FBD as Cross-Country Approach to the Nexus between Conspiracism and Populism (Paper RMarkdown)
Julien Labarre, Bruce Bimber, Inmaculada GuttiƩrrez, Karolina Koc-Michalska
Version of September 23, 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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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 == "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",
"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 == "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",
"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 == "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",
"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 == "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",
"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", "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 | 0.417*** | 0.362*** | ||
| Ethnocentrism | 0.152*** | 0.093*** | 0.148*** | 0.097*** |
| Right-Wing Authoritarianism | -0.062*** | -0.038** | -0.009 | 0.012 |
| Social Dominance Orientation | -0.063*** | -0.067*** | 0.048*** | 0.044*** |
| Religiosity | -0.102*** | -0.075*** | -0.095*** | -0.071*** |
| Ideology | 0.105*** | 0.099*** | 0.123*** | 0.118*** |
| Age | -0.059*** | 0.013 | -0.145*** | -0.082*** |
| Education | -0.055*** | -0.039*** | -0.073*** | -0.059*** |
| Political Interest | 0.100*** | 0.094*** | -0.015 | -0.020. |
| Income | -0.029* | 0.007 | -0.042*** | -0.011 |
| Marital Status | 0.109*** | 0.092*** | 0.107*** | 0.092*** |
| Country: United Kingdom | 0.021 | 0.017 | 0.024 | 0.020 |
| Country: Poland | -0.023 | -0.016 | -0.031 | -0.025 |
| Country: United States | 0.027 | 0.020 | 0.031 | 0.026 |
| Constant | 0.093* | 0.056 | 0.147*** | 0.115** |
| Observations | 8,005 | 8,005 | 8,005 | 8,005 |
| R2 | 0.065 | 0.226 | 0.123 | 0.244 |
| Adjusted R2 | 0.063 | 0.225 | 0.122 | 0.243 |
| Residual Std. Error | 0.968 (df = 7991) | 0.880 (df = 7990) | 0.937 (df = 7991) | 0.870 (df = 7990) |
| F Statistic | 42.499*** (df = 13; 7991) | 166.800*** (df = 14; 7990) | 86.326*** (df = 13; 7991) | 184.579*** (df = 14; 7990) |
| 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", "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*** | 0.449*** | 0.355*** |
| Ethnocentrism | 0.178*** | 0.016 | 0.136*** | 0.084*** |
| Right-Wing Authoritarianism | -0.060** | 0.052* | 0.014 | -0.135*** |
| Social Dominance Orientation | -0.069** | -0.018 | -0.078** | -0.117*** |
| Religiosity | -0.093*** | -0.050* | -0.092*** | -0.048* |
| Ideology | 0.041. | 0.249*** | 0.050* | 0.030 |
| Age | -0.056** | 0.026 | -0.047* | 0.094*** |
| Education | -0.074*** | -0.050** | -0.014 | -0.044* |
| Political Interest | 0.043* | 0.135*** | 0.100*** | 0.064** |
| Income | 0.002 | 0.040* | -0.007 | 0.009 |
| Marital Status | 0.047 | 0.073. | 0.080. | 0.135** |
| Constant | 0.189** | 0.121. | -0.002 | 0.024 |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.262 | 0.321 | 0.278 | 0.169 |
| Adjusted R2 | 0.258 | 0.317 | 0.274 | 0.164 |
| Residual Std. Error | 0.862 (df = 1992) | 0.826 (df = 1988) | 0.852 (df = 1988) | 0.914 (df = 1989) |
| F Statistic | 64.208*** (df = 11; 1992) | 85.474*** (df = 11; 1988) | 69.598*** (df = 11; 1988) | 36.690*** (df = 11; 1989) |
| 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", "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*** | 0.388*** | 0.322*** |
| Ethnocentrism | 0.143*** | 0.022 | 0.090*** | 0.121*** |
| Right-Wing Authoritarianism | -0.022 | 0.071*** | 0.007 | 0.020 |
| Social Dominance Orientation | 0.028 | 0.105*** | 0.081** | -0.014 |
| Religiosity | -0.077*** | -0.029 | -0.095*** | -0.052* |
| Ideology | 0.113*** | 0.271*** | 0.032 | 0.034 |
| Age | -0.114*** | -0.129*** | -0.099*** | 0.008 |
| Education | -0.083*** | -0.077*** | -0.060*** | -0.026 |
| Political Interest | -0.067** | 0.012 | -0.0001 | -0.055* |
| Income | -0.051* | 0.013 | 0.008 | -0.005 |
| Marital Status | 0.059 | 0.090* | 0.125** | 0.074 |
| Constant | 0.208** | 0.199** | 0.118. | 0.017 |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.258 | 0.391 | 0.259 | 0.154 |
| Adjusted R2 | 0.254 | 0.388 | 0.255 | 0.150 |
| Residual Std. Error | 0.864 (df = 1992) | 0.783 (df = 1988) | 0.863 (df = 1988) | 0.922 (df = 1989) |
| F Statistic | 63.040*** (df = 11; 1992) | 116.045*** (df = 11; 1988) | 63.171*** (df = 11; 1988) | 33.009*** (df = 11; 1989) |
| 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", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Populist Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Ethnocentrism | 0.247*** | 0.054* | 0.210*** | 0.128*** |
| Right-Wing Authoritarianism | -0.098*** | 0.027 | 0.002 | -0.135*** |
| Social Dominance Orientation | -0.061* | 0.004 | -0.105*** | -0.107*** |
| Religiosity | -0.129*** | -0.067** | -0.122*** | -0.055* |
| Ideology | 0.055* | 0.286*** | 0.040 | 0.021 |
| Age | -0.097*** | -0.083*** | -0.161*** | 0.058* |
| Education | -0.106*** | -0.052* | -0.022 | -0.050* |
| Political Interest | 0.020 | 0.159*** | 0.116*** | 0.065** |
| Income | -0.041 | 0.015 | -0.067** | -0.015 |
| Marital Status | 0.079 | 0.075. | 0.113* | 0.142** |
| Constant | 0.262*** | 0.126. | 0.006 | 0.034 |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.099 | 0.172 | 0.098 | 0.047 |
| Adjusted R2 | 0.095 | 0.168 | 0.093 | 0.043 |
| Residual Std. Error | 0.951 (df = 1993) | 0.912 (df = 1989) | 0.952 (df = 1989) | 0.978 (df = 1990) |
| F Statistic | 22.013*** (df = 10; 1993) | 41.320*** (df = 10; 1989) | 21.611*** (df = 10; 1989) | 9.896*** (df = 10; 1990) |
| 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", "Age", "Education", "Political Interest", "Income", "Marital Status", "Constant"))| Dependent variable: | ||||
| Conspiratorial Attitudes | ||||
| France | United States | United Kingdom | Poland | |
| (1) | (2) | (3) | (4) | |
| Ethnocentrism | 0.197*** | 0.055* | 0.154*** | 0.161*** |
| Right-Wing Authoritarianism | -0.053* | 0.049* | -0.003 | 0.020 |
| Social Dominance Orientation | 0.034 | 0.124*** | 0.058* | -0.005 |
| Religiosity | -0.106*** | -0.045* | -0.121*** | -0.059* |
| Ideology | 0.124*** | 0.303*** | 0.023 | 0.026 |
| Age | -0.147*** | -0.225*** | -0.198*** | -0.025 |
| Education | -0.108*** | -0.078*** | -0.067*** | -0.031 |
| Political Interest | -0.085*** | 0.033 | 0.014 | -0.054* |
| Income | -0.085*** | -0.009 | -0.044. | -0.027 |
| Marital Status | 0.085. | 0.091* | 0.153*** | 0.081. |
| Constant | 0.266*** | 0.203** | 0.126. | 0.026 |
| Observations | 2,004 | 2,000 | 2,000 | 2,001 |
| R2 | 0.155 | 0.276 | 0.125 | 0.055 |
| Adjusted R2 | 0.151 | 0.272 | 0.120 | 0.050 |
| Residual Std. Error | 0.922 (df = 1993) | 0.853 (df = 1989) | 0.938 (df = 1989) | 0.975 (df = 1990) |
| F Statistic | 36.573*** (df = 10; 1993) | 75.671*** (df = 10; 1989) | 28.289*** (df = 10; 1989) | 11.477*** (df = 10; 1990) |
| 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
+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
+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
+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
+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 != "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 != "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, 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
+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
+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
+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 == "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",
"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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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
+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 + 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
## -3.7419 -0.6044 -0.0207 0.5893 3.0817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.132e-02 6.978e-02 0.162 0.87113
## ethnocentrism_std 2.150e-01 2.869e-02 7.493 1.00e-13 ***
## rwa_std -4.975e-05 2.598e-02 -0.002 0.99847
## sdo_std -9.217e-02 2.941e-02 -3.134 0.00175 **
## church_attendance_std -1.247e-01 2.342e-02 -5.325 1.12e-07 ***
## ideology_std 4.939e-02 2.487e-02 1.986 0.04715 *
## age_std -1.211e-01 2.452e-02 -4.940 8.47e-07 ***
## education_collapse -2.361e-02 1.887e-02 -1.251 0.21106
## Q1_interest_in_politics_std 1.122e-01 2.321e-02 4.835 1.44e-06 ***
## income_num_std -5.880e-02 2.440e-02 -2.410 0.01606 *
## marital_status 1.126e-01 4.649e-02 2.422 0.01554 *
## big5_conscientiousness_std 2.991e-02 2.549e-02 1.173 0.24082
## big5_agreeableness_std -9.373e-03 2.498e-02 -0.375 0.70753
## big5_extraversion_std 1.992e-02 2.317e-02 0.860 0.39014
## big5_openness_std 7.656e-02 2.396e-02 3.195 0.00142 **
## big5_stability_std -1.210e-01 2.503e-02 -4.833 1.45e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9455 on 1984 degrees of freedom
## Multiple R-squared: 0.1127, Adjusted R-squared: 0.106
## F-statistic: 16.8 on 15 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 +
## 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
## -4.0533 -0.4998 -0.0095 0.5242 3.1571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.011759 0.062524 0.188 0.85083
## devalued_std 0.462143 0.020919 22.092 < 2e-16 ***
## ethnocentrism_std 0.150758 0.025869 5.828 6.54e-09 ***
## rwa_std 0.006580 0.023279 0.283 0.77746
## sdo_std -0.069331 0.026375 -2.629 0.00864 **
## church_attendance_std -0.091781 0.021039 -4.362 1.35e-05 ***
## ideology_std 0.051605 0.022284 2.316 0.02067 *
## age_std -0.046225 0.022234 -2.079 0.03774 *
## education_collapse -0.016713 0.016910 -0.988 0.32309
## Q1_interest_in_politics_std 0.088029 0.020823 4.228 2.47e-05 ***
## income_num_std -0.012479 0.021965 -0.568 0.57000
## marital_status 0.073060 0.041697 1.752 0.07990 .
## big5_conscientiousness_std 0.064185 0.022896 2.803 0.00511 **
## big5_agreeableness_std -0.008459 0.022381 -0.378 0.70551
## big5_extraversion_std 0.045195 0.020796 2.173 0.02988 *
## big5_openness_std 0.058645 0.021487 2.729 0.00640 **
## big5_stability_std -0.007829 0.023006 -0.340 0.73366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8472 on 1983 degrees of freedom
## Multiple R-squared: 0.288, Adjusted R-squared: 0.2822
## F-statistic: 50.12 on 16 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 +
## 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
## -2.81504 -0.58685 -0.04091 0.61154 2.87706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.140045 0.068742 2.037 0.041756 *
## ethnocentrism_std 0.155061 0.028262 5.487 4.62e-08 ***
## rwa_std 0.007741 0.025592 0.302 0.762316
## sdo_std 0.051760 0.028976 1.786 0.074201 .
## church_attendance_std -0.115780 0.023073 -5.018 5.69e-07 ***
## ideology_std 0.030241 0.024500 1.234 0.217213
## age_std -0.146475 0.024159 -6.063 1.59e-09 ***
## education_collapse -0.071179 0.018588 -3.829 0.000133 ***
## Q1_interest_in_politics_std 0.007046 0.022862 0.308 0.757954
## income_num_std -0.037512 0.024039 -1.560 0.118809
## marital_status 0.151218 0.045802 3.302 0.000979 ***
## big5_conscientiousness_std -0.031288 0.025116 -1.246 0.213007
## big5_agreeableness_std -0.045807 0.024607 -1.862 0.062814 .
## big5_extraversion_std 0.024937 0.022830 1.092 0.274829
## big5_openness_std 0.087821 0.023608 3.720 0.000205 ***
## big5_stability_std -0.068587 0.024659 -2.781 0.005464 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9315 on 1984 degrees of freedom
## Multiple R-squared: 0.1388, Adjusted R-squared: 0.1323
## F-statistic: 21.33 on 15 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 + 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
## -3.4566 -0.5172 -0.0031 0.5213 3.1660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.140424 0.063313 2.218 0.026672 *
## devalued_std 0.399585 0.021183 18.864 < 2e-16 ***
## ethnocentrism_std 0.099542 0.026196 3.800 0.000149 ***
## rwa_std 0.013473 0.023572 0.572 0.567673
## sdo_std 0.071511 0.026708 2.678 0.007478 **
## church_attendance_std -0.087305 0.021304 -4.098 4.34e-05 ***
## ideology_std 0.032152 0.022565 1.425 0.154349
## age_std -0.081702 0.022514 -3.629 0.000292 ***
## education_collapse -0.065220 0.017123 -3.809 0.000144 ***
## Q1_interest_in_politics_std -0.013849 0.021086 -0.657 0.511387
## income_num_std 0.002539 0.022242 0.114 0.909140
## marital_status 0.117037 0.042224 2.772 0.005626 **
## big5_conscientiousness_std -0.001654 0.023185 -0.071 0.943123
## big5_agreeableness_std -0.045017 0.022664 -1.986 0.047136 *
## big5_extraversion_std 0.046791 0.021059 2.222 0.026399 *
## big5_openness_std 0.072333 0.021759 3.324 0.000902 ***
## big5_stability_std 0.029240 0.023296 1.255 0.209578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8579 on 1983 degrees of freedom
## Multiple R-squared: 0.2699, Adjusted R-squared: 0.264
## F-statistic: 45.81 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + 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
## -3.7898 -0.5833 0.0181 0.6411 2.7966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.263791 0.071929 3.667 0.000251 ***
## ethnocentrism_std 0.250251 0.026031 9.613 < 2e-16 ***
## rwa_std -0.096984 0.024957 -3.886 0.000105 ***
## sdo_std -0.055007 0.025280 -2.176 0.029682 *
## church_attendance_std -0.128366 0.022112 -5.805 7.46e-09 ***
## ideology_std 0.055132 0.024228 2.276 0.022976 *
## age_std -0.094845 0.022915 -4.139 3.63e-05 ***
## education_collapse -0.106423 0.018525 -5.745 1.06e-08 ***
## Q1_interest_in_politics_std 0.017869 0.022566 0.792 0.428537
## income_num_std -0.039872 0.025850 -1.542 0.123133
## marital_status 0.079753 0.051750 1.541 0.123444
## big5_conscientiousness_std 0.002454 0.024797 0.099 0.921192
## big5_agreeableness_std 0.007715 0.024463 0.315 0.752507
## big5_extraversion_std -0.005150 0.022395 -0.230 0.818128
## big5_openness_std 0.036141 0.023709 1.524 0.127567
## big5_stability_std -0.028241 0.023198 -1.217 0.223599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9517 on 1988 degrees of freedom
## Multiple R-squared: 0.101, Adjusted R-squared: 0.09425
## F-statistic: 14.9 on 15 and 1988 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 +
## 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
## -4.0577 -0.5201 0.0177 0.5591 3.9816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.197823 0.064944 3.046 0.00235 **
## devalued_std 0.436340 0.020408 21.380 < 2e-16 ***
## ethnocentrism_std 0.183615 0.023683 7.753 1.42e-14 ***
## rwa_std -0.064672 0.022559 -2.867 0.00419 **
## sdo_std -0.048615 0.022802 -2.132 0.03312 *
## church_attendance_std -0.094141 0.020006 -4.706 2.71e-06 ***
## ideology_std 0.034762 0.021871 1.589 0.11213
## age_std -0.067564 0.020706 -3.263 0.00112 **
## education_collapse -0.075784 0.016769 -4.519 6.57e-06 ***
## Q1_interest_in_politics_std 0.037537 0.020372 1.843 0.06554 .
## income_num_std -0.004264 0.023373 -0.182 0.85525
## marital_status 0.041758 0.046706 0.894 0.37139
## big5_conscientiousness_std 0.032097 0.022407 1.432 0.15218
## big5_agreeableness_std 0.028169 0.022084 1.276 0.20226
## big5_extraversion_std 0.027539 0.020255 1.360 0.17411
## big5_openness_std 0.035861 0.021382 1.677 0.09367 .
## big5_stability_std 0.033683 0.021121 1.595 0.11093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8583 on 1987 degrees of freedom
## Multiple R-squared: 0.2692, Adjusted R-squared: 0.2633
## F-statistic: 45.74 on 16 and 1987 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## 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
## -2.70850 -0.57892 0.01503 0.58540 2.80644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27763 0.06923 4.010 6.29e-05 ***
## ethnocentrism_std 0.20235 0.02505 8.076 1.14e-15 ***
## rwa_std -0.03661 0.02402 -1.524 0.127674
## sdo_std 0.02426 0.02433 0.997 0.318898
## church_attendance_std -0.10021 0.02128 -4.709 2.67e-06 ***
## ideology_std 0.12549 0.02332 5.381 8.26e-08 ***
## age_std -0.13068 0.02206 -5.925 3.67e-09 ***
## education_collapse -0.11213 0.01783 -6.289 3.92e-10 ***
## Q1_interest_in_politics_std -0.09563 0.02172 -4.403 1.12e-05 ***
## income_num_std -0.08387 0.02488 -3.371 0.000764 ***
## marital_status 0.08450 0.04981 1.696 0.089960 .
## big5_conscientiousness_std -0.02790 0.02387 -1.169 0.242583
## big5_agreeableness_std -0.03958 0.02355 -1.681 0.092952 .
## big5_extraversion_std 0.01951 0.02155 0.905 0.365490
## big5_openness_std 0.08858 0.02282 3.882 0.000107 ***
## big5_stability_std -0.06129 0.02233 -2.745 0.006101 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.916 on 1988 degrees of freedom
## Multiple R-squared: 0.1672, Adjusted R-squared: 0.1609
## F-statistic: 26.61 on 15 and 1988 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 + 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
## -2.89842 -0.54491 0.01375 0.54688 2.86804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.226399 0.064969 3.485 0.000503 ***
## devalued_std 0.338846 0.020416 16.597 < 2e-16 ***
## ethnocentrism_std 0.150606 0.023692 6.357 2.55e-10 ***
## rwa_std -0.011515 0.022567 -0.510 0.609928
## sdo_std 0.029222 0.022810 1.281 0.200317
## church_attendance_std -0.073633 0.020014 -3.679 0.000240 ***
## ideology_std 0.109669 0.021880 5.012 5.85e-07 ***
## age_std -0.109492 0.020714 -5.286 1.39e-07 ***
## education_collapse -0.088337 0.016775 -5.266 1.55e-07 ***
## Q1_interest_in_politics_std -0.080357 0.020380 -3.943 8.33e-05 ***
## income_num_std -0.056217 0.023382 -2.404 0.016295 *
## marital_status 0.054991 0.046724 1.177 0.239357
## big5_conscientiousness_std -0.004879 0.022416 -0.218 0.827728
## big5_agreeableness_std -0.023693 0.022092 -1.072 0.283648
## big5_extraversion_std 0.044896 0.020263 2.216 0.026827 *
## big5_openness_std 0.088365 0.021390 4.131 3.76e-05 ***
## big5_stability_std -0.013207 0.021129 -0.625 0.532011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8587 on 1987 degrees of freedom
## Multiple R-squared: 0.2686, Adjusted R-squared: 0.2627
## F-statistic: 45.61 on 16 and 1987 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + 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
## -3.9654 -0.5847 -0.0067 0.6028 3.1052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.122450 0.074607 1.641 0.100900
## ethnocentrism_std 0.046920 0.025928 1.810 0.070503 .
## rwa_std 0.034955 0.024842 1.407 0.159557
## sdo_std -0.009456 0.027689 -0.341 0.732763
## church_attendance_std -0.075835 0.022890 -3.313 0.000939 ***
## ideology_std 0.281369 0.024096 11.677 < 2e-16 ***
## age_std -0.076037 0.024436 -3.112 0.001886 **
## education_collapse -0.051015 0.021284 -2.397 0.016627 *
## Q1_interest_in_politics_std 0.165773 0.022148 7.485 1.07e-13 ***
## income_num_std 0.017564 0.022438 0.783 0.433834
## marital_status 0.077592 0.043929 1.766 0.077495 .
## big5_conscientiousness_std 0.036610 0.025518 1.435 0.151533
## big5_agreeableness_std -0.061473 0.026039 -2.361 0.018332 *
## big5_extraversion_std -0.027773 0.021938 -1.266 0.205674
## big5_openness_std -0.012565 0.023932 -0.525 0.599625
## big5_stability_std -0.026998 0.024728 -1.092 0.275066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9106 on 1984 degrees of freedom
## Multiple R-squared: 0.1771, Adjusted R-squared: 0.1709
## F-statistic: 28.47 on 15 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 +
## 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
## -3.7113 -0.5013 0.0169 0.5364 3.4179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.144329 0.067220 2.147 0.031905 *
## devalued_std 0.445616 0.020742 21.484 < 2e-16 ***
## ethnocentrism_std 0.020197 0.023391 0.863 0.388005
## rwa_std 0.039871 0.022381 1.782 0.074984 .
## sdo_std -0.011707 0.024945 -0.469 0.638908
## church_attendance_std -0.056458 0.020641 -2.735 0.006289 **
## ideology_std 0.236792 0.021807 10.858 < 2e-16 ***
## age_std 0.006634 0.022347 0.297 0.766597
## education_collapse -0.057312 0.019176 -2.989 0.002836 **
## Q1_interest_in_politics_std 0.125753 0.020040 6.275 4.28e-10 ***
## income_num_std 0.032179 0.020225 1.591 0.111757
## marital_status 0.075048 0.039575 1.896 0.058058 .
## big5_conscientiousness_std 0.086439 0.023105 3.741 0.000188 ***
## big5_agreeableness_std -0.037389 0.023485 -1.592 0.111541
## big5_extraversion_std 0.008396 0.019835 0.423 0.672116
## big5_openness_std -0.007143 0.021561 -0.331 0.740474
## big5_stability_std 0.072148 0.022750 3.171 0.001541 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8203 on 1983 degrees of freedom
## Multiple R-squared: 0.3325, Adjusted R-squared: 0.3271
## F-statistic: 61.73 on 16 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 +
## 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
## -2.85269 -0.52697 -0.01242 0.56504 2.65465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.190274 0.069511 2.737 0.006250 **
## ethnocentrism_std 0.045025 0.024157 1.864 0.062489 .
## rwa_std 0.070199 0.023145 3.033 0.002453 **
## sdo_std 0.113377 0.025798 4.395 1.17e-05 ***
## church_attendance_std -0.045999 0.021326 -2.157 0.031132 *
## ideology_std 0.308298 0.022451 13.732 < 2e-16 ***
## age_std -0.188009 0.022766 -8.258 2.67e-16 ***
## education_collapse -0.074801 0.019830 -3.772 0.000167 ***
## Q1_interest_in_politics_std 0.040594 0.020635 1.967 0.049298 *
## income_num_std 0.000187 0.020905 0.009 0.992865
## marital_status 0.094544 0.040928 2.310 0.020990 *
## big5_conscientiousness_std -0.031695 0.023775 -1.333 0.182643
## big5_agreeableness_std -0.049807 0.024261 -2.053 0.040202 *
## big5_extraversion_std -0.038056 0.020440 -1.862 0.062768 .
## big5_openness_std 0.049330 0.022297 2.212 0.027051 *
## big5_stability_std -0.061531 0.023039 -2.671 0.007631 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8484 on 1984 degrees of freedom
## Multiple R-squared: 0.2857, Adjusted R-squared: 0.2803
## F-statistic: 52.9 on 15 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 + 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
## -2.91199 -0.47960 0.01676 0.48726 2.57157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.208548 0.064044 3.256 0.001148 **
## devalued_std 0.372197 0.019762 18.834 < 2e-16 ***
## ethnocentrism_std 0.022705 0.022286 1.019 0.308427
## rwa_std 0.074306 0.021323 3.485 0.000503 ***
## sdo_std 0.111497 0.023766 4.691 2.90e-06 ***
## church_attendance_std -0.029815 0.019666 -1.516 0.129657
## ideology_std 0.271065 0.020777 13.047 < 2e-16 ***
## age_std -0.118959 0.021291 -5.587 2.63e-08 ***
## education_collapse -0.080061 0.018270 -4.382 1.24e-05 ***
## Q1_interest_in_politics_std 0.007168 0.019093 0.375 0.707395
## income_num_std 0.012394 0.019270 0.643 0.520173
## marital_status 0.092419 0.037705 2.451 0.014327 *
## big5_conscientiousness_std 0.009925 0.022013 0.451 0.652148
## big5_agreeableness_std -0.029691 0.022375 -1.327 0.184677
## big5_extraversion_std -0.007846 0.018898 -0.415 0.678056
## big5_openness_std 0.053859 0.020542 2.622 0.008812 **
## big5_stability_std 0.021279 0.021675 0.982 0.326361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7815 on 1983 degrees of freedom
## Multiple R-squared: 0.3941, Adjusted R-squared: 0.3892
## F-statistic: 80.6 on 16 and 1983 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = populism_rw_std ~ ethnocentrism_std + rwa_std +
## sdo_std + church_attendance_std + ideology_std + 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
## -4.0001 -0.6126 -0.0151 0.6261 2.9177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04335 0.06303 0.688 0.491714
## ethnocentrism_std 0.11728 0.02593 4.522 6.48e-06 ***
## rwa_std -0.13504 0.02927 -4.613 4.22e-06 ***
## sdo_std -0.07768 0.02536 -3.063 0.002222 **
## church_attendance_std -0.04109 0.02479 -1.658 0.097534 .
## ideology_std 0.01554 0.02454 0.633 0.526502
## age_std 0.03169 0.02384 1.329 0.183917
## education_collapse -0.04835 0.02070 -2.336 0.019604 *
## Q1_interest_in_politics_std 0.06313 0.02337 2.701 0.006970 **
## income_num_std -0.02027 0.02355 -0.861 0.389596
## marital_status 0.12227 0.04795 2.550 0.010853 *
## big5_conscientiousness_std 0.09311 0.02691 3.460 0.000551 ***
## big5_agreeableness_std 0.04306 0.02742 1.570 0.116560
## big5_extraversion_std 0.02771 0.02745 1.009 0.312987
## big5_openness_std -0.06637 0.02461 -2.697 0.007056 **
## big5_stability_std -0.02555 0.02509 -1.018 0.308591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.972 on 1985 degrees of freedom
## Multiple R-squared: 0.06234, Adjusted R-squared: 0.05525
## F-statistic: 8.798 on 15 and 1985 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 +
## 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
## -4.4382 -0.5559 -0.0037 0.5526 3.3044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.034836 0.058634 0.594 0.552495
## devalued_std 0.371912 0.021122 17.608 < 2e-16 ***
## ethnocentrism_std 0.069707 0.024275 2.872 0.004127 **
## rwa_std -0.127222 0.027233 -4.672 3.19e-06 ***
## sdo_std -0.085272 0.023597 -3.614 0.000309 ***
## church_attendance_std -0.032558 0.023064 -1.412 0.158220
## ideology_std 0.018410 0.022827 0.807 0.420042
## age_std 0.056462 0.022224 2.541 0.011142 *
## education_collapse -0.042930 0.019259 -2.229 0.025917 *
## Q1_interest_in_politics_std 0.053373 0.021748 2.454 0.014207 *
## income_num_std -0.005222 0.021923 -0.238 0.811772
## marital_status 0.114211 0.044608 2.560 0.010530 *
## big5_conscientiousness_std 0.091352 0.025030 3.650 0.000269 ***
## big5_agreeableness_std 0.038860 0.025510 1.523 0.127843
## big5_extraversion_std 0.040487 0.025548 1.585 0.113184
## big5_openness_std -0.055204 0.022899 -2.411 0.016008 *
## big5_stability_std 0.049591 0.023724 2.090 0.036712 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9041 on 1984 degrees of freedom
## Multiple R-squared: 0.1891, Adjusted R-squared: 0.1825
## F-statistic: 28.91 on 16 and 1984 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = conspiratorial_orientation_std ~ ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## 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
## -3.2480 -0.5853 -0.0088 0.6684 2.5271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03313 0.06298 0.526 0.59886
## ethnocentrism_std 0.15589 0.02591 6.016 2.12e-09 ***
## rwa_std 0.01902 0.02925 0.650 0.51552
## sdo_std 0.01422 0.02534 0.561 0.57466
## church_attendance_std -0.04951 0.02477 -1.999 0.04576 *
## ideology_std 0.02558 0.02452 1.043 0.29686
## age_std -0.03477 0.02382 -1.459 0.14460
## education_collapse -0.03013 0.02068 -1.457 0.14530
## Q1_interest_in_politics_std -0.05286 0.02335 -2.264 0.02370 *
## income_num_std -0.02599 0.02353 -1.104 0.26955
## marital_status 0.06672 0.04791 1.393 0.16389
## big5_conscientiousness_std 0.07609 0.02689 2.830 0.00470 **
## big5_agreeableness_std 0.01735 0.02740 0.633 0.52678
## big5_extraversion_std 0.02287 0.02743 0.834 0.40444
## big5_openness_std -0.03449 0.02459 -1.403 0.16079
## big5_stability_std -0.06796 0.02507 -2.711 0.00676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9712 on 1985 degrees of freedom
## Multiple R-squared: 0.06388, Adjusted R-squared: 0.05681
## F-statistic: 9.031 on 15 and 1985 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 + 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
## -3.3373 -0.5406 0.0195 0.6065 2.7156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.025635 0.059594 0.430 0.66713
## devalued_std 0.327730 0.021468 15.266 < 2e-16 ***
## ethnocentrism_std 0.113969 0.024672 4.619 4.1e-06 ***
## rwa_std 0.025911 0.027679 0.936 0.34931
## sdo_std 0.007536 0.023983 0.314 0.75339
## church_attendance_std -0.041987 0.023442 -1.791 0.07343 .
## ideology_std 0.028110 0.023201 1.212 0.22582
## age_std -0.012945 0.022588 -0.573 0.56665
## education_collapse -0.025357 0.019574 -1.295 0.19532
## Q1_interest_in_politics_std -0.061463 0.022104 -2.781 0.00548 **
## income_num_std -0.012730 0.022282 -0.571 0.56784
## marital_status 0.059625 0.045338 1.315 0.18862
## big5_conscientiousness_std 0.074546 0.025440 2.930 0.00343 **
## big5_agreeableness_std 0.013648 0.025928 0.526 0.59869
## big5_extraversion_std 0.034136 0.025966 1.315 0.18879
## big5_openness_std -0.024658 0.023274 -1.059 0.28951
## big5_stability_std -0.001747 0.024112 -0.072 0.94225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9189 on 1984 degrees of freedom
## Multiple R-squared: 0.1623, Adjusted R-squared: 0.1555
## F-statistic: 24.02 on 16 and 1984 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 == "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",
"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 == "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",
"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 +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = US_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7902 -0.3069 -0.1260 0.3241 1.2238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.384381 0.032912 11.679 < 2e-16 ***
## devalued_std 0.022474 0.009610 2.339 0.01946 *
## ethnocentrism_std -0.062621 0.011402 -5.492 4.48e-08 ***
## rwa_std 0.050611 0.010790 4.691 2.91e-06 ***
## sdo_std 0.061084 0.011614 5.260 1.60e-07 ***
## church_attendance_std 0.005266 0.009996 0.527 0.59837
## ideology_std 0.163601 0.010625 15.398 < 2e-16 ***
## age_std 0.080185 0.010402 7.709 1.99e-14 ***
## education_collapse -0.028898 0.009383 -3.080 0.00210 **
## Q1_interest_in_politics_std 0.025618 0.009704 2.640 0.00836 **
## income_num_std 0.017799 0.009893 1.799 0.07215 .
## marital_status 0.039444 0.019392 2.034 0.04208 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4025 on 1988 degrees of freedom
## Multiple R-squared: 0.2506, Adjusted R-squared: 0.2464
## F-statistic: 60.43 on 11 and 1988 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = vote_lepen ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = FR_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7836 -0.2480 -0.1236 0.1047 1.3489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2998465 0.0283375 10.581 < 2e-16 ***
## devalued_std 0.0451692 0.0087409 5.168 2.61e-07 ***
## ethnocentrism_std 0.0916837 0.0103043 8.898 < 2e-16 ***
## rwa_std -0.0193142 0.0097169 -1.988 0.04698 *
## sdo_std -0.0256909 0.0095530 -2.689 0.00722 **
## church_attendance_std 0.0204150 0.0086996 2.347 0.01904 *
## ideology_std 0.1287809 0.0095222 13.524 < 2e-16 ***
## age_std -0.0202638 0.0088813 -2.282 0.02262 *
## education_collapse -0.0309425 0.0073024 -4.237 2.37e-05 ***
## Q1_interest_in_politics_std 0.0047846 0.0088276 0.542 0.58788
## income_num_std -0.0002376 0.0101706 -0.023 0.98136
## marital_status 0.0290589 0.0204336 1.422 0.15515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3758 on 1992 degrees of freedom
## Multiple R-squared: 0.2, Adjusted R-squared: 0.1956
## F-statistic: 45.28 on 11 and 1992 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = vote_prawo ~ devalued_std + ethnocentrism_std +
## rwa_std + sdo_std + church_attendance_std + ideology_std +
## age_std + education_collapse + Q1_interest_in_politics_std +
## income_num_std + marital_status, data = PL_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.84460 -0.23685 -0.09932 0.22092 1.10771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.288512 0.023343 12.360 < 2e-16 ***
## devalued_std -0.001266 0.008212 -0.154 0.87751
## ethnocentrism_std 0.017882 0.009523 1.878 0.06055 .
## rwa_std 0.082556 0.010759 7.673 2.61e-14 ***
## sdo_std -0.053105 0.009096 -5.838 6.16e-09 ***
## church_attendance_std -0.042142 0.009132 -4.615 4.19e-06 ***
## ideology_std 0.159888 0.009056 17.656 < 2e-16 ***
## age_std 0.028083 0.008584 3.272 0.00109 **
## education_collapse -0.020471 0.007658 -2.673 0.00758 **
## Q1_interest_in_politics_std 0.041777 0.008555 4.883 1.13e-06 ***
## income_num_std 0.001146 0.008664 0.132 0.89481
## marital_status 0.014156 0.017631 0.803 0.42211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3603 on 1989 degrees of freedom
## Multiple R-squared: 0.3042, Adjusted R-squared: 0.3004
## F-statistic: 79.07 on 11 and 1989 DF, p-value: < 2.2e-16