The role of partisanship, resentment, and subclinical antisocial traits in illiberal attitudes (Paper RMarkdown)
Julien Labarre & Pierce Christoffersen
Version of September 26, 2025
Abstract
Why do citizens tolerate illiberal governance in a self-proclaimed democracy? We investigate the antecedents of support for illiberal governance tactics in the wake of Donald Trump’s reelection. We compare four families of explanations—partisan tradeoffs under polarization, authoritarian predispositions and preference for hierarchy, dark personality traits, and resentment captured by feelings of being devalued (FBD). We also examine populist attitudes as a contested case that may either license or restrain illiberalism. Using an original nationally matched survey of 1,059 US voters fielded in late 2024, we analyze predictors of two outcomes: approval of presidential lawbreaking and support for granting Trump permanent emergency powers. Results show sizable and consistent associations for partisanship, with dark-trait measures exerting significant independent effects, and psychopathy even rivaling a vote cast for Trump in the case of “casual” lawbreaking. Feelings of being devalued and social dominance orientation are also positively associated with illiberal attitudes, while right-wing authoritarianism relates specifically to support for emergency powers. By contrast, we find that populist attitudes–which are often loosely paired with illiberal attitudes in political commentary–are negatively associated with both outcomes, suggesting that anti-elite orientations may constrain rather than fuel illiberalism.
Packages
Below are the packages necessary for the analysis.
# Load all the necessary packages
library(estimatr)
library(huxtable)
library(car)
library(broom.mixed)
library(modelsummary)
library(ggplot2)
library(gridExtra)
library(dplyr)
library(grid)
library(stargazer)
library(tidyverse)
library(rcartocolor)
library(cluster)
library(factoextra)
library(corrplot)
library(readr)
library(pheatmap)
library(writexl)
library(ggplot2)
library(jtools)
library(haven)
library(readr)
library(readxl)
library(hrbrthemes)
library(viridis)
library(igraph)
library(leaps)
library(lmtest)
library(fmsb)
library(RColorBrewer)
library(scales)
library(patchwork)
library(stargazer)
# Disable scientific notation
options(scipen=999)Regression models
## Rows: 1059 Columns: 380
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): StartDate, EndDate, IPAddress, RecordedDate, ResponseId, Distribu...
## dbl (344): Status, Progress, Duration__in_seconds_, Finished, LocationLatitu...
## lgl (19): RecipientLastName, RecipientFirstName, RecipientEmail, ExternalRe...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Variable definition and recoding
df1 <- df1 %>%
mutate(
# keep as is
trump_lawbreaking_acceptance = Q39,
# keep as is
trump_emergency_powers_support = Q40,
# recode prefer not to say as NA
gender = ifelse(Q2 == 98, NA, Q2),
# calculate age
age = 2024 - Q3,
# keep education as is
education = Q5,
# keep republican as is
republican = Republicans,
# recode ethnic background into white dummy
white = ifelse(Q7 == 1, 1, 0),
# recode ideology
ideology = ifelse(Q24 == 99, NA, Q24),
# keep political interest as is
political_interest = Q22,
# keep Trump voter as is
trump_voter = TrumpVoter,
# fbd
fbd = rowMeans(across(c(Q20_1, Q20_2, Q20_3)), na.rm = TRUE),
# machiavellianism (averages Q33_1 through Q33_7)
machiavellianism = rowMeans(across(all_of(paste0("Q33_", 1:7))), na.rm = TRUE),
# narcissism (averages Q34_1 through Q34_7)
narcissism = rowMeans(across(all_of(paste0("Q34_", 1:7))), na.rm = TRUE),
#psychopathy (averages Q35_1 through Q35_7)
psychopathy = rowMeans(across(all_of(paste0("Q35_", 1:7))), na.rm = TRUE),
# sadism (averages Q36_1 through Q36_7)
sadism = rowMeans(across(all_of(paste0("Q36_", 1:7))), na.rm = TRUE),
# RWA reverse-coded items:
Q17_1r = 8 - Q17_1,
Q17_4r = 8 - Q17_4,
Q17_5r = 8 - Q17_5,
Q17_7r = 8 - Q17_7,
# RWA index: mean of all 8 (after reversing the 4 items)
rwa = rowMeans(across(c(Q17_1r, Q17_2, Q17_3, Q17_4r, Q17_5r, Q17_6)), na.rm = TRUE),
# SDO reverse-coded items: 1–7 scale, so use 8 - x
Q16_3r = 8 - Q16__3,
Q16_4r = 8 - Q16__4,
Q16_7r = 8 - Q16__7,
Q16_8r = 8 - Q16__8,
# SDO index: mean of all 8 items
sdo = rowMeans(across(c(Q16__1, Q16__2, Q16_3r, Q16_4r, Q16__5, Q16__6, Q16_7r, Q16_8r)), na.rm = TRUE),
# populist attitudes
populist_attitudes = rowMeans(across(paste0("Q37_", 1:8)), na.rm = TRUE)
)Cronbach’s alpha
# Reliability for multi-item scales
library(psych)
# --- Item sets (matching your questionnaire) ---
FBD_items <- df1 %>% dplyr::select(Q20_1, Q20_2, Q20_3)
POP_items <- df1 %>% dplyr::select(dplyr::all_of(paste0("Q37_", 1:8))) # Akkerman et al. 8 items
RWA_items <- df1 %>% dplyr::select(dplyr::all_of(paste0("Q17_", 1:6))) # Bizumic & Duckitt VSA items
SDO_items <- df1 %>% dplyr::select(dplyr::all_of(c("Q16__1","Q16__2","Q16__3","Q16__4",
"Q16__5","Q16__6","Q16__7","Q16__8"))) # SDO7(s)
MACH_items <- df1 %>% dplyr::select(dplyr::all_of(paste0("Q33_", 1:7))) # SD4 Machiavellianism
NARC_items <- df1 %>% dplyr::select(dplyr::all_of(paste0("Q34_", 1:7))) # SD4 Narcissism
PSY_items <- df1 %>% dplyr::select(dplyr::all_of(paste0("Q35_", 1:7))) # SD4 Psychopathy
SADISM_items<- df1 %>% dplyr::select(dplyr::all_of(paste0("Q36_", 1:7))) # SD4 Sadism (if used)
# --- Compute alphas; check.keys lets psych reverse-code as needed ---
a_FBD <- psych::alpha(FBD_items, check.keys = TRUE)
a_POP <- psych::alpha(POP_items, check.keys = TRUE)
a_RWA <- psych::alpha(RWA_items, check.keys = TRUE)
a_SDO <- psych::alpha(SDO_items, check.keys = TRUE)
a_MACH <- psych::alpha(MACH_items, check.keys = TRUE)
a_NARC <- psych::alpha(NARC_items, check.keys = TRUE)
a_PSY <- psych::alpha(PSY_items, check.keys = TRUE)
a_SADISM<- psych::alpha(SADISM_items,check.keys = TRUE)
# --- Rounded values for inline reporting ---
alpha_fbd <- round(a_FBD$total$raw_alpha, 2)
alpha_pop <- round(a_POP$total$raw_alpha, 2)
alpha_rwa <- round(a_RWA$total$raw_alpha, 2)
alpha_sdo <- round(a_SDO$total$raw_alpha, 2)
alpha_mach <- round(a_MACH$total$raw_alpha, 2)
alpha_narc <- round(a_NARC$total$raw_alpha, 2)
alpha_psy <- round(a_PSY$total$raw_alpha, 2)
alpha_sad <- round(a_SADISM$total$raw_alpha,2)
# Optional: quick table for the appendix
reliab_tbl <- tibble::tibble(
scale = c("Feelings of being devalued (FBD)", "Populist attitudes",
"Right-wing authoritarianism (RWA)", "Social dominance orientation (SDO)",
"Machiavellianism", "Narcissism", "Psychopathy", "Sadism"),
items = c(3, 8, 8, 8, 7, 7, 7, 7),
alpha = c(alpha_fbd, alpha_pop, alpha_rwa, alpha_sdo,
alpha_mach, alpha_narc, alpha_psy, alpha_sad)
)
knitr::kable(reliab_tbl, digits = 2, caption = "Internal consistency (Cronbach’s α) for all multi-item scales.")| scale | items | alpha |
|---|---|---|
| Feelings of being devalued (FBD) | 3 | 0.82 |
| Populist attitudes | 8 | 0.82 |
| Right-wing authoritarianism (RWA) | 8 | 0.58 |
| Social dominance orientation (SDO) | 8 | 0.76 |
| Machiavellianism | 7 | 0.78 |
| Narcissism | 7 | 0.87 |
| Psychopathy | 7 | 0.90 |
| Sadism | 7 | 0.87 |
Trump lawbreaking
# Run OLS regression
model1 <- lm(trump_lawbreaking_acceptance ~ education + white + ideology + gender +
political_interest + trump_voter + fbd + populist_attitudes + psychopathy + narcissism + machiavellianism + rwa + sdo , data=df1
)
# Format Regression Table
stargazer(model1, report = "vcsp*", 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, single.row=TRUE, dep.var.labels = c("Acceptance of presidential lawbreaking"), title = "Regression Results (Wave 2)", covariate.labels = c("Education", "White", "Ideology", "Gender", "Political interest", "Trump vote", "Feelings of being devalued", "Populist attitudes", "Psychopathy", "Narcissism", "Machiavellianism", "Right-wing authoritarianism", "Social dominance orientation"))| Dependent variable: | |
| Acceptance of presidential lawbreaking | |
| Education | -0.054 (0.051) |
| p = 0.296 | |
| White | -0.097 (0.106) |
| p = 0.362 | |
| Ideology | 0.042 (0.020) |
| p = 0.039* | |
| Gender | 0.001 (0.012) |
| p = 0.953 | |
| Political interest | -0.030 (0.060) |
| p = 0.623 | |
| Trump vote | 0.770 (0.116) |
| p = 0.000*** | |
| Feelings of being devalued | 0.116 (0.037) |
| p = 0.002** | |
| Populist attitudes | -0.260 (0.084) |
| p = 0.003** | |
| Psychopathy | 0.570 (0.064) |
| p = 0.000*** | |
| Narcissism | 0.324 (0.074) |
| p = 0.00002*** | |
| Machiavellianism | 0.164 (0.090) |
| p = 0.068. | |
| Right-wing authoritarianism | 0.029 (0.054) |
| p = 0.591 | |
| Social dominance orientation | 0.226 (0.055) |
| p = 0.00004*** | |
| Constant | -0.926 (0.436) |
| p = 0.034* | |
| Observations | 1,059 |
| R2 | 0.336 |
| Adjusted R2 | 0.328 |
| Residual Std. Error | 1.632 (df = 1045) |
| F Statistic | 40.695*** (df = 13; 1045) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 |
Trump emergency powers
# Run OLS regression
model2 <- lm(trump_emergency_powers_support ~ education + white + ideology + gender +
political_interest + trump_voter + fbd + populist_attitudes + psychopathy + narcissism + machiavellianism + rwa + sdo , data=df1
)
# Format Regression Table
stargazer(model2, report = "vcsp*", 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, single.row=TRUE, dep.var.labels = c("Support for Emergency Powers"), title = "Regression Results (Wave 2)", covariate.labels = c("Education", "White", "Ideology", "Gender", "Political interest", "Trump vote", "Feelings of being devalued", "Populist attitudes", "Psychopathy", "Narcissism", "Machiavellianism", "Right-wing authoritarianism", "Social dominance orientation"))| Dependent variable: | |
| Support for Emergency Powers | |
| Education | -0.185 (0.050) |
| p = 0.0002*** | |
| White | -0.110 (0.102) |
| p = 0.284 | |
| Ideology | 0.063 (0.020) |
| p = 0.002** | |
| Gender | 0.013 (0.012) |
| p = 0.249 | |
| Political interest | 0.064 (0.058) |
| p = 0.270 | |
| Trump vote | 1.295 (0.112) |
| p = 0.000*** | |
| Feelings of being devalued | 0.126 (0.035) |
| p = 0.0004*** | |
| Populist attitudes | -0.175 (0.081) |
| p = 0.033* | |
| Psychopathy | 0.302 (0.062) |
| p = 0.00001*** | |
| Narcissism | 0.515 (0.071) |
| p = 0.000*** | |
| Machiavellianism | 0.256 (0.087) |
| p = 0.004** | |
| Right-wing authoritarianism | 0.160 (0.052) |
| p = 0.003** | |
| Social dominance orientation | 0.210 (0.053) |
| p = 0.0001*** | |
| Constant | -2.182 (0.421) |
| p = 0.00000*** | |
| Observations | 1,059 |
| R2 | 0.429 |
| Adjusted R2 | 0.422 |
| Residual Std. Error | 1.574 (df = 1045) |
| F Statistic | 60.406*** (df = 13; 1045) |
| Note: | . p<0.1; * p<0.05; ** p<0.01; *** p<0.001 |
Comparing both models
# --- One Stargazer table for both models, written to a .tex file ---
stargazer(
model1, model2,
# se = rob_se, # <- uncomment to use robust SEs
type = "latex",
out = "Output/regression_table.tex", # <- this file goes to Overleaf
title = "Regression Results",
column.labels = c("Tolerance for lawbreaking", "Support for Trump emergency powers"),
dep.var.labels.include = FALSE,
report = "vcsp*", # coef, SE, p, and stars
digits = 3,
single.row = TRUE,
star.char = c(".", "*", "**", "***"),
star.cutoffs = c(0.1, 0.05, 0.01, 0.001),
covariate.labels = c(
"Education","White","Ideology","Gender","Political interest","Trump vote",
"Feelings of being devalued","Populist attitudes",
"Psychopathy","Narcissism","Machiavellianism",
"Right-wing authoritarianism","Social dominance orientation"
),
notes = c(". p<0.1; * p<0.05; ** p<0.01; *** p<0.001"),
notes.append = FALSE
)Plotting regression models
# Clean labels for plotting
coef_labels <- c(
"Education" = "education",
"White" = "white",
"Ideology" = "ideology",
"Gender" = "gender",
"Political interest" = "political_interest",
"Trump vote" = "trump_voter",
"Feelings of being devalued" = "fbd",
"Populist attitudes" = "populist_attitudes",
"Psychopathy" = "psychopathy",
"Narcissism" = "narcissism",
"Machiavellianism" = "machiavellianism",
"Right-wing authoritarianism" = "rwa",
"Social dominance orientation" = "sdo"
)
# Overlay plot for both models
p_overlay <- plot_summs(
model1, model2,
model.names = c("Tolerance for Trump's lawbreaking", "Support for Trump emergency powers"),
coefs = coef_labels,
scale = TRUE, # standardized coefficients
robust = "HC2", # robust CIs
ci.level = 0.95,
plot.distributions = FALSE,
exclude.intercept = TRUE,
point.size = 2.6,
line.size = 0.7,
colors = c("darkgoldenrod1", "coral") # two distinct colors
) +
geom_vline(xintercept = 0, linetype = 2, linewidth = 0.5) +
labs(x = "Standardized coefficient (β)", y = NULL, title = "Comparing Trump Norm Violation Models") +
theme_minimal(base_size = 12) +
theme(
legend.position = "bottom",
legend.title = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(size = 10)
)
p_overlay
Better plot
library(dplyr)
library(broom)
library(ggplot2)
# --- 1) Make z-scored copies of ALL predictors + BOTH DVs ---
preds_all <- c("education","white","ideology","gender","political_interest",
"trump_voter","fbd","populist_attitudes","psychopathy",
"narcissism","machiavellianism","rwa","sdo")
dfz <- df1 %>%
mutate(
across(all_of(preds_all), ~ as.numeric(.x), .names = "{.col}") %>%
{ mutate(., across(all_of(preds_all), ~ as.numeric(scale(.x)),
.names = "{.col}_z")) } %>%
mutate(lawbreaking_z = as.numeric(scale(trump_lawbreaking_acceptance)),
emerpowers_z = as.numeric(scale(trump_emergency_powers_support)))
)
# --- 2) Fit models ONLY on z-variables ---
m1z <- lm(lawbreaking_z ~ education_z + white_z + ideology_z + gender_z +
political_interest_z + trump_voter_z + fbd_z + populist_attitudes_z +
psychopathy_z + narcissism_z + machiavellianism_z + rwa_z + sdo_z,
data = dfz)
m2z <- lm(emerpowers_z ~ education_z + white_z + ideology_z + gender_z +
political_interest_z + trump_voter_z + fbd_z + populist_attitudes_z +
psychopathy_z + narcissism_z + machiavellianism_z + rwa_z + sdo_z,
data = dfz)
# --- 3) Tidy + 95%/99% CIs ---
df1_95 <- tidy(m1z, conf.int = TRUE) %>% mutate(Model = "Tolerance for Trump's lawbreaking")
df2_95 <- tidy(m2z, conf.int = TRUE) %>% mutate(Model = "Support for Trump emergency powers")
ci99_m1 <- confint(m1z, level = 0.99) %>% as.data.frame() %>%
mutate(term = rownames(.), Model = "Tolerance for Trump's lawbreaking")
ci99_m2 <- confint(m2z, level = 0.99) %>% as.data.frame() %>%
mutate(term = rownames(.), Model = "Support for Trump emergency powers")
names(ci99_m1)[1:2] <- c("conf.low2","conf.high2")
names(ci99_m2)[1:2] <- c("conf.low2","conf.high2")
ci99_df <- bind_rows(ci99_m1, ci99_m2)
coef_df <- bind_rows(df1_95, df2_95) %>%
left_join(ci99_df, by = c("term","Model")) %>%
filter(term != "(Intercept)") %>%
mutate(term = case_when(
term == "education_z" ~ "Education",
term == "white_z" ~ "White",
term == "ideology_z" ~ "Ideology",
term == "gender_z" ~ "Gender",
term == "political_interest_z" ~ "Political interest",
term == "trump_voter_z" ~ "Trump vote",
term == "fbd_z" ~ "Feelings of being devalued",
term == "populist_attitudes_z" ~ "Populist attitudes",
term == "psychopathy_z" ~ "Psychopathy",
term == "narcissism_z" ~ "Narcissism",
term == "machiavellianism_z" ~ "Machiavellianism",
term == "rwa_z" ~ "Right-wing authoritarianism",
term == "sdo_z" ~ "Social dominance orientation",
TRUE ~ term
))
# Order terms (top→bottom)
order_terms <- c("Education","White","Ideology","Gender","Political interest",
"Trump vote","Feelings of being devalued","Populist attitudes",
"Psychopathy","Narcissism","Machiavellianism",
"Right-wing authoritarianism","Social dominance orientation")
coef_df <- coef_df %>% mutate(term = factor(term, levels = rev(order_terms)))
# vertical offset so points/intervals don’t overlap
offset <- 0.18
coef_df <- coef_df %>%
mutate(term_id = as.numeric(term),
y = ifelse(Model == "Support for Trump emergency powers",
term_id + offset, term_id - offset))
# --- NEW: faint shading for selected substantively important predictors ---
highlight_terms <- c("Trump vote",
"Feelings of being devalued",
"Psychopathy",
"Narcissism",
"Social dominance orientation", "Ideology", "Populist attitudes")
band_df <- coef_df %>%
distinct(term, term_id) %>%
filter(term %in% highlight_terms) %>%
transmute(ymin = term_id - 0.5, ymax = term_id + 0.5)
# --- 4) Plot (with background bands) ---
p_better <- ggplot(coef_df, aes(y = y, x = estimate, shape = Model)) +
# background bands behind selected rows
geom_rect(data = band_df,
aes(xmin = -Inf, xmax = Inf, ymin = ymin, ymax = ymax),
inherit.aes = FALSE, fill = "grey90", alpha = 0.7) +
# CIs and points
geom_errorbarh(aes(xmin = conf.low2, xmax = conf.high2), height = 0,
linewidth = 0.4, color = "gray75", alpha = 0.5) + # 99% CI
geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0,
linewidth = 0.7, color = "gray25") + # 95% CI
geom_point(size = 3, color = "black", fill = "white", stroke = 0.9) +
geom_vline(xintercept = 0, linetype = "dotted") +
scale_shape_manual(values = c(
"Support for Trump emergency powers" = 16, # solid circle
"Tolerance for Trump's lawbreaking" = 21 # circle with border + white fill
)) +
scale_y_continuous(
breaks = seq_along(levels(coef_df$term)),
labels = levels(coef_df$term),
expand = expansion(mult = c(0.02, 0.05))
) +
labs(x = "Standardized coefficient (β)", y = NULL,
caption = "Note: Shaded rows indicate predictors significant at the 95% level in both models."
) +
theme_minimal(base_size = 14) +
theme(
legend.position = "bottom",
legend.title = element_blank(),
panel.grid.minor = element_blank(),
plot.caption = element_text(size = 10, face = "italic", hjust = 0.5)
)
# save the plot as PNG in the Output folder
ggsave(filename = "Output/better_plot.png",
plot = p_better,
width = 8, height = 9.5, dpi = 300)
p_better
Helter-skelter thoughts
CONSIDER ADDING Partisan Propaganda / Normalization
Hypothesis
Support for norm violations is greater among respondents with high
exposure to right-wing media.
Make sure not to give flank to critiques of pathologizing populist and illiberal voters, or worse, right-wing politics. Avoid all language that suggests “psychological deficits”, “deficiencies,” or “disorders.” Try to stick to accepted terminology around dark traits and dark tetrad. We can say things like support for illiberal tactics correlates with elements of sublicnical forms of psychopathy, narcissism, but don’t go further. As Furedi (2021) says: “The project of representing people’s voting behavior as a personality issue – rather than a matter of political choice – invariably leads to the depoliticization of populist behavior. Of course, we can’t be accused of depoliticizing populist/illiberal behavior in our paper, since we’re tying it to political polarization, partisanship, and we actually find Trump vote to be the strongest predictor. But nonetheless, let’s tread carefully.
Look at Hawkins, Riding and Mudde 2017
Maybe think about delegative democracy as a concept–or maybe speak of endorsement of delegative democracy instead of illiberal attitudes?
Blockwise variance
# --- Make a complete-case dataset across ALL variables used anywhere ---
all_vars <- c(
# outcomes
"trump_lawbreaking_acceptance","trump_emergency_powers_support",
# demographics / controls
"education","white", "gender","political_interest",
# partisanship
"trump_voter", "ideology",
# authoritarian predispositions
"rwa","sdo",
# dark traits
"machiavellianism","narcissism","psychopathy",
# affect
"fbd",
# populism
"populist_attitudes"
)
df_cc <- df1 %>% dplyr::select(all_of(all_vars)) %>% na.omit()
nrow(df_cc) # report common N used in all blockwise models## [1] 1059# --- Define the conceptual blocks (vectors of variable names) ---
block_demog <- c("education","white","gender","political_interest")
block_party <- c("trump_voter", "ideology")
block_auth <- c("rwa","sdo")
block_dark <- c("machiavellianism","narcissism","psychopathy")
block_fbd <- c("fbd")
block_pop <- c("populist_attitudes")
blocks_list <- list(
"Demographics & ideology" = block_demog,
"Partisanship" = block_party,
"Authoritarian predispositions" = block_auth,
"Dark traits (SD4)" = block_dark,
"Affective resentment (FBD)" = block_fbd,
"Populist attitudes" = block_pop
)
# --- Helper: build hierarchical models and compute R2, ΔR2, F-change ---
run_blockwise <- function(outcome, blocks, data) {
# sequentially add blocks
models <- list()
terms_so_far <- character(0)
rows <- list()
i <- 0
for (bn in names(blocks)) {
i <- i + 1
terms_so_far <- c(terms_so_far, blocks[[bn]])
fml <- as.formula(paste(outcome, "~", paste(terms_so_far, collapse = " + ")))
m <- lm(fml, data = data)
models[[i]] <- m
# Collect stats
R2 <- summary(m)$r.squared
k_new <- length(blocks[[bn]])
if (i == 1) {
rows[[i]] <- tibble::tibble(
step = i,
block = bn,
k_added = k_new,
R2 = R2,
delta_R2 = R2,
F_change = NA_real_,
df1 = NA_integer_,
df2 = NA_integer_,
p_change = NA_real_
)
} else {
# F change test: nested ANOVA between previous and current model
a <- anova(models[[i-1]], models[[i]])
rows[[i]] <- tibble::tibble(
step = i,
block = bn,
k_added = k_new,
R2 = R2,
delta_R2 = R2 - summary(models[[i-1]])$r.squared,
F_change = a$F[2],
df1 = a$Df[2],
df2 = a$Res.Df[2],
p_change = a$`Pr(>F)`[2]
)
}
}
out_tbl <- dplyr::bind_rows(rows)
list(table = out_tbl, models = models)
}Blockwise results: Acceptance of presidential lawbreaking
bw_law <- run_blockwise("trump_lawbreaking_acceptance", blocks_list, df_cc)
law_tbl <- bw_law$table %>%
mutate(
R2 = round(R2, 3),
delta_R2 = round(delta_R2, 3),
F_change = ifelse(is.na(F_change), NA, round(F_change, 2)),
p_change = ifelse(is.na(p_change), NA, scales::pvalue(p_change))
)
knitr::kable(
law_tbl,
align = "lccrrrcc",
caption = "Blockwise variance decomposition for acceptance of presidential lawbreaking",
col.names = c("Step","Block","k added","R²","ΔR²","F-change","df1","df2","p")
)| Step | Block | k added | R² | ΔR² | F-change | df1 | df2 | p |
|---|---|---|---|---|---|---|---|---|
| 1 | Demographics & ideology | 4 | 0.018 | 0.018 | NA | NA | NA | NA |
| 2 | Partisanship | 2 | 0.118 | 0.101 | 60.00 | 2 | 1052 | <0.001 |
| 3 | Authoritarian predispositions | 2 | 0.179 | 0.061 | 38.85 | 2 | 1050 | <0.001 |
| 4 | Dark traits (SD4) | 3 | 0.326 | 0.147 | 75.90 | 3 | 1047 | <0.001 |
| 5 | Affective resentment (FBD) | 1 | 0.330 | 0.005 | 7.08 | 1 | 1046 | 0.008 |
| 6 | Populist attitudes | 1 | 0.336 | 0.006 | 9.48 | 1 | 1045 | 0.002 |
ggplot(law_tbl, aes(x = reorder(block, step), y = delta_R2)) +
geom_col() +
geom_text(aes(label = sprintf("+%.3f", delta_R2)), vjust = -0.4, size = 3.5) +
labs(x = NULL, y = expression(Delta*R^2),
title = "Incremental variance explained by block (Lawbreaking)") +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
Blockwise results: Support for Trump emergency powers
bw_emerg <- run_blockwise("trump_emergency_powers_support", blocks_list, df_cc)
em_tbl <- bw_emerg$table %>%
mutate(
R2 = round(R2, 3),
delta_R2 = round(delta_R2, 3),
F_change = ifelse(is.na(F_change), NA, round(F_change, 2)),
p_change = ifelse(is.na(p_change), NA, scales::pvalue(p_change))
)
knitr::kable(
em_tbl,
align = "lccrrrcc",
caption = "Blockwise variance decomposition for support for permanent emergency powers",
col.names = c("Step","Block","k added","R²","ΔR²","F-change","df1","df2","p")
)| Step | Block | k added | R² | ΔR² | F-change | df1 | df2 | p |
|---|---|---|---|---|---|---|---|---|
| 1 | Demographics & ideology | 4 | 0.041 | 0.041 | NA | NA | NA | NA |
| 2 | Partisanship | 2 | 0.254 | 0.213 | 150.37 | 2 | 1052 | <0.001 |
| 3 | Authoritarian predispositions | 2 | 0.298 | 0.043 | 32.50 | 2 | 1050 | <0.001 |
| 4 | Dark traits (SD4) | 3 | 0.421 | 0.123 | 74.17 | 3 | 1047 | <0.001 |
| 5 | Affective resentment (FBD) | 1 | 0.427 | 0.006 | 10.48 | 1 | 1046 | 0.001 |
| 6 | Populist attitudes | 1 | 0.429 | 0.003 | 4.61 | 1 | 1045 | 0.032 |
ggplot(em_tbl, aes(x = reorder(block, step), y = delta_R2)) +
geom_col() +
geom_text(aes(label = sprintf("+%.3f", delta_R2)), vjust = -0.4, size = 3.5) +
labs(x = NULL, y = expression(Delta*R^2),
title = "Incremental variance explained by block (Emergency powers)") +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
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Gotta read
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