrob
combined_plot <- arrangeGrob(
plot2, plot1,
ncol = 2,
top = textGrob("Populist & Conspiratorial Attitudes Per Country (Main Models)\n",
gp = gpar(fontface = "bold", fontsize = 20), vjust = 1)
)
# Printing the combined plot with annotations
grid.draw(combined_plot)
Correlations populist vote x attitudes
PL
# Spearman correlation
correlation_spearmanPL <- cor(PL_data$vote_prawo, PL_data$populism_rw_std, method = "spearman")
# Kendall correlation
correlation_kendallPL <- cor(PL_data$vote_prawo, PL_data$populism_rw_std, method = "kendall")
# Print the results
print(correlation_spearmanPL)## [1] -0.01558146## [1] -0.01294549US
# Spearman correlation
correlation_spearmanUS <- cor(US_data$vote_trump, US_data$populism_rw_std, method = "spearman")
# Kendall correlation
correlation_kendallUS <- cor(US_data$vote_trump, US_data$populism_rw_std, method = "kendall")
# Print the results
print(correlation_spearmanUS)## [1] 0.2728396## [1] 0.2265467FR
# Spearman correlation
correlation_spearmanFR <- cor(FR_data$vote_lepen, FR_data$populism_rw_std, method = "spearman")
# Kendall correlation
correlation_kendallFR <- cor(FR_data$vote_lepen, FR_data$populism_rw_std, method = "kendall")
# Print the results
print(correlation_spearmanFR)## [1] 0.2827311## [1] 0.23457Summary RWP
##
## Call:
## lm(formula = vote_trump ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7790 -0.3099 -0.1174 0.3181 1.1483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.277706 0.036193 7.673 2.62e-14 ***
## devalued_std 0.022552 0.009504 2.373 0.017747 *
## ethnocentrism_std -0.051363 0.011400 -4.506 7.00e-06 ***
## rwa_std 0.053261 0.010678 4.988 6.64e-07 ***
## sdo_std 0.052031 0.011565 4.499 7.22e-06 ***
## church_attendance_std 0.001127 0.009905 0.114 0.909439
## ideology_std 0.160107 0.010521 15.218 < 2e-16 ***
## ethnic_majority 0.160387 0.023793 6.741 2.06e-11 ***
## age_std 0.066876 0.010475 6.384 2.14e-10 ***
## education_collapse -0.032696 0.009297 -3.517 0.000446 ***
## Q1_interest_in_politics_std 0.026992 0.009600 2.812 0.004977 **
## income_num_std 0.013731 0.009803 1.401 0.161467
## marital_status 0.022786 0.019337 1.178 0.238796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.398 on 1987 degrees of freedom
## Multiple R-squared: 0.2673, Adjusted R-squared: 0.2629
## F-statistic: 60.42 on 12 and 1987 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = vote_lepen ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7846 -0.2482 -0.1231 0.1137 1.3444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2376517 0.0402674 5.902 4.21e-09 ***
## devalued_std 0.0453457 0.0087331 5.192 2.29e-07 ***
## ethnocentrism_std 0.0910505 0.0102988 8.841 < 2e-16 ***
## rwa_std -0.0179359 0.0097285 -1.844 0.0654 .
## sdo_std -0.0243618 0.0095637 -2.547 0.0109 *
## church_attendance_std 0.0177253 0.0087792 2.019 0.0436 *
## ideology_std 0.1268561 0.0095545 13.277 < 2e-16 ***
## ethnic_majority 0.0675774 0.0311129 2.172 0.0300 *
## age_std -0.0235738 0.0090030 -2.618 0.0089 **
## education_collapse -0.0305089 0.0072983 -4.180 3.04e-05 ***
## Q1_interest_in_politics_std 0.0051625 0.0088211 0.585 0.5585
## income_num_std -0.0000351 0.0101615 -0.003 0.9972
## marital_status 0.0277436 0.0204235 1.358 0.1745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3755 on 1991 degrees of freedom
## Multiple R-squared: 0.2019, Adjusted R-squared: 0.1971
## F-statistic: 41.98 on 12 and 1991 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = vote_prawo ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## ethnic_majority + age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8447 -0.2393 -0.0957 0.2216 1.1011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2038311 0.0463028 4.402 1.13e-05 ***
## devalued_std -0.0015786 0.0082060 -0.192 0.84747
## ethnocentrism_std 0.0178695 0.0095142 1.878 0.06050 .
## rwa_std 0.0829600 0.0107510 7.716 1.88e-14 ***
## sdo_std -0.0524712 0.0090935 -5.770 9.16e-09 ***
## church_attendance_std -0.0421124 0.0091245 -4.615 4.18e-06 ***
## ideology_std 0.1592224 0.0090532 17.587 < 2e-16 ***
## ethnic_majority 0.0889350 0.0420097 2.117 0.03438 *
## age_std 0.0271748 0.0085868 3.165 0.00158 **
## education_collapse -0.0208804 0.0076540 -2.728 0.00643 **
## Q1_interest_in_politics_std 0.0424393 0.0085536 4.962 7.59e-07 ***
## income_num_std 0.0005368 0.0086615 0.062 0.95058
## marital_status 0.0144730 0.0176161 0.822 0.41142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3599 on 1988 degrees of freedom
## Multiple R-squared: 0.3058, Adjusted R-squared: 0.3016
## F-statistic: 72.98 on 12 and 1988 DF, p-value: < 2.2e-16