2024USElecStudy_Trust+Democracy
Alex Flores
2025-07-31
Nature collection on Trust & Democracy: Call for Papers
Deadline: 21 October 2025
Concerns about a rise in extremism across democratic nations and the emergence of illiberal democracies put the spotlight on research that helps us understand how crises of democracy can be averted.
Trust is an integral part of a healthy society: the trust people place in each other builds social cohesion; what media citizens place their trust in affects the spread of (mis)information; trust in democratic processes increases citizens’ engagement and democratic participation; and trust in democratic institutions underpins their legitimacy.
This Collection and open call for papers brings together work that researches the role of trust for society and - by extension - a functional democracy.
We welcome work that spans the breadth of basic research to large-scale interventions. The journals will consider submissions of research Articles, Registered Reports, and Resources on the topic.
Prep
Libraries & Data
library(tidyverse)
library(sandwich)
library(gtsummary)
library(flextable)
library(tables)
library(psych)
library(ggplot2)
library(openxlsx)
library(lmerTest)
library(lme4)
library(knitr)
library(hrbrthemes)
library(interactions)
library(sjPlot)
library(cowplot)
d <- read.csv("/Users/af13/Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_combined_cleancases_full.csv", na.strings = c("","NA"), header = T, fileEncoding="latin1")mcSummary() function
Variable Construction
Single-measures
# AI Risk
# numeracy
d <- d %>%
group_by(pid) %>%
fill(numeracy, .direction = "downup") %>%
ungroup()
addmargins(table(d$numeracy,d$wave, exclude = F))
# trustGovt
d[d$wave == 1 | d$wave == 2,] <- d[d$wave == 1 | d$wave == 2,] %>%
group_by(pid) %>%
fill(trustGovt1, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustGovt1,d$wave, exclude = F))
d[d$wave == 1 | d$wave == 2,] <- d[d$wave == 1 | d$wave == 2,] %>%
group_by(pid) %>%
fill(trustGovt2, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustGovt2,d$wave, exclude = F))
d$trustGovt <- rowMeans(d[,c("trustGovt1","trustGovt2")], na.rm = T)
d$trustGovt.c <- d$trustGovt - mean(d$trustGovt, na.rm = T)
# trustSci
psych::alpha(d[ ,c("trustSci_1","trustSci_2","trustSci_3","trustSci_4")])
d <- d %>%
group_by(pid) %>%
fill(trustSci_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustSci_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(trustSci_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustSci_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(trustSci_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustSci_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(trustSci_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$trustSci_4,d$wave, exclude = F))
d$trustSci <- rowMeans(d[, c("trustSci_1", "trustSci_2", "trustSci_3", "trustSci_4")], na.rm = T)
table(d$trustSci, d$wave)
# affProj_Trump
d[d$wave == 2 | d$wave == 3,] <- d[d$wave == 2 | d$wave == 3,] %>%
group_by(pid) %>%
fill(affProj_Trump_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$affProj_Trump_1,d$wave, exclude = F))
d[d$wave == 2 | d$wave == 3,] <- d[d$wave == 2 | d$wave == 3,] %>%
group_by(pid) %>%
fill(affProj_Trump_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$affProj_Trump_2,d$wave, exclude = F))
d[d$wave == 2 | d$wave == 3,] <- d[d$wave == 2 | d$wave == 3,] %>%
group_by(pid) %>%
fill(affProj_Trump_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$affProj_Trump_3,d$wave, exclude = F))
d[d$wave == 2 | d$wave == 3,] <- d[d$wave == 2 | d$wave == 3,] %>%
group_by(pid) %>%
fill(affProj_Trump_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$affProj_Trump_4,d$wave, exclude = F))
# punitive policies
d <- d %>%
group_by(pid) %>%
fill(deathpenalty, .direction = "downup") %>%
ungroup()
addmargins(table(d$deathpenalty,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(abortionban, .direction = "downup") %>%
ungroup()
addmargins(table(d$abortionban,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(ICEraids, .direction = "downup") %>%
ungroup()
addmargins(table(d$ICEraids,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(gunctrl, .direction = "downup") %>%
ungroup()
addmargins(table(d$gunctrl,d$wave, exclude = F))
addmargins(table(d$gunctrl,d$wave))
d <- d %>%
group_by(pid) %>%
fill(vaxxpass, .direction = "downup") %>%
ungroup()
addmargins(table(d$vaxxpass,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(hatespeech, .direction = "downup") %>%
ungroup()
addmargins(table(d$hatespeech,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(jailCEOs, .direction = "downup") %>%
ungroup()
addmargins(table(d$jailCEOs,d$wave, exclude = F))
# ideological position based on punitive policies
## deathpenalty, abortionban, ICEraids, gunctrl => negative = more liberal, positive = more conservative
## vaxxpass, hatespeech, jailCEOs => positive = more liberal, negative = more conservative
d$vaxxpass.r <- d$vaxxpass * -1
d$hatespeech.r <- d$hatespeech * -1
d$jailCEOs.r <- d$jailCEOs * -1
describe(d[,c('deathpenalty', 'abortionban', 'ICEraids', 'gunctrl','vaxxpass.r', 'hatespeech.r', 'jailCEOs.r')])
d$issue_ideology <- rowMeans(d[,c('deathpenalty', 'abortionban', 'ICEraids', 'gunctrl','vaxxpass.r', 'hatespeech.r', 'jailCEOs.r')], na.rm = T)
psych::alpha(d[,c('deathpenalty', 'abortionban', 'ICEraids', 'gunctrl','vaxxpass.r', 'hatespeech.r', 'jailCEOs.r')]) # a bit low!
# hist(d$issue_ideology)
psych::describe(d$issue_ideology) # that is a normal distribution
d <- d %>%
group_by(pid) %>%
fill(issue_ideology, .direction = "downup") %>%
ungroup()
addmargins(table(d$issue_ideology,d$wave, exclude = F))
# climate change belief
d$climbel_1 <- d$climatechange_belief_1
d$climbel_2 <- d$climatechange_belief_2
d$climbel_3 <- d$climatechange_belief_3
d$climbel_4 <- d$climatechange_belief_4
d <- d %>%
group_by(pid) %>%
fill(climatechange_belief_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$climatechange_belief_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(climatechange_belief_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$climatechange_belief_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(climatechange_belief_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$climatechange_belief_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(climatechange_belief_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$climatechange_belief_4,d$wave, exclude = F))
# GCB
d <- d %>%
group_by(pid) %>%
fill(GCB_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(GCB_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(GCB_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(GCB_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB_4,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(GCB_5, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB_5,d$wave, exclude = F))
psych::alpha(d[,c("GCB_1", "GCB_2", "GCB_3", "GCB_4", "GCB_5")])
d <- d %>%
group_by(pid) %>%
fill(GCB, .direction = "downup") %>%
ungroup()
addmargins(table(d$GCB,d$wave, exclude = F))
# AI risk
d <- d %>%
group_by(pid) %>%
fill(AIrisk_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$AIrisk_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(AIrisk_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$AIrisk_2,d$wave, exclude = F))
d$AIrisk <- rowMeans(d[,c("AIrisk_1","AIrisk_2")], na.rm = T)
d$AIrisk.c <- d$AIrisk - mean(d$AIrisk, na.rm = T)
# Group FT
d <- d %>%
group_by(pid) %>%
fill(FT_groups_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$FT_groups_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(FT_groups_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$FT_groups_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(FT_groups_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$FT_groups_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(FT_groups_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$FT_groups_4,d$wave, exclude = F))
# Religion
d <- d %>%
group_by(pid) %>%
fill(religion, .direction = "downup") %>%
ungroup()
addmargins(table(d$religion,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(Evangelical, .direction = "downup") %>%
ungroup()
addmargins(table(d$Evangelical,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(rel_importance, .direction = "downup") %>%
ungroup()
addmargins(table(d$rel_importance,d$wave, exclude = F))
# ZIP code
d <- d %>%
group_by(pid) %>%
fill(ZIPcode, .direction = "downup") %>%
ungroup()
addmargins(table(d$ZIPcode,d$wave, exclude = F))
# State
d <- d %>%
group_by(pid) %>%
fill(state, .direction = "downup") %>%
ungroup()
addmargins(table(d$state,d$wave, exclude = F))
d$state <- as.factor(d$state)
d$state[d$state == "invalid"] <- NA
d$state[d$state == "Invalid"] <- NA
d$state <- droplevels(d$state)
table(d$state, exclude = F)
# per_gop
d <- d %>%
group_by(pid) %>%
fill(per_gop, .direction = "downup") %>%
ungroup()
addmargins(table(d$per_gop,d$wave, exclude = F))
d$per_gop <- as.numeric(d$per_gop)
# per_dem
d <- d %>%
group_by(pid) %>%
fill(per_dem, .direction = "downup") %>%
ungroup()
addmargins(table(d$per_dem,d$wave, exclude = F))
d$per_dem <- as.numeric(d$per_dem)
# vote_MC
d <- d %>%
group_by(pid) %>%
fill(vote_MC_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$vote_MC_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(vote_MC_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$vote_MC_2,d$wave, exclude = F))
# Israel
table(d$israel)
d <- d %>%
group_by(pid) %>%
fill(israel, .direction = "downup") %>%
ungroup()
addmargins(table(d$israel,d$wave, exclude = F))
table(d$israel_usRole)
d <- d %>%
group_by(pid) %>%
fill(israel_usRole, .direction = "downup") %>%
ungroup()
addmargins(table(d$israel_usRole,d$wave, exclude = F))
d$israel_usRole <- as.numeric(as.character(d$israel_usRole))
# border
table(d$border_Harris)
d <- d %>%
group_by(pid) %>%
fill(border_Harris, .direction = "downup") %>%
ungroup()
addmargins(table(d$border_Harris,d$wave, exclude = F))
table(d$border_Trump)
d <- d %>%
group_by(pid) %>%
fill(border_Trump, .direction = "downup") %>%
ungroup()
addmargins(table(d$border_Trump,d$wave, exclude = F))
# polling
d <- d %>%
group_by(pid) %>%
fill(pollFreq, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollFreq,d$wave, exclude = F))
d$pollFreq <- dplyr::recode(d$pollFreq,
`1` = 7,
`2` = 6,
`3` = 5,
`4` = 4,
`5` = 3,
`6` = 2,
`7` = 1)
d[d$wave == 1 | d$wave ==2,] <- d[d$wave == 1 | d$wave ==2,] %>%
group_by(pid) %>%
fill(pollAcc, .direction = "downup") %>%
ungroup()
d[d$wave == 3 | d$wave == 4,] <- d[d$wave == 3 | d$wave == 4,] %>%
group_by(pid) %>%
fill(pollAcc, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollAcc,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollPred, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollPred,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollsource, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollsource,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollsource_7_TEXT, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollsource_7_TEXT,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollrel1, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollrel1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollbias1, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollbias1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollrel2, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollrel2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollbias2, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollbias2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollrel3, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollrel3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollbias3, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollbias3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollrel4, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollrel4,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(pollbias4, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollbias4,d$wave, exclude = F))
# NFC
d <- d %>%
group_by(pid) %>%
fill(NFC6_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(NFC6_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(NFC6_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(NFC6_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_4,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(NFC6_5, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_5,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(NFC6_6, .direction = "downup") %>%
ungroup()
addmargins(table(d$NFC6_6,d$wave, exclude = F))
d$NFC <- rowMeans(d[,c("NFC6_1", "NFC6_2", "NFC6_3", "NFC6_4", "NFC6_5", "NFC6_6")], na.rm = T)
# Executive order measures
d <- d %>%
group_by(pid) %>%
fill(EO_impact_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_4,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_4,d$wave, exclude = F))
# gov't process measures
d <- d %>%
group_by(pid) %>%
fill(govt_process, .direction = "downup") %>%
ungroup()
addmargins(table(d$govt_process,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(representation_Dem, .direction = "downup") %>%
ungroup()
addmargins(table(d$representation_Dem,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(representation_Rep, .direction = "downup") %>%
ungroup()
addmargins(table(d$representation_Rep,d$wave, exclude = F))
# ideology
d <- d %>%
group_by(pid) %>%
fill(ideology, .direction = "downup") %>%
ungroup()
addmargins(table(d$ideology,d$wave, exclude = F))
# EO valence & impact
d <- d %>%
group_by(pid) %>%
fill(EO_valence_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_valence_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_valence_4,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_1, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_1,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_2, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_2,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_3, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_3,d$wave, exclude = F))
d <- d %>%
group_by(pid) %>%
fill(EO_impact_4, .direction = "downup") %>%
ungroup()
addmargins(table(d$EO_impact_4,d$wave, exclude = F))
d$EO_valence <- rowMeans(d[,c("EO_valence_1","EO_valence_2","EO_valence_3","EO_valence_4")],na.rm = T)
d$EO_impact <- rowMeans(d[,c("EO_impact_1","EO_impact_2","EO_impact_3","EO_impact_4")],na.rm = T)repeated measures
# emotions
d$Neg_Emo <- rowMeans(d[,c("Emotions_1", "Emotions_2", "Emotions_5")], na.rm = T)
psych::alpha(d[,c("Emotions_1", "Emotions_2", "Emotions_5")])
cor.test(d$Emotions_1[d$wave == 1], d$Emotions_2[d$wave == 1])
cor.test(d$Emotions_1[d$wave == 2], d$Emotions_2[d$wave == 2])
psych::alpha(d[d$wave == 3,c("Emotions_1", "Emotions_2", "Emotions_5")])
psych::alpha(d[d$wave == 4,c("Emotions_1", "Emotions_2", "Emotions_5")])
d$Neg_Emo.c <- d$Neg_Emo - mean(d$Neg_Emo, na.rm = T)
d$Pos_Emo <- rowMeans(d[,c("Emotions_3", "Emotions_4")], na.rm = T)
cor.test(d$Emotions_3, d$Emotions_4)
cor.test(d$Emotions_3[d$wave == 1], d$Emotions_4[d$wave == 1])
cor.test(d$Emotions_3[d$wave == 2], d$Emotions_4[d$wave == 2])
cor.test(d$Emotions_3[d$wave == 3], d$Emotions_4[d$wave == 3])
cor.test(d$Emotions_3[d$wave == 4], d$Emotions_4[d$wave == 4])
d$Pos_Emo.c <- d$Pos_Emo - mean(d$Pos_Emo, na.rm = T)
# vote confidence
d$voteconfidence <- rowMeans(d[,c('voteconf_natl','voteconf_self')], na.rm = T)
cor.test(d$voteconf_natl, d$voteconf_self)
cor.test(d$voteconf_natl[d$wave == 1], d$voteconf_self[d$wave == 1])
cor.test(d$voteconf_natl[d$wave == 2], d$voteconf_self[d$wave == 2])
cor.test(d$voteconf_natl[d$wave == 3], d$voteconf_self[d$wave == 3])
cor.test(d$voteconf_natl[d$wave == 4], d$voteconf_self[d$wave == 4])
# party ID
## Creating continuous measure of party from branching ANES question
d$partyCont <- NA
d$partyCont[d$demStrength == 1] <- -3
d$partyCont[d$demStrength == 2] <- -2
d$partyCont[d$partyClose == 1] <- -1
d$partyCont[d$partyClose == 3] <- 0
d$partyCont[d$partyClose == 2] <- 1
d$partyCont[d$repStrength == 2] <- 2
d$partyCont[d$repStrength == 1] <- 3
## Creating factor measure of party from continuous measure
d$party_factor <- NA
d$party_factor[d$partyCont == -3 | d$partyCont == -2 | d$partyCont == -1] <- "Democrat"
d$party_factor[d$partyCont == 3 | d$partyCont == 2 | d$partyCont == 1] <- "Republican"
d$party_factor[d$partyCont == 0] <- "Independent"
table(d$party_factor)
# affective polarization
## Parties
d$FT_Outgroup <- ifelse(d$party_factor == "Democrat", d$FT_parties_2,
ifelse(d$party_factor == "Republican", d$FT_parties_1, NA))
d$FT_diff <- NA
d$FT_diff <- d$FT_parties_1 - d$FT_parties_2
d$affPol <- ifelse(d$party_factor == "Democrat", d$FT_parties_1 - d$FT_parties_2,
ifelse(d$party_factor == "Republican", d$FT_parties_2 - d$FT_parties_1,
ifelse(d$party_factor == "Independent", NA, NA)))
## Politicians
d$FT_Outcandidate <- ifelse(d$party_factor == "Democrat", d$FT_pols_2,
ifelse(d$party_factor == "Republican", d$FT_pols_1, NA))
d$FT_CandDiff <- NA
d$FT_CandDiff <- d$FT_pols_1 - d$FT_pols_2
describeBy(d$FT_CandDiff, d$party_factor)
d$affPol_cand <- ifelse(d$party_factor == "Democrat", d$FT_pols_1 - d$FT_pols_2,
ifelse(d$party_factor == "Republican", d$FT_pols_2 - d$FT_pols_1,
ifelse(d$party_factor == "Independent", NA, NA)))
# connectedness
d$connectedness_6 <- as.numeric(d$connectedness_6)
d$connectedness <- rowMeans(d[,c("connectedness_1","connectedness_2","connectedness_3","connectedness_4","connectedness_5","connectedness_6","connectedness_7","connectedness_8")], na.rm = T)
psych::alpha(d[ , c("connectedness_1","connectedness_2","connectedness_3","connectedness_4","connectedness_5","connectedness_6","connectedness_7","connectedness_8")])
# demNorms
d$antidemNorms <- rowMeans(d[,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4", "demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")], na.rm = T)
psych::alpha(d[,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[d$wave == 1,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[d$wave == 2,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[d$wave == 3,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[d$wave == 4,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
psych::alpha(d[d$wave == 1,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
psych::alpha(d[d$wave == 2,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
psych::alpha(d[d$wave == 3,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
psych::alpha(d[d$wave == 4,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
d$demNorms <- (d$antidemNorms*-1)
psych::describe(d$demNorms)
psych::describe(d$antidemNorms)
# anti-Asian animus
d$AAA <- rowMeans(d[c("AAA_1", "AAA_2", "AAA_3")], na.rm = T)
psych::alpha(d[,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
# gov't trust
cor.test(d$trustGovt1, d$trustGovt2) # yeah
cor.test(d$trustGovt1[d$wave == 1], d$trustGovt2[d$wave == 1])
cor.test(d$trustGovt1[d$wave == 2], d$trustGovt2[d$wave == 2])
cor.test(d$trustGovt1[d$wave == 3], d$trustGovt2[d$wave == 3])
cor.test(d$trustGovt1[d$wave == 4], d$trustGovt2[d$wave == 4])
d$trustGovt <- rowMeans(d[c("trustGovt1", "trustGovt2")], na.rm = T)
# science trust
d$trustSci <- rowMeans(d[c("trustSci_1", "trustSci_2","trustSci_3","trustSci_4")], na.rm = T)
psych::alpha(d[c("trustSci_1", "trustSci_2","trustSci_3","trustSci_4")])
# Vote Choice
table(d$voteChoice_final, d$wave)
d$voteID <- NA
d$voteID[d$voteChoice_final == 1] <- "Trump"
d$voteID[d$voteChoice_final == 2] <- "Harris"
d$voteID[d$VoteChoice == 1] <- "Trump"
d$voteID[d$VoteChoice == 2] <- "Harris"
d$voteID[d$VoteLean == 1] <- "Trump"
d$voteID[d$VoteLean == 2] <- "Harris"
d$voteID[d$VoteLean == 3] <- "Other"
d$voteID[d$voteChoice_final == 4] <- "Did Not Vote"
d$voteID[d$voteChoice_final == 3] <- "Other" # Hmmm
table(d$voteID, exclude = F)
# counter-proj
## ID
d$proj_ID <- NA
d$proj_ID[d$voteID == "Harris"] <- "Left"
d$proj_ID[d$voteID == "Trump"] <- "Right"
d$proj_ID[d$voteID == "Other" & d$party_factor == "Democrat"] <- "Left"
d$proj_ID[d$voteID == "Other" & d$party_factor == "Republican"] <- "Right"
## affective polarization
d$affPol_proj_Harris <- d$FT_proj_Harris_2 - d$FT_proj_Harris_1
d$affPol_proj_Trump <- d$FT_proj_Trump_1 - d$FT_proj_Trump_2
d$affPol_proj_Dem <- d$FT_proj_Dem_2 - d$FT_proj_Dem_1
d$affPol_proj_Rep <- d$FT_proj_Rep_1 - d$FT_proj_Rep_2
## dem norms
d$demNorms_proj_harris <- rowMeans(d[,c("demNorms_proj_harris_1", "demNorms_proj_harris_2", "demNorms_proj_harris_3", "demNorms_proj_harris_4")], na.rm = T)
d$demNorms_proj_Trump <- rowMeans(d[,c("demNorms_proj_Trump_1", "demNorms_proj_Trump_2", "demNorms_proj_Trump_3", "demNorms_proj_Trump_4")], na.rm = T)
d$demNorms_proj_Dem <- rowMeans(d[,c("demNorms_proj_Dem_1", "demNorms_proj_Dem_2", "demNorms_proj_Dem_3", "demNorms_proj_Dem_4")], na.rm = T)
d$demNorms_proj_Rep <- rowMeans(d[,c("demNorms_proj_Rep_1", "demNorms_proj_Rep_2", "demNorms_proj_Rep_3", "demNorms_proj_Rep_4")], na.rm = T)
# Affective Polarization
#d$affPol_proj <- ifelse(d$voteID == "Harris", d$affPol_proj_Harris,
# ifelse(d$voteID == "Trump", d$affPol_proj_Trump,
# ifelse(d$voteID == "Other" & d$party_factor == "Democrat", #d$affPol_proj_Dem,
# ifelse(d$voteID == "Other" & d$party_factor == "Republican", #d$affPol_proj_Rep, NA))))
d$affPol_proj <- rowMeans(d[,c("affPol_proj_Harris","affPol_proj_Trump","affPol_proj_Dem","affPol_proj_Rep")], na.rm = T)
describeBy(d$affPol_proj, list(d$proj_ID, d$wave))
## Negative Polarization
d$negPol_proj <- ifelse(d$voteID == "Harris", d$FT_proj_Harris_1,
ifelse(d$voteID == "Trump", d$FT_proj_Trump_2,
ifelse(d$voteID == "Other" & d$party_factor == "Democrat", d$FT_proj_Dem_1,
ifelse(d$voteID == "Other" & d$party_factor == "Republican", d$FT_proj_Rep_2, NA))))
## Democratic Norms
d$demNorms_proj <- ifelse(d$voteID == "Harris", d$demNorms_proj_harris,
ifelse(d$voteID == "Trump", d$demNorms_proj_Trump,
ifelse(d$voteID == "Other" & d$party_factor == "Democrat", d$demNorms_proj_Dem,
ifelse(d$voteID == "Other" & d$party_factor == "Republican", d$demNorms_proj_Rep, NA))))
# climate attitudes
d$climBel_proj <- ifelse(d$voteID == "Harris", d$climBel_proj_Harris,
ifelse(d$voteID == "Trump", d$climbel_proj_Trump,
ifelse(d$voteID == "Other" & d$party_factor == "Democrat", d$climBel_proj_Dem,
ifelse(d$voteID == "Other" & d$party_factor == "Republican", d$climBel_proj_Rep, NA))))
# ID centrality variables
## ID formation 1
d$IDform_1 <- rowMeans(d[, c("IDform_Dem_1", "IDform_Rep_1", "IDform_ind_1", "IDform_lib_1", "IDform_leftist_1", "IDform_prog_1", "IDform_con_1", "IDform_right_1", "IDform_mod_1", "IDform_socialist_1", "IDform_natlist_1", "IDform_libertarian_1", "IDform_feminist_1", "IDform_prolife_1", "IDform_prochoice_1", "IDform_MAGA_1", "IDform_enviro_1", "IDform_antiTrump_1", "IDform_pop_1", "IDform_other_1")], na.rm = T)
## ID formation 2
d$IDform_2 <- rowMeans(d[, c("IDform_Dem_2", "IDform_Rep_2", "IDform_ind_2", "IDform_lib_2", "IDform_leftist_2", "IDform_prog_2", "IDform_con_2", "IDform_right_2", "IDform_mod_2", "IDform_socialist_2", "IDform_natlist_2", "IDform_libertarian_2", "IDform_feminist_2", "IDform_prolife_2", "IDform_prochoice_2", "IDform_MAGA_2", "IDform_enviro_2", "IDform_antiTrump_2", "IDform_pop_2", "IDform_other_2")], na.rm = T)
## ID formation 3
d$IDform_3 <- rowMeans(d[, c("IDform_Dem_3", "IDform_Rep_3", "IDform_ind_3", "IDform_lib_3", "IDform_leftist_3", "IDform_prog_3", "IDform_con_3", "IDform_right_3", "IDform_mod_3", "IDform_socialist_3", "IDform_natlist_3", "IDform_libertarian_3", "IDform_feminist_3", "IDform_prolife_3", "IDform_prochoice_3", "IDform_MAGA_3", "IDform_enviro_3", "IDform_antiTrump_3", "IDform_pop_3", "IDform_other_3")], na.rm = T)
## ID formation 4
d$IDform_4 <- rowMeans(d[, c("IDform_Dem_4", "IDform_Rep_4", "IDform_ind_4", "IDform_lib_4", "IDform_leftist_4", "IDform_prog_4", "IDform_con_4", "IDform_right_4", "IDform_mod_4", "IDform_socialist_4", "IDform_natlist_4", "IDform_libertarian_4", "IDform_feminist_4", "IDform_prolife_4", "IDform_prochoice_4", "IDform_MAGA_4", "IDform_enviro_4", "IDform_antiTrump_4", "IDform_pop_4", "IDform_other_4")], na.rm = T)
## ID formation 5
d$IDform_5 <- rowMeans(d[, c("IDform_Dem_5", "IDform_Rep_5", "IDform_ind_5", "IDform_lib_5", "IDform_leftist_5", "IDform_prog_5", "IDform_con_5", "IDform_right_5", "IDform_mod_5", "IDform_socialist_5", "IDform_natlist_5", "IDform_libertarian_5", "IDform_feminist_5", "IDform_prolife_5", "IDform_prochoice_5", "IDform_MAGA_5", "IDform_enviro_5", "IDform_antiTrump_5", "IDform_pop_5", "IDform_other_5")], na.rm = T)
## ID formation 6
d$IDform_6 <- rowMeans(d[, c("IDform_Dem_6", "IDform_Rep_6", "IDform_ind_6", "IDform_lib_6", "IDform_leftist_6", "IDform_prog_6", "IDform_con_6", "IDform_right_6", "IDform_mod_6", "IDform_socialist_6", "IDform_natlist_6", "IDform_libertarian_6", "IDform_feminist_6", "IDform_prolife_6", "IDform_prochoice_6", "IDform_MAGA_6", "IDform_enviro_6", "IDform_antiTrump_6", "IDform_pop_6", "IDform_other_6")], na.rm = T)
## composite identity centrality
d$IDform <- rowMeans(d[,c("IDform_1", "IDform_2", "IDform_3", "IDform_4", "IDform_5", "IDform_6")], na.rm = T)
psych::alpha(d[,c("IDform_1", "IDform_2", "IDform_3", "IDform_4", "IDform_5", "IDform_6")])
# Identity Variables
d$numID_selected <- NA
d$numID_selected <- rowSums(d[,c("polID_Liberal_select", "polID_Leftist_select", "polID_Progressive_select", "polID_Conservative_select", "polID_RightWing_select", "polID_Moderate_select", "polID_Socialist_select", "polID_Nationalist_select", "polID_Libertarian_select", "polID_Feminist_select", "polID_Prolife_select", "polID_Prochoice_select", "polID_MAGA_select", "polID_AntiTrump_select", "polID_Environmentalist_select", "polID_Populist_select", "polID_Other_select")],na.rm = T)
table(d$numID_selected, d$wave)
## Calculate & join ID sds for each participant
ID_sds <- d %>%
dplyr::group_by(pid) %>%
dplyr::summarize(sdID_selected = sd(numID_selected, na.rm = TRUE))
d <- d %>%
left_join(ID_sds, by = "pid")Results
Figures:
Figures are typically sized at 90mm (single column) or 180mm (double column), with a maximum height of 170mm, and font sizes should be 5-7pt at this size
Prep: Analytic codes & centering
### partisan identity vars
# Contrast Codes
## dems vs. reps
d$pDem_Rep <- NA
d$pDem_Rep[d$party_factor == "Democrat"] <- -1/2
d$pDem_Rep[d$party_factor == "Independent"] <- 0
d$pDem_Rep[d$party_factor == "Republican"] <- 1/2
## partisans vs. inds
d$pParty_Ind <- NA
d$pParty_Ind[d$party_factor == "Democrat"] <- -1/3
d$pParty_Ind[d$party_factor == "Independent"] <- 2/3
d$pParty_Ind[d$party_factor == "Republican"] <- -1/3
# Dummy Codes
## Democrats
### dems vs. R
d$pDem_R <- NA
d$pDem_R[d$party_factor == "Democrat"] <- 0
d$pDem_R[d$party_factor == "Independent"] <- 0
d$pDem_R[d$party_factor == "Republican"] <- 1
### dems vs. I
d$pDem_I <- NA
d$pDem_I[d$party_factor == "Democrat"] <- 0
d$pDem_I[d$party_factor == "Independent"] <- 1
d$pDem_I[d$party_factor == "Republican"] <- 0
## Republicans
### reps vs. D
d$pRep_D <- NA
d$pRep_D[d$party_factor == "Democrat"] <- 1
d$pRep_D[d$party_factor == "Independent"] <- 0
d$pRep_D[d$party_factor == "Republican"] <- 0
### reps vs. I
d$pRep_I <- NA
d$pRep_I[d$party_factor == "Democrat"] <- 0
d$pRep_I[d$party_factor == "Independent"] <- 1
d$pRep_I[d$party_factor == "Republican"] <- 0
## Independents
### inds vs. D
d$pInd_D <- NA
d$pInd_D[d$party_factor == "Democrat"] <- 1
d$pInd_D[d$party_factor == "Independent"] <- 0
d$pInd_D[d$party_factor == "Republican"] <- 0
### inds vs. R
d$pInd_R <- NA
d$pInd_R[d$party_factor == "Democrat"] <- 0
d$pInd_R[d$party_factor == "Independent"] <- 0
d$pInd_R[d$party_factor == "Republican"] <- 1
# party switch
d$party_switch.d <- ifelse(d$party_switch == "TRUE", 1, 0)
### wave
d$wave <- as.factor(d$wave)
# contrast codes
d$wave.lin <- NA
d$wave.lin[d$wave == 1] <- -1/2
d$wave.lin[d$wave == 2] <- -1/4
d$wave.lin[d$wave == 3] <- 1/4
d$wave.lin[d$wave == 4] <- 1/2
d$wave.quad <- NA
d$wave.quad[d$wave == 1] <- -1/2
d$wave.quad[d$wave == 2] <- 1/2
d$wave.quad[d$wave == 3] <- 1/2
d$wave.quad[d$wave == 4] <- -1/2
d$wave.cub <- NA
d$wave.cub[d$wave == 1] <- 1/4
d$wave.cub[d$wave == 2] <- -1/2
d$wave.cub[d$wave == 3] <- 1/2
d$wave.cub[d$wave == 4] <- -1/4
# dummy codes
## wave 1
d$wave1_2 <- NA
d$wave1_2[d$wave == 1] <- 0
d$wave1_2[d$wave == 2] <- 1
d$wave1_2[d$wave == 3] <- 0
d$wave1_2[d$wave == 4] <- 0
d$wave1_3 <- NA
d$wave1_3[d$wave == 1] <- 0
d$wave1_3[d$wave == 2] <- 0
d$wave1_3[d$wave == 3] <- 1
d$wave1_3[d$wave == 4] <- 0
d$wave1_4 <- NA
d$wave1_4[d$wave == 1] <- 0
d$wave1_4[d$wave == 2] <- 0
d$wave1_4[d$wave == 3] <- 0
d$wave1_4[d$wave == 4] <- 1
## wave 2
d$wave2_1 <- NA
d$wave2_1[d$wave == 1] <- 1
d$wave2_1[d$wave == 2] <- 0
d$wave2_1[d$wave == 3] <- 0
d$wave2_1[d$wave == 4] <- 0
d$wave2_3 <- NA
d$wave2_3[d$wave == 1] <- 0
d$wave2_3[d$wave == 2] <- 0
d$wave2_3[d$wave == 3] <- 1
d$wave2_3[d$wave == 4] <- 0
d$wave2_4 <- NA
d$wave2_4[d$wave == 1] <- 0
d$wave2_4[d$wave == 2] <- 0
d$wave2_4[d$wave == 3] <- 0
d$wave2_4[d$wave == 4] <- 1
## wave 3
d$wave3_1 <- NA
d$wave3_1[d$wave == 1] <- 1
d$wave3_1[d$wave == 2] <- 0
d$wave3_1[d$wave == 3] <- 0
d$wave3_1[d$wave == 4] <- 0
d$wave3_2 <- NA
d$wave3_2[d$wave == 1] <- 0
d$wave3_2[d$wave == 2] <- 1
d$wave3_2[d$wave == 3] <- 0
d$wave3_2[d$wave == 4] <- 0
d$wave3_4 <- NA
d$wave3_4[d$wave == 1] <- 0
d$wave3_4[d$wave == 2] <- 0
d$wave3_4[d$wave == 3] <- 0
d$wave3_4[d$wave == 4] <- 1
## wave 4
d$wave4_1 <- NA
d$wave4_1[d$wave == 1] <- 1
d$wave4_1[d$wave == 2] <- 0
d$wave4_1[d$wave == 3] <- 0
d$wave4_1[d$wave == 4] <- 0
d$wave4_2 <- NA
d$wave4_2[d$wave == 1] <- 0
d$wave4_2[d$wave == 2] <- 1
d$wave4_2[d$wave == 3] <- 0
d$wave4_2[d$wave == 4] <- 0
d$wave4_3 <- NA
d$wave4_3[d$wave == 1] <- 0
d$wave4_3[d$wave == 2] <- 0
d$wave4_3[d$wave == 3] <- 1
d$wave4_3[d$wave == 4] <- 0
# Polling Variable
d$pollFreq.c <- d$pollFreq - mean(d$pollFreq, na.rm = T)
# Race
d$vs_race <- as.factor(as.character(d$vs_race))
d$vs_race <- relevel(d$vs_race, ref = "White")
# Gender
d$vs_gender <- as.factor(as.character(d$vs_gender))
d$male_female <- NA
d$male_female[d$vs_gender == "Male"] <- -1/2
d$male_female[d$vs_gender == "Female"] <- 1/2
d$male_female[d$vs_gender == "Other"] <- 0
d$nonbinary_mf <- NA
d$nonbinary_mf[d$vs_gender == "Male"] <- 1/3
d$nonbinary_mf[d$vs_gender == "Female"] <- 1/3
d$nonbinary_mf[d$vs_gender == "Other"] <- -2/3
# generic conspiracist beliefs
d$GCB.c <- d$GCB - mean(d$GCB, na.rm = T)
# affPol (scaled)
d$affPol.100 <- d$affPol/100
# poll reliability & poll ideology bias
## averaging across all media sources
d$pollrel <- rowMeans(d[,c("pollrel1", "pollrel2", "pollrel3", "pollrel4")], na.rm = T)
d$pollbias <- rowMeans(d[,c("pollbias1", "pollbias2", "pollbias3", "pollbias4")], na.rm = T)
## scaled variables
d$pollrel.z <- scale(d$pollrel)
d$pollbias.z <- 2 * (d$pollbias - min(d$pollbias, na.rm = TRUE)) / (max(d$pollbias, na.rm = TRUE) - min(d$pollbias, na.rm = TRUE)) - 1
## magnitude and tilt separate
d$pollbias_abs <- abs(d$pollbias)
d$pollbias_tilt <- ifelse(d$pollbias > 0, "conservative",
ifelse(d$pollbias < 0, "liberal", NA))
table(d$pollbias_tilt)##
## conservative liberal
## 2014 37641. Perceived Election Legitimacy is foundational to Democratic Values (Dem Norms, Aff Pol, Trust in Govt)
Democratic values were operationalized using measures of trust in the federal government, support for democratic norms, and affective polarization. Support for democratic norms and affective polarization are measured with respect to the opposing political party, so analyses for these variables exclude Independents.
Perceived election legitimacy was measured with two questions:
How confident are you that your own vote was counted as you intended? (
voteconf_self)How confident are you that votes nationwide were counted as voters intended? (
voteconf_natl)
And averaged together for the main analysis
(voteconfidence).
a. DV: Trust in Govt
wave_label <- c(`1` = "Wave 1", `2` = "Wave 2", `3` = "Wave 3", `4` = "Wave 4")
ggplot(d[!is.na(d$party_factor),]) +
geom_jitter(aes(x = voteconfidence,
y = trustGovt,
fill = party_factor,
color = party_factor),
alpha = .2, size = .2,
height = .2, width = .2) +
geom_smooth(aes(x = voteconfidence,
y = trustGovt,
fill = party_factor,
color = party_factor),
method = "lm", alpha = .4, se = F) +
geom_smooth(aes(x = voteconfidence,
y = trustGovt),
method = "lm", color = "black") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perceived Election Legitimacy",
y = "Trust in Federal Government") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_y_continuous(breaks = seq(-2,2,.5)) +
theme_bw()
(DVPEL1 <- ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = voteconfidence,
y = trustGovt,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = element_blank(),
y = "Trust in Federal Government") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_y_continuous(breaks = seq(-2,1,.5)) +
coord_cartesian(ylim = c(-2,1)) +
theme_bw())
Summary
Perceived election legitimacy (PEL) positively predicts trust in the federal government (b = 0.18, p < .001), with no variation in strength by partisan identity (p = .68 and p = .12) collapsing across time. Over the course of the study, the predictive strength of perceived election legitimacy decreased (b = -0.06, p = .006), with this decline primarily occurring from wave 1 to wave 2, when explanatory power decreased significantly (b = -0.09, p < .001). Differences from wave 2 to wave 3 (p = .12) and wave 4 (p = .92) were not significant. There was partisan variation in this trend over time, such that Democrats and Republicans differed in degree to which the perceived election legitimacy-governmental trust relationship decreased in magnitude (b = -0.13, p = .002). However, perceived election legitimacy still positively predicted trust in the federal government for Democrats (b = 0.15, p < .001) and Republicans (b = 0.24, p < .001), and marginally for Independents (b = 0.08, p = .092), in the final wave of the study.
Models
Main model
TrustPel.m1 <- lmer(trustGovt ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(TrustPel.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.50 | -1.68 – -1.32 | -16.11 | <0.001 | 3061.31 |
| pDem Rep | -0.20 | -0.36 – -0.04 | -2.40 | 0.017 | 7942.57 |
| pParty Ind | -0.04 | -0.23 – 0.14 | -0.46 | 0.646 | 7942.60 |
| wave lin | -0.35 | -0.49 – -0.20 | -4.81 | <0.001 | 5996.13 |
| wave quad | 0.12 | 0.01 – 0.23 | 2.23 | 0.026 | 5755.89 |
| wave cub | -0.26 | -0.40 – -0.13 | -3.80 | <0.001 | 5653.59 |
| voteconfidence | 0.18 | 0.16 – 0.21 | 16.15 | <0.001 | 7927.20 |
| vs race [Black] | 0.22 | 0.12 – 0.32 | 4.19 | <0.001 | 2587.58 |
| vs race [Hispanic] | 0.23 | 0.13 – 0.34 | 4.29 | <0.001 | 2531.51 |
| vs race [Other] | 0.07 | -0.05 – 0.20 | 1.15 | 0.248 | 2448.53 |
| vs age | -0.00 | -0.00 – 0.00 | -0.48 | 0.632 | 2500.76 |
| male female | 0.04 | -0.03 – 0.10 | 1.08 | 0.282 | 2518.40 |
| nonbinary mf | 0.66 | 0.27 – 1.05 | 3.30 | 0.001 | 2585.11 |
| pDem Rep × wave lin | 0.92 | 0.62 – 1.22 | 5.98 | <0.001 | 6086.59 |
| pDem Rep × wave quad | -0.34 | -0.57 – -0.11 | -2.89 | 0.004 | 5844.53 |
| pDem Rep × wave cub | 0.04 | -0.24 – 0.32 | 0.29 | 0.771 | 5704.05 |
| pParty Ind × wave lin | -0.01 | -0.34 – 0.33 | -0.04 | 0.971 | 6011.76 |
| pParty Ind × wave quad | -0.23 | -0.50 – 0.03 | -1.77 | 0.077 | 5772.46 |
| pParty Ind × wave cub | -0.19 | -0.52 – 0.14 | -1.14 | 0.256 | 5686.69 |
| pDem Rep × voteconfidence | 0.01 | -0.03 – 0.05 | 0.42 | 0.677 | 7819.55 |
|
pParty Ind × voteconfidence |
-0.04 | -0.10 – 0.01 | -1.54 | 0.124 | 7878.74 |
| wave lin × voteconfidence | -0.06 | -0.10 – -0.02 | -2.74 | 0.006 | 5985.60 |
|
wave quad × voteconfidence |
-0.03 | -0.06 – 0.00 | -1.87 | 0.062 | 5758.36 |
| wave cub × voteconfidence | 0.06 | 0.02 – 0.10 | 3.17 | 0.002 | 5672.24 |
|
(pDem Rep × wave lin) × voteconfidence |
0.13 | 0.05 – 0.21 | 3.16 | 0.002 | 6044.90 |
|
(pDem Rep × wave quad) × voteconfidence |
-0.06 | -0.12 – 0.01 | -1.75 | 0.080 | 5836.34 |
|
(pDem Rep × wave cub) × voteconfidence |
0.04 | -0.04 – 0.11 | 0.94 | 0.350 | 5717.40 |
|
(pParty Ind × wave lin) × voteconfidence |
-0.04 | -0.14 – 0.06 | -0.72 | 0.471 | 5994.87 |
|
(pParty Ind × wave quad) × voteconfidence |
0.07 | -0.01 – 0.15 | 1.83 | 0.067 | 5776.50 |
|
(pParty Ind × wave cub) × voteconfidence |
0.06 | -0.03 – 0.16 | 1.29 | 0.198 | 5695.19 |
| Random Effects | |||||
| σ2 | 0.40 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.57 | ||||
| N pid | 2592 | ||||
| Observations | 7975 | ||||
| Marginal R2 / Conditional R2 | 0.202 / 0.653 | ||||
Main model centered at Wave 2
TrustPel.m1.w2 <- lmer(trustGovt ~ (pDem_Rep + pParty_Ind) * (wave2_1 + wave2_3 + wave2_4) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(TrustPel.m1.w2, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.22 | -1.42 – -1.02 | -11.86 | <0.001 | 4272.23 |
| pDem Rep | -0.61 | -0.85 – -0.37 | -5.00 | <0.001 | 7158.73 |
| pParty Ind | -0.06 | -0.35 – 0.22 | -0.44 | 0.662 | 7265.19 |
| wave2 1 | -0.23 | -0.37 – -0.10 | -3.42 | 0.001 | 5985.61 |
| wave2 3 | -0.44 | -0.59 – -0.28 | -5.58 | <0.001 | 5679.64 |
| wave2 4 | -0.45 | -0.61 – -0.28 | -5.36 | <0.001 | 5732.22 |
| voteconfidence | 0.15 | 0.12 – 0.18 | 9.00 | <0.001 | 7077.11 |
| vs race [Black] | 0.22 | 0.12 – 0.32 | 4.19 | <0.001 | 2587.58 |
| vs race [Hispanic] | 0.23 | 0.13 – 0.34 | 4.29 | <0.001 | 2531.51 |
| vs race [Other] | 0.07 | -0.05 – 0.20 | 1.15 | 0.248 | 2448.53 |
| vs age | -0.00 | -0.00 – 0.00 | -0.48 | 0.632 | 2500.76 |
| male female | 0.04 | -0.03 – 0.10 | 1.08 | 0.282 | 2518.40 |
| nonbinary mf | 0.66 | 0.27 – 1.05 | 3.30 | 0.001 | 2585.11 |
| pDem Rep × wave2 1 | 0.14 | -0.15 – 0.43 | 0.95 | 0.340 | 6144.00 |
| pDem Rep × wave2 3 | 0.50 | 0.19 – 0.81 | 3.14 | 0.002 | 5681.36 |
| pDem Rep × wave2 4 | 1.04 | 0.69 – 1.38 | 5.84 | <0.001 | 5763.27 |
| pParty Ind × wave2 1 | 0.09 | -0.23 – 0.41 | 0.56 | 0.572 | 5987.40 |
| pParty Ind × wave2 3 | -0.19 | -0.57 – 0.18 | -1.02 | 0.309 | 5736.25 |
| pParty Ind × wave2 4 | 0.18 | -0.21 – 0.58 | 0.91 | 0.363 | 5769.88 |
| pDem Rep × voteconfidence | -0.07 | -0.13 – -0.01 | -2.20 | 0.028 | 6845.48 |
|
pParty Ind × voteconfidence |
-0.03 | -0.11 – 0.05 | -0.68 | 0.499 | 6978.74 |
| wave2 1 × voteconfidence | 0.09 | 0.05 – 0.13 | 4.54 | <0.001 | 6000.48 |
| wave2 3 × voteconfidence | 0.03 | -0.01 – 0.08 | 1.54 | 0.123 | 5676.57 |
| wave2 4 × voteconfidence | 0.00 | -0.04 – 0.05 | 0.10 | 0.923 | 5725.28 |
|
(pDem Rep × wave2 1) × voteconfidence |
0.05 | -0.03 – 0.13 | 1.23 | 0.218 | 6118.44 |
|
(pDem Rep × wave2 3) × voteconfidence |
0.10 | 0.02 – 0.18 | 2.41 | 0.016 | 5688.79 |
|
(pDem Rep × wave2 4) × voteconfidence |
0.16 | 0.07 – 0.26 | 3.46 | 0.001 | 5757.37 |
|
(pParty Ind × wave2 1) × voteconfidence |
-0.02 | -0.11 – 0.08 | -0.31 | 0.757 | 6002.23 |
|
(pParty Ind × wave2 3) × voteconfidence |
0.05 | -0.06 – 0.15 | 0.81 | 0.416 | 5715.45 |
|
(pParty Ind × wave2 4) × voteconfidence |
-0.09 | -0.20 – 0.03 | -1.43 | 0.154 | 5749.32 |
| Random Effects | |||||
| σ2 | 0.40 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.57 | ||||
| N pid | 2592 | ||||
| Observations | 7975 | ||||
| Marginal R2 / Conditional R2 | 0.202 / 0.653 | ||||
Model centered at Wave 4, party ID-specific effects
TrustPel.m1.w4.d <- lmer(trustGovt ~ (pDem_R + pDem_I) * (wave4_1 + wave4_2+ wave4_3) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(TrustPel.m1.w4.d, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.92 | -2.16 – -1.68 | -15.59 | <0.001 | 6451.12 |
| pDem R | 0.42 | 0.12 – 0.72 | 2.78 | 0.005 | 6631.69 |
| pDem I | 0.33 | -0.01 – 0.67 | 1.88 | 0.060 | 6659.08 |
| wave4 1 | 0.69 | 0.47 – 0.92 | 6.08 | <0.001 | 6253.85 |
| wave4 2 | 1.03 | 0.82 – 1.23 | 9.76 | <0.001 | 5738.32 |
| wave4 3 | 0.41 | 0.19 – 0.62 | 3.76 | <0.001 | 5635.34 |
| voteconfidence | 0.15 | 0.10 – 0.19 | 5.89 | <0.001 | 6469.47 |
| vs race [Black] | 0.22 | 0.12 – 0.32 | 4.19 | <0.001 | 2587.58 |
| vs race [Hispanic] | 0.23 | 0.13 – 0.34 | 4.29 | <0.001 | 2531.51 |
| vs race [Other] | 0.07 | -0.05 – 0.20 | 1.15 | 0.248 | 2448.53 |
| vs age | -0.00 | -0.00 – 0.00 | -0.48 | 0.632 | 2500.76 |
| male female | 0.04 | -0.03 – 0.10 | 1.08 | 0.282 | 2518.40 |
| nonbinary mf | 0.66 | 0.27 – 1.05 | 3.30 | 0.001 | 2585.11 |
| pDem R × wave4 1 | -0.90 | -1.23 – -0.56 | -5.20 | <0.001 | 6102.44 |
| pDem R × wave4 2 | -1.04 | -1.38 – -0.69 | -5.84 | <0.001 | 5763.27 |
| pDem R × wave4 3 | -0.53 | -0.89 – -0.18 | -2.96 | 0.003 | 5634.26 |
| pDem I × wave4 1 | -0.54 | -0.94 – -0.14 | -2.65 | 0.008 | 6038.10 |
| pDem I × wave4 2 | -0.70 | -1.11 – -0.29 | -3.36 | 0.001 | 5763.67 |
| pDem I × wave4 3 | -0.64 | -1.07 – -0.22 | -2.97 | 0.003 | 5618.08 |
| pDem R × voteconfidence | 0.09 | 0.01 – 0.17 | 2.32 | 0.021 | 6361.49 |
| pDem I × voteconfidence | -0.07 | -0.17 – 0.04 | -1.27 | 0.203 | 6469.22 |
| wave4 1 × voteconfidence | 0.12 | 0.06 – 0.18 | 4.08 | <0.001 | 6157.56 |
| wave4 2 × voteconfidence | 0.05 | -0.01 – 0.11 | 1.77 | 0.076 | 5728.74 |
| wave4 3 × voteconfidence | 0.02 | -0.04 – 0.08 | 0.64 | 0.521 | 5632.50 |
|
(pDem R × wave4 1) × voteconfidence |
-0.11 | -0.21 – -0.02 | -2.41 | 0.016 | 6059.96 |
|
(pDem R × wave4 2) × voteconfidence |
-0.16 | -0.26 – -0.07 | -3.46 | 0.001 | 5757.37 |
|
(pDem R × wave4 3) × voteconfidence |
-0.06 | -0.16 – 0.03 | -1.26 | 0.207 | 5639.66 |
|
(pDem I × wave4 1) × voteconfidence |
0.01 | -0.11 – 0.13 | 0.21 | 0.835 | 6005.22 |
|
(pDem I × wave4 2) × voteconfidence |
0.00 | -0.12 – 0.13 | 0.06 | 0.953 | 5744.42 |
|
(pDem I × wave4 3) × voteconfidence |
0.10 | -0.03 – 0.23 | 1.56 | 0.120 | 5620.69 |
| Random Effects | |||||
| σ2 | 0.40 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.57 | ||||
| N pid | 2592 | ||||
| Observations | 7975 | ||||
| Marginal R2 / Conditional R2 | 0.202 / 0.653 | ||||
TrustPel.m1.w4.i <- lmer(trustGovt ~ (pInd_R + pInd_D) * (wave4_1 + wave4_2+ wave4_3) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(TrustPel.m1.w4.i, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.59 | -1.93 – -1.25 | -9.20 | <0.001 | 7935.81 |
| pInd R | 0.09 | -0.29 – 0.47 | 0.47 | 0.638 | 6617.54 |
| pInd D | -0.33 | -0.67 – 0.01 | -1.88 | 0.060 | 6659.08 |
| wave4 1 | 0.15 | -0.17 – 0.48 | 0.92 | 0.358 | 5929.39 |
| wave4 2 | 0.33 | -0.02 – 0.68 | 1.82 | 0.069 | 5757.75 |
| wave4 3 | -0.24 | -0.61 – 0.13 | -1.28 | 0.201 | 5593.93 |
| voteconfidence | 0.08 | -0.01 – 0.17 | 1.69 | 0.092 | 6514.82 |
| vs race [Black] | 0.22 | 0.12 – 0.32 | 4.19 | <0.001 | 2587.58 |
| vs race [Hispanic] | 0.23 | 0.13 – 0.34 | 4.29 | <0.001 | 2531.51 |
| vs race [Other] | 0.07 | -0.05 – 0.20 | 1.15 | 0.248 | 2448.53 |
| vs age | -0.00 | -0.00 – 0.00 | -0.48 | 0.632 | 2500.76 |
| male female | 0.04 | -0.03 – 0.10 | 1.08 | 0.282 | 2518.40 |
| nonbinary mf | 0.66 | 0.27 – 1.05 | 3.30 | 0.001 | 2585.11 |
| pInd R × wave4 1 | -0.36 | -0.77 – 0.06 | -1.69 | 0.091 | 5952.22 |
| pInd R × wave4 2 | -0.34 | -0.78 – 0.11 | -1.46 | 0.144 | 5773.03 |
| pInd R × wave4 3 | 0.11 | -0.36 – 0.57 | 0.46 | 0.644 | 5607.24 |
| pInd D × wave4 1 | 0.54 | 0.14 – 0.94 | 2.65 | 0.008 | 6038.10 |
| pInd D × wave4 2 | 0.70 | 0.29 – 1.11 | 3.36 | 0.001 | 5763.67 |
| pInd D × wave4 3 | 0.64 | 0.22 – 1.07 | 2.97 | 0.003 | 5618.08 |
| pInd R × voteconfidence | 0.16 | 0.05 – 0.27 | 2.84 | 0.004 | 6421.12 |
| pInd D × voteconfidence | 0.07 | -0.04 – 0.17 | 1.27 | 0.203 | 6469.22 |
| wave4 1 × voteconfidence | 0.14 | 0.03 – 0.24 | 2.55 | 0.011 | 5956.45 |
| wave4 2 × voteconfidence | 0.05 | -0.05 – 0.16 | 0.99 | 0.321 | 5738.44 |
| wave4 3 × voteconfidence | 0.12 | 0.01 – 0.23 | 2.09 | 0.037 | 5602.71 |
|
(pInd R × wave4 1) × voteconfidence |
-0.13 | -0.25 – -0.00 | -1.96 | 0.050 | 5973.44 |
|
(pInd R × wave4 2) × voteconfidence |
-0.17 | -0.30 – -0.04 | -2.51 | 0.012 | 5755.59 |
|
(pInd R × wave4 3) × voteconfidence |
-0.16 | -0.30 – -0.03 | -2.34 | 0.019 | 5617.35 |
|
(pInd D × wave4 1) × voteconfidence |
-0.01 | -0.13 – 0.11 | -0.21 | 0.835 | 6005.22 |
|
(pInd D × wave4 2) × voteconfidence |
-0.00 | -0.13 – 0.12 | -0.06 | 0.953 | 5744.42 |
|
(pInd D × wave4 3) × voteconfidence |
-0.10 | -0.23 – 0.03 | -1.56 | 0.120 | 5620.69 |
| Random Effects | |||||
| σ2 | 0.40 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.57 | ||||
| N pid | 2592 | ||||
| Observations | 7975 | ||||
| Marginal R2 / Conditional R2 | 0.202 / 0.653 | ||||
TrustPel.m1.w4.r <- lmer(trustGovt ~ (pRep_D + pRep_I) * (wave4_1 + wave4_2+ wave4_3) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(TrustPel.m1.w4.r, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.50 | -1.79 – -1.21 | -10.04 | <0.001 | 7679.64 |
| pRep D | -0.42 | -0.72 – -0.12 | -2.78 | 0.005 | 6631.69 |
| pRep I | -0.09 | -0.47 – 0.29 | -0.47 | 0.638 | 6617.54 |
| wave4 1 | -0.20 | -0.46 – 0.05 | -1.58 | 0.113 | 5943.16 |
| wave4 2 | -0.01 | -0.29 – 0.27 | -0.06 | 0.952 | 5762.21 |
| wave4 3 | -0.13 | -0.41 – 0.15 | -0.90 | 0.371 | 5615.31 |
| voteconfidence | 0.24 | 0.18 – 0.30 | 7.46 | <0.001 | 6361.90 |
| vs race [Black] | 0.22 | 0.12 – 0.32 | 4.19 | <0.001 | 2587.58 |
| vs race [Hispanic] | 0.23 | 0.13 – 0.34 | 4.29 | <0.001 | 2531.51 |
| vs race [Other] | 0.07 | -0.05 – 0.20 | 1.15 | 0.248 | 2448.53 |
| vs age | -0.00 | -0.00 – 0.00 | -0.48 | 0.632 | 2500.76 |
| male female | 0.04 | -0.03 – 0.10 | 1.08 | 0.282 | 2518.40 |
| nonbinary mf | 0.66 | 0.27 – 1.05 | 3.30 | 0.001 | 2585.11 |
| pRep D × wave4 1 | 0.90 | 0.56 – 1.23 | 5.20 | <0.001 | 6102.44 |
| pRep D × wave4 2 | 1.04 | 0.69 – 1.38 | 5.84 | <0.001 | 5763.27 |
| pRep D × wave4 3 | 0.53 | 0.18 – 0.89 | 2.96 | 0.003 | 5634.26 |
| pRep I × wave4 1 | 0.36 | -0.06 – 0.77 | 1.69 | 0.091 | 5952.22 |
| pRep I × wave4 2 | 0.34 | -0.11 – 0.78 | 1.46 | 0.144 | 5773.03 |
| pRep I × wave4 3 | -0.11 | -0.57 – 0.36 | -0.46 | 0.644 | 5607.24 |
| pRep D × voteconfidence | -0.09 | -0.17 – -0.01 | -2.32 | 0.021 | 6361.49 |
| pRep I × voteconfidence | -0.16 | -0.27 – -0.05 | -2.84 | 0.004 | 6421.12 |
| wave4 1 × voteconfidence | 0.01 | -0.06 – 0.08 | 0.25 | 0.803 | 5969.97 |
| wave4 2 × voteconfidence | -0.11 | -0.19 – -0.04 | -3.00 | 0.003 | 5761.37 |
| wave4 3 × voteconfidence | -0.04 | -0.12 – 0.03 | -1.10 | 0.272 | 5628.29 |
|
(pRep D × wave4 1) × voteconfidence |
0.11 | 0.02 – 0.21 | 2.41 | 0.016 | 6059.96 |
|
(pRep D × wave4 2) × voteconfidence |
0.16 | 0.07 – 0.26 | 3.46 | 0.001 | 5757.37 |
|
(pRep D × wave4 3) × voteconfidence |
0.06 | -0.03 – 0.16 | 1.26 | 0.207 | 5639.66 |
|
(pRep I × wave4 1) × voteconfidence |
0.13 | 0.00 – 0.25 | 1.96 | 0.050 | 5973.44 |
|
(pRep I × wave4 2) × voteconfidence |
0.17 | 0.04 – 0.30 | 2.51 | 0.012 | 5755.59 |
|
(pRep I × wave4 3) × voteconfidence |
0.16 | 0.03 – 0.30 | 2.34 | 0.019 | 5617.35 |
| Random Effects | |||||
| σ2 | 0.40 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.57 | ||||
| N pid | 2592 | ||||
| Observations | 7975 | ||||
| Marginal R2 / Conditional R2 | 0.202 / 0.653 | ||||
b. DV: Democratic Norms
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_jitter(aes(x = voteconfidence,
y = demNorms,
fill = party_factor,
color = party_factor),
alpha = .2, size = .2,
height = .2, width = .2) +
geom_smooth(aes(x = voteconfidence,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm", alpha = .4, se = F) +
geom_smooth(aes(x = voteconfidence,
y = demNorms),
method = "lm", color = "black") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perceived Election Legitimacy",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_y_continuous(breaks = seq(-3,3,1)) +
coord_cartesian(ylim = c(0,3)) +
theme_bw()
(DVPEL2 <- ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = voteconfidence,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = element_blank(),
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_y_continuous(breaks = seq(0,2,.5)) +
coord_cartesian(ylim = c(0,2.25)) +
theme_bw())
Summary
Perceived election legitimacy positively predicts support for democratic norms (b = 0.14, p < .001), without change over course of the study (all p’s > .10). There were partisan differences in strength of predictive power (b = -0.13, p < .001), such that perceived election legitimacy was more strongly predictive of support for democratic norms for Democrats (b = 0.21, p < .001) than for Republicans (b = 0.07, p < .001), collapsing across time.
Models
Main model
DNpel.m1 <- lmer(demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DNpel.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.59 | 0.31 – 0.88 | 4.11 | <0.001 | 2844.51 |
| pDem Rep | 0.19 | -0.03 – 0.41 | 1.65 | 0.098 | 7128.32 |
| wave lin | -0.05 | -0.24 – 0.15 | -0.45 | 0.650 | 5390.85 |
| wave quad | 0.09 | -0.06 – 0.24 | 1.19 | 0.235 | 5202.29 |
| wave cub | -0.01 | -0.19 – 0.18 | -0.07 | 0.947 | 5092.39 |
| voteconfidence | 0.14 | 0.11 – 0.17 | 9.64 | <0.001 | 6977.95 |
| vs race [Black] | -0.75 | -0.91 – -0.59 | -9.46 | <0.001 | 2422.92 |
| vs race [Hispanic] | -0.56 | -0.72 – -0.40 | -6.81 | <0.001 | 2387.20 |
| vs race [Other] | -0.11 | -0.31 – 0.08 | -1.14 | 0.252 | 2321.58 |
| vs age | 0.01 | 0.01 – 0.02 | 7.40 | <0.001 | 2356.37 |
| male female | 0.03 | -0.07 – 0.13 | 0.66 | 0.512 | 2366.05 |
| nonbinary mf | -0.71 | -1.35 – -0.08 | -2.22 | 0.026 | 2488.56 |
| pDem Rep × wave lin | 0.49 | 0.09 – 0.89 | 2.41 | 0.016 | 5418.19 |
| pDem Rep × wave quad | 0.06 | -0.24 – 0.37 | 0.41 | 0.683 | 5232.06 |
| pDem Rep × wave cub | -0.28 | -0.65 – 0.09 | -1.46 | 0.145 | 5119.96 |
| pDem Rep × voteconfidence | -0.13 | -0.19 – -0.08 | -4.69 | <0.001 | 6900.78 |
| wave lin × voteconfidence | 0.04 | -0.01 – 0.10 | 1.61 | 0.107 | 5367.59 |
|
wave quad × voteconfidence |
0.00 | -0.04 – 0.04 | 0.18 | 0.859 | 5200.78 |
| wave cub × voteconfidence | -0.01 | -0.06 – 0.04 | -0.43 | 0.665 | 5104.60 |
|
(pDem Rep × wave lin) × voteconfidence |
-0.10 | -0.21 – 0.01 | -1.84 | 0.066 | 5381.93 |
|
(pDem Rep × wave quad) × voteconfidence |
0.01 | -0.07 – 0.09 | 0.17 | 0.862 | 5222.15 |
|
(pDem Rep × wave cub) × voteconfidence |
0.05 | -0.05 – 0.15 | 0.91 | 0.365 | 5126.08 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.18 | ||||
| ICC | 0.63 | ||||
| N pid | 2360 | ||||
| Observations | 7151 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.662 | ||||
Party ID-specific Effects
DNpel.m1.d <- lmer(demNorms ~ (pDem_R) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DNpel.m1.d, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.50 | 0.21 – 0.80 | 3.33 | 0.001 | 3114.67 |
| pDem R | 0.19 | -0.03 – 0.41 | 1.65 | 0.098 | 7128.32 |
| wave lin | -0.29 | -0.55 – -0.03 | -2.21 | 0.027 | 5485.61 |
| wave quad | 0.06 | -0.13 – 0.25 | 0.61 | 0.540 | 5242.00 |
| wave cub | 0.13 | -0.10 – 0.36 | 1.13 | 0.258 | 5124.93 |
| voteconfidence | 0.21 | 0.17 – 0.25 | 11.08 | <0.001 | 7053.86 |
| vs race [Black] | -0.75 | -0.91 – -0.59 | -9.46 | <0.001 | 2422.92 |
| vs race [Hispanic] | -0.56 | -0.72 – -0.40 | -6.81 | <0.001 | 2387.20 |
| vs race [Other] | -0.11 | -0.31 – 0.08 | -1.14 | 0.252 | 2321.58 |
| vs age | 0.01 | 0.01 – 0.02 | 7.40 | <0.001 | 2356.37 |
| male female | 0.03 | -0.07 – 0.13 | 0.66 | 0.512 | 2366.05 |
| nonbinary mf | -0.71 | -1.35 – -0.08 | -2.22 | 0.026 | 2488.55 |
| pDem R × wave lin | 0.49 | 0.09 – 0.89 | 2.41 | 0.016 | 5418.19 |
| pDem R × wave quad | 0.06 | -0.24 – 0.37 | 0.41 | 0.683 | 5232.06 |
| pDem R × wave cub | -0.28 | -0.65 – 0.09 | -1.46 | 0.145 | 5119.96 |
| pDem R × voteconfidence | -0.13 | -0.19 – -0.08 | -4.69 | <0.001 | 6900.78 |
| wave lin × voteconfidence | 0.10 | 0.03 – 0.16 | 2.74 | 0.006 | 5414.20 |
|
wave quad × voteconfidence |
0.00 | -0.05 – 0.05 | 0.00 | 0.998 | 5212.90 |
| wave cub × voteconfidence | -0.03 | -0.09 – 0.03 | -1.10 | 0.273 | 5106.05 |
|
(pDem R × wave lin) × voteconfidence |
-0.10 | -0.21 – 0.01 | -1.84 | 0.066 | 5381.93 |
|
(pDem R × wave quad) × voteconfidence |
0.01 | -0.07 – 0.09 | 0.17 | 0.862 | 5222.15 |
|
(pDem R × wave cub) × voteconfidence |
0.05 | -0.05 – 0.15 | 0.91 | 0.365 | 5126.08 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.18 | ||||
| ICC | 0.63 | ||||
| N pid | 2360 | ||||
| Observations | 7151 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.662 | ||||
DNpel.m1.r <- lmer(demNorms ~ (pRep_D) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DNpel.m1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.69 | 0.37 – 1.00 | 4.30 | <0.001 | 3538.80 |
| pRep D | -0.19 | -0.41 – 0.03 | -1.65 | 0.098 | 7128.32 |
| wave lin | 0.20 | -0.10 – 0.50 | 1.29 | 0.197 | 5345.69 |
| wave quad | 0.12 | -0.11 – 0.36 | 1.03 | 0.304 | 5200.79 |
| wave cub | -0.14 | -0.44 – 0.15 | -0.97 | 0.331 | 5094.78 |
| voteconfidence | 0.07 | 0.03 – 0.12 | 3.38 | 0.001 | 6832.25 |
| vs race [Black] | -0.75 | -0.91 – -0.59 | -9.46 | <0.001 | 2422.92 |
| vs race [Hispanic] | -0.56 | -0.72 – -0.40 | -6.81 | <0.001 | 2387.20 |
| vs race [Other] | -0.11 | -0.31 – 0.08 | -1.14 | 0.252 | 2321.58 |
| vs age | 0.01 | 0.01 – 0.02 | 7.40 | <0.001 | 2356.37 |
| male female | 0.03 | -0.07 – 0.13 | 0.66 | 0.512 | 2366.05 |
| nonbinary mf | -0.71 | -1.35 – -0.08 | -2.22 | 0.026 | 2488.55 |
| pRep D × wave lin | -0.49 | -0.89 – -0.09 | -2.41 | 0.016 | 5418.19 |
| pRep D × wave quad | -0.06 | -0.37 – 0.24 | -0.41 | 0.683 | 5232.06 |
| pRep D × wave cub | 0.28 | -0.09 – 0.65 | 1.46 | 0.145 | 5119.96 |
| pRep D × voteconfidence | 0.13 | 0.08 – 0.19 | 4.69 | <0.001 | 6900.78 |
| wave lin × voteconfidence | -0.01 | -0.09 – 0.08 | -0.15 | 0.881 | 5348.76 |
|
wave quad × voteconfidence |
0.01 | -0.06 – 0.07 | 0.22 | 0.822 | 5210.65 |
| wave cub × voteconfidence | 0.01 | -0.07 – 0.09 | 0.30 | 0.764 | 5120.94 |
|
(pRep D × wave lin) × voteconfidence |
0.10 | -0.01 – 0.21 | 1.84 | 0.066 | 5381.93 |
|
(pRep D × wave quad) × voteconfidence |
-0.01 | -0.09 – 0.07 | -0.17 | 0.862 | 5222.15 |
|
(pRep D × wave cub) × voteconfidence |
-0.05 | -0.15 – 0.05 | -0.91 | 0.365 | 5126.08 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.18 | ||||
| ICC | 0.63 | ||||
| N pid | 2360 | ||||
| Observations | 7151 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.662 | ||||
c. DV: Affective Polarization
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_jitter(aes(x = voteconfidence,
y = affPol,
fill = party_factor,
color = party_factor),
alpha = .2, size = .2,
height = .2, width = .2) +
geom_smooth(aes(x = voteconfidence,
y = affPol,
fill = party_factor,
color = party_factor),
method = "lm", alpha = .4, se = F) +
geom_smooth(aes(x = voteconfidence,
y = affPol),
method = "lm", color = "black") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perceived Election Legitimacy",
y = "Affective Polarization") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
coord_cartesian(ylim = c(0,100)) +
theme_bw()
(DVPEL3 <- ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = voteconfidence,
y = affPol,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perceived Election Legitimacy",
y = "Affective Polarization") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
coord_cartesian(ylim = c(0,100)) +
theme_bw())
Negative Polarization (Just Outparty Perceptions) Instead
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_jitter(aes(x = voteconfidence,
y = FT_Outgroup,
fill = party_factor,
color = party_factor),
alpha = .2, size = .2,
height = .2, width = .2) +
geom_smooth(aes(x = voteconfidence,
y = FT_Outgroup,
fill = party_factor,
color = party_factor),
method = "lm", alpha = .4, se = F) +
geom_smooth(aes(x = voteconfidence,
y = FT_Outgroup),
method = "lm", color = "black") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perceived Election Legitimacy",
y = "Negative Polarization") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","#db2b27")) +
# coord_cartesian(ylim = c(0,100)) +
theme_bw()
Summary
Perceived election legitimacy did not significantly predict affective polarization when collapsing across time and partisan identity (p = .090), nor did its predictive strength vary over time (all p’s > .10) or by partisan identity, collapsing across time (p = .73).
However, there was a significant interaction of perceived election legitimacy, partisan identity, and linear time (b = 4.64, p = .001). For Democrats, the perceived election legitimacy-affective polarization relationship was positive collapsing across time (b = 0.21, p < .001) but became more negative from wave 1 to wave 4 (b = 0.10, p = .006), while for Republicans, the relationship was also positive collapsing over time (b = 0.07, p = .001), but did not significantly change over time (all p’s > .10).
Models
Main Model
APpel.m1 <- lmer(affPol ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(APpel.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| affPol | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 37.23 | 29.65 – 44.82 | 9.63 | <0.001 | 2779.26 |
| pDem Rep | -8.07 | -13.69 – -2.45 | -2.81 | 0.005 | 7111.72 |
| wave lin | 0.97 | -3.98 – 5.92 | 0.38 | 0.701 | 5278.90 |
| wave quad | 1.30 | -2.44 – 5.04 | 0.68 | 0.495 | 5111.83 |
| wave cub | 0.91 | -3.69 – 5.51 | 0.39 | 0.699 | 5015.87 |
| voteconfidence | -0.63 | -1.37 – 0.10 | -1.69 | 0.090 | 6811.09 |
| vs race [Black] | -1.92 | -6.10 – 2.26 | -0.90 | 0.367 | 2407.29 |
| vs race [Hispanic] | -2.52 | -6.87 – 1.83 | -1.14 | 0.256 | 2359.86 |
| vs race [Other] | 1.17 | -4.13 – 6.48 | 0.43 | 0.664 | 2304.94 |
| vs age | 0.28 | 0.18 – 0.37 | 5.82 | <0.001 | 2336.02 |
| male female | 0.87 | -1.84 – 3.58 | 0.63 | 0.527 | 2346.17 |
| nonbinary mf | 5.73 | -11.20 – 22.67 | 0.66 | 0.507 | 2447.57 |
| pDem Rep × wave lin | -12.71 | -22.66 – -2.77 | -2.51 | 0.012 | 5304.28 |
| pDem Rep × wave quad | -6.21 | -13.73 – 1.32 | -1.62 | 0.106 | 5137.40 |
| pDem Rep × wave cub | 10.87 | 1.62 – 20.12 | 2.30 | 0.021 | 5039.82 |
| pDem Rep × voteconfidence | -0.25 | -1.68 – 1.17 | -0.35 | 0.726 | 6716.53 |
| wave lin × voteconfidence | 0.75 | -0.60 – 2.10 | 1.09 | 0.277 | 5257.62 |
|
wave quad × voteconfidence |
-0.01 | -1.02 – 1.00 | -0.02 | 0.982 | 5109.66 |
| wave cub × voteconfidence | -1.03 | -2.27 – 0.20 | -1.64 | 0.100 | 5027.14 |
|
(pDem Rep × wave lin) × voteconfidence |
4.64 | 1.93 – 7.34 | 3.36 | 0.001 | 5270.64 |
|
(pDem Rep × wave quad) × voteconfidence |
2.37 | 0.33 – 4.40 | 2.28 | 0.023 | 5127.78 |
|
(pDem Rep × wave cub) × voteconfidence |
-2.85 | -5.33 – -0.38 | -2.26 | 0.024 | 5045.83 |
| Random Effects | |||||
| σ2 | 432.07 | ||||
| τ00 pid | 881.60 | ||||
| ICC | 0.67 | ||||
| N pid | 2360 | ||||
| Observations | 7141 | ||||
| Marginal R2 / Conditional R2 | 0.027 / 0.680 | ||||
Party ID-specific Effects
APpel.m1.d <- lmer(demNorms ~ (pDem_R) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(APpel.m1.d, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.50 | 0.21 – 0.80 | 3.33 | 0.001 | 3114.67 |
| pDem R | 0.19 | -0.03 – 0.41 | 1.65 | 0.098 | 7128.32 |
| wave lin | -0.29 | -0.55 – -0.03 | -2.21 | 0.027 | 5485.61 |
| wave quad | 0.06 | -0.13 – 0.25 | 0.61 | 0.540 | 5242.00 |
| wave cub | 0.13 | -0.10 – 0.36 | 1.13 | 0.258 | 5124.93 |
| voteconfidence | 0.21 | 0.17 – 0.25 | 11.08 | <0.001 | 7053.86 |
| vs race [Black] | -0.75 | -0.91 – -0.59 | -9.46 | <0.001 | 2422.92 |
| vs race [Hispanic] | -0.56 | -0.72 – -0.40 | -6.81 | <0.001 | 2387.20 |
| vs race [Other] | -0.11 | -0.31 – 0.08 | -1.14 | 0.252 | 2321.58 |
| vs age | 0.01 | 0.01 – 0.02 | 7.40 | <0.001 | 2356.37 |
| male female | 0.03 | -0.07 – 0.13 | 0.66 | 0.512 | 2366.05 |
| nonbinary mf | -0.71 | -1.35 – -0.08 | -2.22 | 0.026 | 2488.55 |
| pDem R × wave lin | 0.49 | 0.09 – 0.89 | 2.41 | 0.016 | 5418.19 |
| pDem R × wave quad | 0.06 | -0.24 – 0.37 | 0.41 | 0.683 | 5232.06 |
| pDem R × wave cub | -0.28 | -0.65 – 0.09 | -1.46 | 0.145 | 5119.96 |
| pDem R × voteconfidence | -0.13 | -0.19 – -0.08 | -4.69 | <0.001 | 6900.78 |
| wave lin × voteconfidence | 0.10 | 0.03 – 0.16 | 2.74 | 0.006 | 5414.20 |
|
wave quad × voteconfidence |
0.00 | -0.05 – 0.05 | 0.00 | 0.998 | 5212.90 |
| wave cub × voteconfidence | -0.03 | -0.09 – 0.03 | -1.10 | 0.273 | 5106.05 |
|
(pDem R × wave lin) × voteconfidence |
-0.10 | -0.21 – 0.01 | -1.84 | 0.066 | 5381.93 |
|
(pDem R × wave quad) × voteconfidence |
0.01 | -0.07 – 0.09 | 0.17 | 0.862 | 5222.15 |
|
(pDem R × wave cub) × voteconfidence |
0.05 | -0.05 – 0.15 | 0.91 | 0.365 | 5126.08 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.18 | ||||
| ICC | 0.63 | ||||
| N pid | 2360 | ||||
| Observations | 7151 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.662 | ||||
APpel.m1.r <- lmer(demNorms ~ (pRep_D) * (wave.lin + wave.quad + wave.cub) * voteconfidence
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(APpel.m1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.69 | 0.37 – 1.00 | 4.30 | <0.001 | 3538.80 |
| pRep D | -0.19 | -0.41 – 0.03 | -1.65 | 0.098 | 7128.32 |
| wave lin | 0.20 | -0.10 – 0.50 | 1.29 | 0.197 | 5345.69 |
| wave quad | 0.12 | -0.11 – 0.36 | 1.03 | 0.304 | 5200.79 |
| wave cub | -0.14 | -0.44 – 0.15 | -0.97 | 0.331 | 5094.78 |
| voteconfidence | 0.07 | 0.03 – 0.12 | 3.38 | 0.001 | 6832.25 |
| vs race [Black] | -0.75 | -0.91 – -0.59 | -9.46 | <0.001 | 2422.92 |
| vs race [Hispanic] | -0.56 | -0.72 – -0.40 | -6.81 | <0.001 | 2387.20 |
| vs race [Other] | -0.11 | -0.31 – 0.08 | -1.14 | 0.252 | 2321.58 |
| vs age | 0.01 | 0.01 – 0.02 | 7.40 | <0.001 | 2356.37 |
| male female | 0.03 | -0.07 – 0.13 | 0.66 | 0.512 | 2366.05 |
| nonbinary mf | -0.71 | -1.35 – -0.08 | -2.22 | 0.026 | 2488.55 |
| pRep D × wave lin | -0.49 | -0.89 – -0.09 | -2.41 | 0.016 | 5418.19 |
| pRep D × wave quad | -0.06 | -0.37 – 0.24 | -0.41 | 0.683 | 5232.06 |
| pRep D × wave cub | 0.28 | -0.09 – 0.65 | 1.46 | 0.145 | 5119.96 |
| pRep D × voteconfidence | 0.13 | 0.08 – 0.19 | 4.69 | <0.001 | 6900.78 |
| wave lin × voteconfidence | -0.01 | -0.09 – 0.08 | -0.15 | 0.881 | 5348.76 |
|
wave quad × voteconfidence |
0.01 | -0.06 – 0.07 | 0.22 | 0.822 | 5210.65 |
| wave cub × voteconfidence | 0.01 | -0.07 – 0.09 | 0.30 | 0.764 | 5120.94 |
|
(pRep D × wave lin) × voteconfidence |
0.10 | -0.01 – 0.21 | 1.84 | 0.066 | 5381.93 |
|
(pRep D × wave quad) × voteconfidence |
-0.01 | -0.09 – 0.07 | -0.17 | 0.862 | 5222.15 |
|
(pRep D × wave cub) × voteconfidence |
-0.05 | -0.15 – 0.05 | -0.91 | 0.365 | 5126.08 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.18 | ||||
| ICC | 0.63 | ||||
| N pid | 2360 | ||||
| Observations | 7151 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.662 | ||||
d. All pro-democracy attitudes controlling for each other?
DemPEL.m1 <- lmer(trustGovt ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * voteconfidence * demNorms * affPol.100
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DemPEL.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| trustGovt | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -1.48 | -1.72 – -1.23 | -11.82 | <0.001 | 4787.82 |
| pDem Rep | -0.14 | -0.47 – 0.19 | -0.85 | 0.397 | 6380.64 |
| wave lin | -0.32 | -0.69 – 0.05 | -1.68 | 0.094 | 5377.33 |
| wave quad | 0.01 | -0.27 – 0.29 | 0.07 | 0.948 | 5239.83 |
| wave cub | -0.38 | -0.73 – -0.04 | -2.16 | 0.031 | 5211.06 |
| voteconfidence | 0.24 | 0.19 – 0.29 | 10.27 | <0.001 | 6449.21 |
| demNorms | 0.04 | -0.05 – 0.13 | 0.82 | 0.410 | 6284.75 |
| affPol 100 | -0.14 | -0.38 – 0.10 | -1.16 | 0.248 | 6416.24 |
| vs race [Black] | 0.20 | 0.09 – 0.31 | 3.68 | <0.001 | 2324.48 |
| vs race [Hispanic] | 0.25 | 0.14 – 0.36 | 4.37 | <0.001 | 2259.13 |
| vs race [Other] | 0.08 | -0.06 – 0.21 | 1.14 | 0.255 | 2150.29 |
| vs age | 0.00 | -0.00 – 0.00 | 0.61 | 0.543 | 2245.65 |
| male female | -0.00 | -0.07 – 0.07 | -0.10 | 0.922 | 2211.92 |
| nonbinary mf | 0.66 | 0.22 – 1.09 | 2.98 | 0.003 | 2348.23 |
| pDem Rep × wave lin | 0.90 | 0.15 – 1.65 | 2.35 | 0.019 | 5494.58 |
| pDem Rep × wave quad | -0.56 | -1.13 – 0.01 | -1.94 | 0.053 | 5303.02 |
| pDem Rep × wave cub | 0.23 | -0.47 – 0.92 | 0.64 | 0.523 | 5204.97 |
| pDem Rep × voteconfidence | -0.04 | -0.13 – 0.05 | -0.83 | 0.408 | 6223.72 |
| wave lin × voteconfidence | 0.05 | -0.05 – 0.15 | 0.98 | 0.325 | 5395.88 |
|
wave quad × voteconfidence |
-0.01 | -0.09 – 0.07 | -0.21 | 0.832 | 5251.81 |
| wave cub × voteconfidence | 0.11 | 0.01 – 0.20 | 2.20 | 0.028 | 5214.41 |
| pDem Rep × demNorms | -0.03 | -0.20 – 0.14 | -0.35 | 0.725 | 6037.53 |
| wave lin × demNorms | 0.13 | -0.07 – 0.34 | 1.26 | 0.208 | 5342.34 |
| wave quad × demNorms | 0.12 | -0.04 – 0.27 | 1.48 | 0.138 | 5241.87 |
| wave cub × demNorms | 0.19 | -0.00 – 0.37 | 1.94 | 0.052 | 5241.63 |
| voteconfidence × demNorms | -0.04 | -0.06 – -0.01 | -3.01 | 0.003 | 6289.99 |
| pDem Rep × affPol 100 | -0.11 | -0.58 – 0.36 | -0.47 | 0.640 | 6256.98 |
| wave lin × affPol 100 | -0.32 | -0.84 – 0.20 | -1.19 | 0.232 | 5363.86 |
| wave quad × affPol 100 | 0.28 | -0.12 – 0.69 | 1.36 | 0.173 | 5275.32 |
| wave cub × affPol 100 | 0.31 | -0.19 – 0.82 | 1.22 | 0.224 | 5183.22 |
|
voteconfidence × affPol 100 |
-0.02 | -0.09 – 0.04 | -0.69 | 0.493 | 6264.43 |
| demNorms × affPol 100 | 0.01 | -0.12 – 0.14 | 0.10 | 0.917 | 6094.61 |
|
(pDem Rep × wave lin) × voteconfidence |
-0.19 | -0.40 – 0.01 | -1.82 | 0.069 | 5495.04 |
|
(pDem Rep × wave quad) × voteconfidence |
0.12 | -0.03 – 0.28 | 1.55 | 0.122 | 5315.11 |
|
(pDem Rep × wave cub) × voteconfidence |
-0.06 | -0.25 – 0.13 | -0.61 | 0.545 | 5194.61 |
|
(pDem Rep × wave lin) × demNorms |
-0.10 | -0.52 – 0.31 | -0.48 | 0.630 | 5393.13 |
|
(pDem Rep × wave quad) × demNorms |
0.02 | -0.30 – 0.33 | 0.10 | 0.916 | 5337.64 |
|
(pDem Rep × wave cub) × demNorms |
-0.15 | -0.52 – 0.23 | -0.76 | 0.446 | 5276.04 |
|
(pDem Rep × voteconfidence) × demNorms |
0.00 | -0.04 – 0.05 | 0.12 | 0.904 | 6129.73 |
|
(wave lin × voteconfidence) × demNorms |
-0.06 | -0.12 – -0.01 | -2.41 | 0.016 | 5348.74 |
|
(wave quad × voteconfidence) × demNorms |
-0.02 | -0.06 – 0.02 | -1.18 | 0.239 | 5239.53 |
|
(wave cub × voteconfidence) × demNorms |
-0.05 | -0.10 – -0.00 | -2.10 | 0.036 | 5239.18 |
|
(pDem Rep × wave lin) × affPol 100 |
0.20 | -0.86 – 1.26 | 0.37 | 0.715 | 5449.66 |
|
(pDem Rep × wave quad) × affPol 100 |
0.58 | -0.24 – 1.40 | 1.39 | 0.166 | 5311.23 |
|
(pDem Rep × wave cub) × affPol 100 |
0.32 | -0.69 – 1.33 | 0.62 | 0.533 | 5172.17 |
|
(pDem Rep × voteconfidence) × affPol 100 |
0.07 | -0.05 – 0.20 | 1.14 | 0.255 | 6144.56 |
|
(wave lin × voteconfidence) × affPol 100 |
-0.01 | -0.15 – 0.14 | -0.13 | 0.899 | 5375.16 |
|
(wave quad × voteconfidence) × affPol 100 |
-0.08 | -0.19 – 0.03 | -1.36 | 0.173 | 5277.75 |
|
(wave cub × voteconfidence) × affPol 100 |
-0.11 | -0.25 – 0.03 | -1.56 | 0.119 | 5206.94 |
|
(pDem Rep × demNorms) × affPol 100 |
-0.06 | -0.31 – 0.20 | -0.43 | 0.668 | 5964.37 |
|
(wave lin × demNorms) × affPol 100 |
-0.08 | -0.38 – 0.22 | -0.51 | 0.612 | 5286.02 |
|
(wave quad × demNorms) × affPol 100 |
-0.24 | -0.47 – -0.00 | -2.00 | 0.045 | 5305.48 |
|
(wave cub × demNorms) × affPol 100 |
-0.34 | -0.62 – -0.06 | -2.35 | 0.019 | 5186.40 |
|
(voteconfidence × demNorms) × affPol 100 |
0.01 | -0.02 – 0.04 | 0.53 | 0.599 | 6110.59 |
|
(pDem Rep × wave lin × voteconfidence) × demNorms |
0.05 | -0.06 – 0.15 | 0.85 | 0.394 | 5395.22 |
|
(pDem Rep × wave quad × voteconfidence) × demNorms |
-0.01 | -0.09 – 0.07 | -0.34 | 0.734 | 5324.73 |
|
(pDem Rep × wave cub × voteconfidence) × demNorms |
0.05 | -0.04 – 0.15 | 1.09 | 0.276 | 5251.81 |
|
(pDem Rep × wave lin × voteconfidence) × affPol 100 |
0.52 | 0.23 – 0.81 | 3.49 | <0.001 | 5453.00 |
|
(pDem Rep × wave quad × voteconfidence) × affPol 100 |
-0.32 | -0.54 – -0.09 | -2.78 | 0.006 | 5313.52 |
|
(pDem Rep × wave cub × voteconfidence) × affPol 100 |
-0.02 | -0.29 – 0.26 | -0.12 | 0.904 | 5183.66 |
|
(pDem Rep × wave lin × demNorms) × affPol 100 |
0.24 | -0.37 – 0.84 | 0.77 | 0.442 | 5312.13 |
|
(pDem Rep × wave quad × demNorms) × affPol 100 |
-0.28 | -0.75 – 0.19 | -1.16 | 0.244 | 5366.87 |
|
(pDem Rep × wave cub × demNorms) × affPol 100 |
-0.29 | -0.85 – 0.27 | -1.00 | 0.316 | 5199.55 |
|
(pDem Rep × voteconfidence × demNorms) × affPol 100 |
0.02 | -0.05 – 0.08 | 0.49 | 0.623 | 6052.75 |
|
(wave lin × voteconfidence × demNorms) × affPol 100 |
0.03 | -0.05 – 0.11 | 0.76 | 0.448 | 5292.31 |
|
(wave quad × voteconfidence × demNorms) × affPol 100 |
0.05 | -0.00 – 0.11 | 1.84 | 0.066 | 5295.64 |
|
(wave cub × voteconfidence × demNorms) × affPol 100 |
0.09 | 0.02 – 0.16 | 2.43 | 0.015 | 5200.83 |
|
(pDem Rep × wave lin × voteconfidence × demNorms) × affPol 100 |
-0.07 | -0.22 – 0.08 | -0.93 | 0.350 | 5319.16 |
|
(pDem Rep × wave quad × voteconfidence × demNorms) × affPol 100 |
0.06 | -0.06 – 0.18 | 1.01 | 0.311 | 5345.10 |
|
(pDem Rep × wave cub × voteconfidence × demNorms) × affPol 100 |
0.04 | -0.10 – 0.19 | 0.61 | 0.541 | 5200.73 |
| Random Effects | |||||
| σ2 | 0.37 | ||||
| τ00 pid | 0.53 | ||||
| ICC | 0.58 | ||||
| N pid | 2345 | ||||
| Observations | 7051 | ||||
| Marginal R2 / Conditional R2 | 0.238 / 0.683 | ||||
DemPEL.m2 <- lmer(demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * voteconfidence * affPol.100 * trustGovt.c
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DemPEL.m2, show.stat = T, show.df = T, df.method = "satterthwaite")| demNorms | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.65 | 0.33 – 0.96 | 4.00 | <0.001 | 3966.12 |
| pDem Rep | -0.04 | -0.42 – 0.33 | -0.23 | 0.814 | 6514.27 |
| wave lin | -0.10 | -0.51 – 0.31 | -0.47 | 0.636 | 5302.38 |
| wave quad | 0.03 | -0.27 – 0.34 | 0.21 | 0.831 | 5163.99 |
| wave cub | -0.31 | -0.67 – 0.06 | -1.65 | 0.100 | 5102.69 |
| voteconfidence | 0.15 | 0.10 – 0.20 | 5.90 | <0.001 | 6593.39 |
| affPol 100 | -0.22 | -0.52 – 0.09 | -1.38 | 0.167 | 6299.20 |
| trustGovt c | -0.14 | -0.31 – 0.03 | -1.62 | 0.106 | 6260.29 |
| vs race [Black] | -0.73 | -0.88 – -0.57 | -9.30 | <0.001 | 2375.08 |
| vs race [Hispanic] | -0.54 | -0.70 – -0.38 | -6.67 | <0.001 | 2334.13 |
| vs race [Other] | -0.11 | -0.30 – 0.09 | -1.10 | 0.272 | 2252.40 |
| vs age | 0.01 | 0.01 – 0.02 | 7.46 | <0.001 | 2347.07 |
| male female | 0.03 | -0.06 – 0.13 | 0.69 | 0.492 | 2309.78 |
| nonbinary mf | -0.57 | -1.19 – 0.05 | -1.80 | 0.072 | 2442.95 |
| pDem Rep × wave lin | 0.25 | -0.58 – 1.08 | 0.59 | 0.557 | 5325.20 |
| pDem Rep × wave quad | -0.26 | -0.88 – 0.36 | -0.83 | 0.405 | 5230.48 |
| pDem Rep × wave cub | 0.12 | -0.62 – 0.85 | 0.31 | 0.756 | 5133.68 |
| pDem Rep × voteconfidence | -0.04 | -0.14 – 0.06 | -0.78 | 0.434 | 6381.56 |
| wave lin × voteconfidence | 0.04 | -0.07 – 0.15 | 0.71 | 0.477 | 5346.94 |
|
wave quad × voteconfidence |
0.02 | -0.06 – 0.10 | 0.49 | 0.627 | 5187.55 |
| wave cub × voteconfidence | 0.04 | -0.06 – 0.14 | 0.82 | 0.410 | 5119.64 |
| pDem Rep × affPol 100 | 0.28 | -0.32 – 0.88 | 0.91 | 0.361 | 6150.11 |
| wave lin × affPol 100 | 0.06 | -0.63 – 0.76 | 0.18 | 0.859 | 5311.33 |
| wave quad × affPol 100 | 0.09 | -0.43 – 0.61 | 0.34 | 0.732 | 5171.71 |
| wave cub × affPol 100 | 0.55 | -0.08 – 1.17 | 1.71 | 0.087 | 5130.95 |
|
voteconfidence × affPol 100 |
-0.02 | -0.10 – 0.06 | -0.40 | 0.686 | 6236.76 |
| pDem Rep × trustGovt c | 0.03 | -0.30 – 0.35 | 0.15 | 0.882 | 6070.05 |
| wave lin × trustGovt c | -0.10 | -0.49 – 0.29 | -0.49 | 0.622 | 5524.32 |
| wave quad × trustGovt c | 0.02 | -0.27 – 0.31 | 0.15 | 0.881 | 5230.43 |
| wave cub × trustGovt c | -0.12 | -0.47 – 0.23 | -0.67 | 0.503 | 5107.65 |
|
voteconfidence × trustGovt c |
0.00 | -0.04 – 0.04 | 0.00 | 1.000 | 6209.86 |
| affPol 100 × trustGovt c | 0.25 | -0.01 – 0.51 | 1.87 | 0.061 | 5972.12 |
|
(pDem Rep × wave lin) × voteconfidence |
-0.02 | -0.24 – 0.20 | -0.19 | 0.849 | 5361.53 |
|
(pDem Rep × wave quad) × voteconfidence |
0.05 | -0.11 – 0.22 | 0.62 | 0.534 | 5243.99 |
|
(pDem Rep × wave cub) × voteconfidence |
-0.06 | -0.26 – 0.13 | -0.65 | 0.513 | 5145.56 |
|
(pDem Rep × wave lin) × affPol 100 |
-0.22 | -1.61 – 1.17 | -0.31 | 0.760 | 5323.54 |
|
(pDem Rep × wave quad) × affPol 100 |
0.11 | -0.93 – 1.15 | 0.21 | 0.836 | 5205.68 |
|
(pDem Rep × wave cub) × affPol 100 |
-1.11 | -2.36 – 0.14 | -1.74 | 0.082 | 5138.35 |
|
(pDem Rep × voteconfidence) × affPol 100 |
-0.19 | -0.34 – -0.03 | -2.35 | 0.019 | 6119.09 |
|
(wave lin × voteconfidence) × affPol 100 |
-0.01 | -0.19 – 0.17 | -0.08 | 0.937 | 5342.02 |
|
(wave quad × voteconfidence) × affPol 100 |
-0.02 | -0.16 – 0.11 | -0.33 | 0.745 | 5194.98 |
|
(wave cub × voteconfidence) × affPol 100 |
-0.10 | -0.26 – 0.07 | -1.17 | 0.243 | 5145.24 |
|
(pDem Rep × wave lin) × trustGovt c |
-0.35 | -1.12 – 0.43 | -0.88 | 0.380 | 5483.85 |
|
(pDem Rep × wave quad) × trustGovt c |
-0.18 | -0.76 – 0.41 | -0.60 | 0.549 | 5308.26 |
|
(pDem Rep × wave cub) × trustGovt c |
-0.82 | -1.52 – -0.11 | -2.28 | 0.023 | 5146.36 |
|
(pDem Rep × voteconfidence) × trustGovt c |
0.00 | -0.08 – 0.09 | 0.09 | 0.928 | 6036.89 |
|
(wave lin × voteconfidence) × trustGovt c |
0.07 | -0.03 – 0.17 | 1.34 | 0.179 | 5582.92 |
|
(wave quad × voteconfidence) × trustGovt c |
-0.01 | -0.09 – 0.06 | -0.35 | 0.730 | 5227.87 |
|
(wave cub × voteconfidence) × trustGovt c |
0.02 | -0.07 – 0.11 | 0.47 | 0.636 | 5112.29 |
|
(pDem Rep × affPol 100) × trustGovt c |
-0.14 | -0.65 – 0.38 | -0.52 | 0.600 | 5874.97 |
|
(wave lin × affPol 100) × trustGovt c |
0.10 | -0.51 – 0.72 | 0.33 | 0.743 | 5470.61 |
|
(wave quad × affPol 100) × trustGovt c |
-0.04 | -0.51 – 0.42 | -0.19 | 0.850 | 5217.81 |
|
(wave cub × affPol 100) × trustGovt c |
0.21 | -0.35 – 0.77 | 0.74 | 0.460 | 5110.62 |
|
(voteconfidence × affPol 100) × trustGovt c |
-0.05 | -0.11 – 0.01 | -1.58 | 0.113 | 5919.31 |
|
(pDem Rep × wave lin × voteconfidence) × affPol 100 |
0.12 | -0.24 – 0.48 | 0.67 | 0.505 | 5355.01 |
|
(pDem Rep × wave quad × voteconfidence) × affPol 100 |
0.03 | -0.24 – 0.30 | 0.24 | 0.811 | 5229.10 |
|
(pDem Rep × wave cub × voteconfidence) × affPol 100 |
0.27 | -0.05 – 0.59 | 1.65 | 0.100 | 5146.99 |
|
(pDem Rep × wave lin × voteconfidence) × trustGovt c |
0.05 | -0.14 – 0.25 | 0.54 | 0.588 | 5553.14 |
|
(pDem Rep × wave quad × voteconfidence) × trustGovt c |
0.05 | -0.10 – 0.19 | 0.63 | 0.531 | 5307.45 |
|
(pDem Rep × wave cub × voteconfidence) × trustGovt c |
0.22 | 0.04 – 0.39 | 2.41 | 0.016 | 5144.89 |
|
(pDem Rep × wave lin × affPol 100) × trustGovt c |
-0.10 | -1.33 – 1.13 | -0.15 | 0.877 | 5446.75 |
|
(pDem Rep × wave quad × affPol 100) × trustGovt c |
-0.64 | -1.56 – 0.29 | -1.34 | 0.180 | 5258.23 |
|
(pDem Rep × wave cub × affPol 100) × trustGovt c |
0.51 | -0.62 – 1.64 | 0.89 | 0.376 | 5126.71 |
|
(pDem Rep × voteconfidence × affPol 100) × trustGovt c |
0.02 | -0.11 – 0.14 | 0.29 | 0.775 | 5841.66 |
|
(wave lin × voteconfidence × affPol 100) × trustGovt c |
-0.06 | -0.21 – 0.09 | -0.77 | 0.444 | 5565.08 |
|
(wave quad × voteconfidence × affPol 100) × trustGovt c |
0.04 | -0.07 – 0.16 | 0.71 | 0.475 | 5228.23 |
|
(wave cub × voteconfidence × affPol 100) × trustGovt c |
-0.06 | -0.20 – 0.07 | -0.90 | 0.366 | 5124.22 |
|
(pDem Rep × wave lin × voteconfidence × affPol 100) × trustGovt c |
0.09 | -0.22 – 0.39 | 0.55 | 0.585 | 5543.41 |
|
(pDem Rep × wave quad × voteconfidence × affPol 100) × trustGovt c |
0.15 | -0.08 – 0.38 | 1.31 | 0.190 | 5272.30 |
|
(pDem Rep × wave cub × voteconfidence × affPol 100) × trustGovt c |
-0.18 | -0.46 – 0.10 | -1.25 | 0.213 | 5132.71 |
| Random Effects | |||||
| σ2 | 0.70 | ||||
| τ00 pid | 1.11 | ||||
| ICC | 0.61 | ||||
| N pid | 2345 | ||||
| Observations | 7051 | ||||
| Marginal R2 / Conditional R2 | 0.115 / 0.658 | ||||
DemPEL.m3 <- lmer(affPol ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * voteconfidence * demNorms * trustGovt.c
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(DemPEL.m3, show.stat = T, show.df = T, df.method = "satterthwaite")| affPol | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 36.86 | 28.91 – 44.80 | 9.10 | <0.001 | 3287.89 |
| pDem Rep | -9.92 | -17.38 – -2.46 | -2.61 | 0.009 | 6591.66 |
| wave lin | 4.03 | -3.85 – 11.90 | 1.00 | 0.316 | 5217.62 |
| wave quad | 1.63 | -4.26 – 7.53 | 0.54 | 0.588 | 5069.57 |
| wave cub | 0.56 | -6.56 – 7.67 | 0.15 | 0.878 | 4992.57 |
| voteconfidence | 0.01 | -1.00 – 1.01 | 0.02 | 0.988 | 6172.26 |
| demNorms | -1.40 | -3.41 – 0.60 | -1.37 | 0.169 | 5987.30 |
| trustGovt c | -3.46 | -6.43 – -0.49 | -2.28 | 0.022 | 6033.26 |
| vs race [Black] | -2.08 | -6.24 – 2.09 | -0.98 | 0.328 | 2412.15 |
| vs race [Hispanic] | -2.71 | -7.02 – 1.61 | -1.23 | 0.219 | 2328.18 |
| vs race [Other] | 0.63 | -4.63 – 5.89 | 0.23 | 0.815 | 2239.72 |
| vs age | 0.28 | 0.19 – 0.38 | 5.92 | <0.001 | 2340.60 |
| male female | 0.80 | -1.88 – 3.48 | 0.59 | 0.558 | 2295.42 |
| nonbinary mf | 5.58 | -11.12 – 22.27 | 0.65 | 0.513 | 2391.84 |
| pDem Rep × wave lin | -6.33 | -22.15 – 9.50 | -0.78 | 0.433 | 5245.10 |
| pDem Rep × wave quad | -6.63 | -18.47 – 5.22 | -1.10 | 0.273 | 5090.19 |
| pDem Rep × wave cub | 27.04 | 12.77 – 41.31 | 3.72 | <0.001 | 5004.69 |
| pDem Rep × voteconfidence | 1.36 | -0.62 – 3.33 | 1.35 | 0.178 | 6057.35 |
| wave lin × voteconfidence | -0.57 | -2.78 – 1.63 | -0.51 | 0.612 | 5245.67 |
|
wave quad × voteconfidence |
-0.12 | -1.77 – 1.52 | -0.15 | 0.883 | 5089.33 |
| wave cub × voteconfidence | -0.38 | -2.35 – 1.59 | -0.38 | 0.707 | 5003.38 |
| pDem Rep × demNorms | -0.11 | -4.05 – 3.84 | -0.05 | 0.958 | 5899.91 |
| wave lin × demNorms | -0.14 | -4.61 – 4.32 | -0.06 | 0.950 | 5196.56 |
| wave quad × demNorms | -0.48 | -3.83 – 2.88 | -0.28 | 0.781 | 5068.18 |
| wave cub × demNorms | -0.72 | -4.79 – 3.35 | -0.35 | 0.729 | 5009.96 |
| voteconfidence × demNorms | -0.09 | -0.60 – 0.41 | -0.36 | 0.722 | 5909.22 |
| pDem Rep × trustGovt c | -1.11 | -7.00 – 4.78 | -0.37 | 0.711 | 5962.49 |
| wave lin × trustGovt c | 3.69 | -2.89 – 10.28 | 1.10 | 0.271 | 5310.12 |
| wave quad × trustGovt c | -1.45 | -6.43 – 3.52 | -0.57 | 0.567 | 5075.38 |
| wave cub × trustGovt c | 4.72 | -1.49 – 10.93 | 1.49 | 0.137 | 5012.29 |
|
voteconfidence × trustGovt c |
0.46 | -0.28 – 1.19 | 1.22 | 0.224 | 6008.27 |
| demNorms × trustGovt c | 1.19 | -0.45 – 2.83 | 1.42 | 0.154 | 5752.17 |
|
(pDem Rep × wave lin) × voteconfidence |
3.62 | -0.81 – 8.04 | 1.60 | 0.109 | 5257.00 |
|
(pDem Rep × wave quad) × voteconfidence |
3.15 | -0.15 – 6.45 | 1.87 | 0.061 | 5105.18 |
|
(pDem Rep × wave cub) × voteconfidence |
-7.46 | -11.40 – -3.52 | -3.71 | <0.001 | 5012.47 |
|
(pDem Rep × wave lin) × demNorms |
3.28 | -5.68 – 12.24 | 0.72 | 0.473 | 5219.81 |
|
(pDem Rep × wave quad) × demNorms |
-2.76 | -9.49 – 3.97 | -0.80 | 0.421 | 5085.57 |
|
(pDem Rep × wave cub) × demNorms |
-12.67 | -20.83 – -4.50 | -3.04 | 0.002 | 5019.07 |
|
(pDem Rep × voteconfidence) × demNorms |
-0.86 | -1.86 – 0.14 | -1.68 | 0.092 | 5839.15 |
|
(wave lin × voteconfidence) × demNorms |
0.33 | -0.82 – 1.48 | 0.57 | 0.571 | 5217.97 |
|
(wave quad × voteconfidence) × demNorms |
0.08 | -0.78 – 0.95 | 0.19 | 0.848 | 5084.53 |
|
(wave cub × voteconfidence) × demNorms |
-0.11 | -1.14 – 0.93 | -0.21 | 0.837 | 5013.88 |
|
(pDem Rep × wave lin) × trustGovt c |
7.27 | -5.81 – 20.36 | 1.09 | 0.276 | 5262.66 |
|
(pDem Rep × wave quad) × trustGovt c |
4.38 | -5.63 – 14.40 | 0.86 | 0.391 | 5102.89 |
|
(pDem Rep × wave cub) × trustGovt c |
6.26 | -6.21 – 18.74 | 0.98 | 0.325 | 5032.97 |
|
(pDem Rep × voteconfidence) × trustGovt c |
0.29 | -1.17 – 1.75 | 0.39 | 0.695 | 5956.58 |
|
(wave lin × voteconfidence) × trustGovt c |
-0.54 | -2.19 – 1.10 | -0.65 | 0.517 | 5324.55 |
|
(wave quad × voteconfidence) × trustGovt c |
0.30 | -0.93 – 1.53 | 0.48 | 0.629 | 5076.42 |
|
(wave cub × voteconfidence) × trustGovt c |
-1.37 | -2.90 – 0.15 | -1.76 | 0.078 | 5003.24 |
|
(pDem Rep × demNorms) × trustGovt c |
-0.93 | -4.18 – 2.31 | -0.56 | 0.573 | 5684.23 |
|
(wave lin × demNorms) × trustGovt c |
-0.38 | -4.16 – 3.40 | -0.20 | 0.845 | 5212.14 |
|
(wave quad × demNorms) × trustGovt c |
-0.72 | -3.61 – 2.16 | -0.49 | 0.623 | 5098.82 |
|
(wave cub × demNorms) × trustGovt c |
-2.51 | -6.05 – 1.04 | -1.39 | 0.166 | 5023.56 |
|
(voteconfidence × demNorms) × trustGovt c |
-0.18 | -0.57 – 0.20 | -0.95 | 0.344 | 5744.00 |
|
(pDem Rep × wave lin × voteconfidence) × demNorms |
-0.78 | -3.08 – 1.53 | -0.66 | 0.510 | 5238.14 |
|
(pDem Rep × wave quad × voteconfidence) × demNorms |
0.28 | -1.45 – 2.00 | 0.31 | 0.754 | 5097.14 |
|
(pDem Rep × wave cub × voteconfidence) × demNorms |
3.23 | 1.15 – 5.30 | 3.05 | 0.002 | 5018.09 |
|
(pDem Rep × wave lin × voteconfidence) × trustGovt c |
-1.55 | -4.82 – 1.72 | -0.93 | 0.352 | 5287.89 |
|
(pDem Rep × wave quad × voteconfidence) × trustGovt c |
-1.29 | -3.76 – 1.19 | -1.02 | 0.307 | 5100.53 |
|
(pDem Rep × wave cub × voteconfidence) × trustGovt c |
-0.35 | -3.41 – 2.71 | -0.22 | 0.824 | 5024.36 |
|
(pDem Rep × wave lin × demNorms) × trustGovt c |
5.90 | -1.62 – 13.43 | 1.54 | 0.124 | 5187.90 |
|
(pDem Rep × wave quad × demNorms) × trustGovt c |
-4.27 | -10.08 – 1.55 | -1.44 | 0.151 | 5133.39 |
|
(pDem Rep × wave cub × demNorms) × trustGovt c |
-7.10 | -14.23 – 0.03 | -1.95 | 0.051 | 5050.21 |
|
(pDem Rep × voteconfidence × demNorms) × trustGovt c |
0.28 | -0.48 – 1.04 | 0.72 | 0.469 | 5696.48 |
|
(wave lin × voteconfidence × demNorms) × trustGovt c |
-0.05 | -0.94 – 0.84 | -0.11 | 0.914 | 5242.25 |
|
(wave quad × voteconfidence × demNorms) × trustGovt c |
0.02 | -0.66 – 0.69 | 0.04 | 0.965 | 5114.56 |
|
(wave cub × voteconfidence × demNorms) × trustGovt c |
0.58 | -0.25 – 1.40 | 1.37 | 0.170 | 5018.20 |
|
(pDem Rep × wave lin × voteconfidence × demNorms) × trustGovt c |
-1.14 | -2.91 – 0.63 | -1.26 | 0.207 | 5221.97 |
|
(pDem Rep × wave quad × voteconfidence × demNorms) × trustGovt c |
0.92 | -0.44 – 2.29 | 1.33 | 0.183 | 5144.59 |
|
(pDem Rep × wave cub × voteconfidence × demNorms) × trustGovt c |
1.29 | -0.37 – 2.94 | 1.53 | 0.127 | 5044.90 |
| Random Effects | |||||
| σ2 | 427.43 | ||||
| τ00 pid | 849.56 | ||||
| ICC | 0.67 | ||||
| N pid | 2345 | ||||
| Observations | 7051 | ||||
| Marginal R2 / Conditional R2 | 0.039 / 0.678 | ||||
##
## Pearson's product-moment correlation
##
## data: d$demNorms and d$affPol
## t = -4.3318, df = 7160, p-value = 1.499e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07419860 -0.02799901
## sample estimates:
## cor
## -0.05112616##
## Pearson's product-moment correlation
##
## data: d$demNorms and d$trustGovt
## t = -7.0066, df = 7081, p-value = 2.668e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10606102 -0.05980333
## sample estimates:
## cor
## -0.08297687##
## Pearson's product-moment correlation
##
## data: d$affPol and d$trustGovt
## t = -4.5911, df = 7070, p-value = 4.486e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07772887 -0.03125292
## sample estimates:
## cor
## -0.05452042
2. PEL Over Time (Outcome Bias)
Plots of PEL spread
ggplot(d, aes(x=voteconfidence)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Perceived Election Legitimacy")
ggplot(d[!is.na(d$party_factor),], aes(x=voteconfidence, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
facet_grid(wave~., labeller = as_labeller(wave_label)) +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Perceived Election Legitimacy")
ggplot(d[!is.na(d$party_factor),],
aes(x = party_factor,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .3, width =.3, alpha=0.4, size = .5, show.legend = F ) +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary( geom = "point", fun = "mean", color = "black", size = 1 , show.legend = F) +
theme_bw() +
scale_fill_manual(values = c("#1696d2","grey","#db2b27")) +
scale_color_manual(values = c("#1696d2","grey","#db2b27")) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy")
a. PEL Is Susceptible to Outcome Bias
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconfidence,
color = party_factor,
fill = party_factor)) +
geom_jitter(alpha = .3, size = .2) +
stat_summary(geom = "path", fun = "mean", linetype = "dashed", show.legend = F, aes(group = party_factor, color = party_factor)) +
stat_summary(geom = "point", fun = "mean") +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","black","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","black","#db2b27")) +
scale_x_discrete(labels = c("Wave 1","Wave 2", "Wave 3", "Wave 4")) +
theme(legend.position = "none") +
theme_bw()
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconfidence,
color = party_factor,
fill = party_factor)) +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary(geom = "path", fun = "mean", linetype = "dashed", show.legend = F, aes(group = party_factor, color = party_factor)) +
stat_summary(geom = "point", fun = "mean") +
labs(x = "Wave",
y = "Perceived Election Legitimacy",
title = "Perceived Election Legitimacy Over Time, Displayed by Partisan Identity") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey42","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey42","#db2b27")) +
theme(legend.position = "none") +
theme_bw()
Mean (and SD) for PEL by party over time
# Average PEL
kable(round(tapply(d$voteconfidence, list(d$party_factor, d$wave), FUN = mean, na.rm = T),2))| 1 | 2 | 3 | 4 | |
|---|---|---|---|---|
| Democrat | 3.87 | 3.60 | 3.58 | 3.39 |
| Independent | 2.72 | 3.05 | 3.15 | 2.95 |
| Republican | 2.67 | 3.68 | 3.62 | 3.65 |
## 1 2 3 4
## Democrat 1.06 1.20 1.14 1.22
## Independent 1.28 1.28 1.23 1.32
## Republican 1.16 1.02 0.96 1.02Summary
Is perceived election legitimacy sensitive to election outcome? And does outcome bias differed by partisan identity?
In wave 1, before the election, perceived election legitimacy was near the midpoint of the scale (b = 2.68, 95% CI = [2.49, 2.88], t(2722.4) = 27.12, p < .001), with Democrats expressing higher perceived election legitimacy (M = 3.87) than Republicans (M = 2.67; b = -1.24, 95% CI = [-1.33, -1.15], t(2471.0) = -27.35, p < .001) and Independents (M = 3.87; b = -0.49, 95% CI = [-0.59, -0.39], t(3297.0) = -9.52, p < .001). Collapsing across partisan identity, perceived election legitimacy increased over linear time (b = 0.17, 95% CI = [0.11, 0.23], t(2111.0) = 6.06, p < .001).
Partisan identity moderated this change over time (b = 1.20, 95% CI = [1.10, 1.31], t(2126.9) = 22.13, p < .001), in the expected direction. Democrats’ perceived election legitimacy falls linearly over time (b = -0.43, 95% CI = [-0.50, -0.37], t(5920.0) = -12.87, p < .001) until, in wave 4, Democrats express lower perceived election legitimacy (M = 3.39) than Republicans (M = 3.65; b = 0.29, 95% CI = [0.18, 0.40], t(7995.0) = 5.04, p < .001) and higher perceived election legitimacy than Independents (M = 2.95; b = -0.28, 95% CI = [-0.45, -0.12], t(7667.0) = -3.46, p = .001). Republicans’ perceived election legitimacy increases over linear time (b = 0.77, 95% CI = [0.70, 0.85], t(5898.6) = 20.66, p < .001), though this increase takes place primarily from wave 1 to wave 2 (immediately pre- and post-election; b = 1.04, 95% CI = [0.97, 1.11], t(5729.2) = 29.88, p < .001), after which Republicans’ perceived election legitimacy remains stable from wave 2 to wave 3 and wave 4 (both p’s > .05).
Models
Main Model
PEL.m1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) + vs_race + vs_age + male_female + nonbinary_mf + (wave.lin | pid),
data = d)
tab_model(PEL.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.91 | 2.71 – 3.10 | 29.71 | <0.001 | 2638.49 |
| pDem Rep | -0.21 | -0.28 – -0.13 | -5.43 | <0.001 | 3622.70 |
| pParty Ind | -0.39 | -0.49 – -0.30 | -8.33 | <0.001 | 6033.17 |
| wave lin | 0.17 | 0.11 – 0.23 | 5.63 | <0.001 | 2110.88 |
| wave quad | 0.24 | 0.20 – 0.28 | 11.39 | <0.001 | 4552.42 |
| wave cub | -0.10 | -0.15 – -0.05 | -3.91 | <0.001 | 4078.55 |
| vs race [Black] | -0.22 | -0.33 – -0.10 | -3.75 | <0.001 | 2629.05 |
| vs race [Hispanic] | -0.03 | -0.15 – 0.09 | -0.52 | 0.602 | 2594.59 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.240 | 2538.99 |
| vs age | 0.01 | 0.00 – 0.01 | 5.42 | <0.001 | 2554.74 |
| male female | -0.38 | -0.45 – -0.31 | -10.17 | <0.001 | 2539.38 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.17 | 0.030 | 2650.68 |
| pDem Rep × wave lin | 1.20 | 1.10 – 1.31 | 22.13 | <0.001 | 2126.93 |
| pDem Rep × wave quad | 0.56 | 0.48 – 0.63 | 15.19 | <0.001 | 4605.95 |
| pDem Rep × wave cub | -0.65 | -0.73 – -0.56 | -14.74 | <0.001 | 4097.92 |
| pParty Ind × wave lin | 0.01 | -0.14 – 0.17 | 0.19 | 0.851 | 2568.65 |
| pParty Ind × wave quad | 0.02 | -0.09 – 0.13 | 0.32 | 0.746 | 4805.45 |
| pParty Ind × wave cub | 0.11 | -0.02 – 0.24 | 1.63 | 0.104 | 4262.92 |
| Random Effects | |||||
| σ2 | 0.50 | ||||
| τ00 pid | 0.69 | ||||
| τ11 pid.wave.lin | 0.25 | ||||
| ρ01 pid | 0.21 | ||||
| ICC | 0.59 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.644 | ||||
Model Centered at Wave 1
PEL.m1.w1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * wave
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m1.w1, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.68 | 2.49 – 2.88 | 27.12 | <0.001 | 2722.44 |
| pDem Rep | -1.24 | -1.33 – -1.15 | -27.35 | <0.001 | 6359.22 |
| pParty Ind | -0.38 | -0.49 – -0.27 | -6.56 | <0.001 | 7978.01 |
| wave [2] | 0.35 | 0.30 – 0.41 | 13.31 | <0.001 | 5827.54 |
| wave [3] | 0.34 | 0.28 – 0.40 | 12.08 | <0.001 | 5895.87 |
| wave [4] | 0.22 | 0.16 – 0.28 | 6.86 | <0.001 | 5959.39 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pDem Rep × wave [2] | 1.33 | 1.24 – 1.42 | 28.44 | <0.001 | 5771.22 |
| pDem Rep × wave [3] | 1.29 | 1.20 – 1.39 | 26.34 | <0.001 | 5832.62 |
| pDem Rep × wave [4] | 1.53 | 1.42 – 1.64 | 26.56 | <0.001 | 5918.42 |
| pParty Ind × wave [2] | -0.07 | -0.20 – 0.07 | -0.95 | 0.344 | 5948.40 |
| pParty Ind × wave [3] | 0.04 | -0.10 – 0.19 | 0.58 | 0.564 | 5995.74 |
| pParty Ind × wave [4] | -0.05 | -0.22 – 0.11 | -0.61 | 0.545 | 6045.97 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
Party ID-specific Effects (at different time points)
PEL.m1.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m1.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.14 | 2.95 – 3.34 | 31.63 | <0.001 | 2669.12 |
| pDem R | -0.20 | -0.27 – -0.13 | -5.31 | <0.001 | 3915.77 |
| pDem I | -0.50 | -0.59 – -0.40 | -10.01 | <0.001 | 6238.04 |
| wave lin | -0.43 | -0.50 – -0.37 | -12.87 | <0.001 | 5919.98 |
| wave quad | -0.04 | -0.09 – 0.01 | -1.69 | 0.091 | 5709.24 |
| wave cub | 0.19 | 0.13 – 0.25 | 6.17 | <0.001 | 5609.38 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pDem R × wave lin | 1.21 | 1.11 – 1.30 | 23.94 | <0.001 | 5916.56 |
| pDem R × wave quad | 0.55 | 0.47 – 0.62 | 14.60 | <0.001 | 5707.11 |
| pDem R × wave cub | -0.64 | -0.73 – -0.56 | -14.20 | <0.001 | 5616.45 |
| pDem I × wave lin | 0.61 | 0.45 – 0.76 | 7.80 | <0.001 | 6041.11 |
| pDem I × wave quad | 0.29 | 0.17 – 0.40 | 4.91 | <0.001 | 5807.56 |
| pDem I × wave cub | -0.21 | -0.36 – -0.07 | -2.98 | 0.003 | 5704.80 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
PEL.m1.d.w <- lmer(voteconfidence ~ (pDem_R + pDem_I) * wave + vs_race + vs_age + male_female + nonbinary_mf + (1 | pid),
data = d)
tab_model(PEL.m1.d.w, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.43 | 3.23 – 3.62 | 34.12 | <0.001 | 2794.49 |
| pDem R | -1.24 | -1.33 – -1.15 | -27.35 | <0.001 | 6359.22 |
| pDem I | -1.00 | -1.12 – -0.88 | -16.41 | <0.001 | 7857.48 |
| wave [2] | -0.29 | -0.35 – -0.23 | -9.35 | <0.001 | 5788.82 |
| wave [3] | -0.32 | -0.38 – -0.26 | -9.84 | <0.001 | 5852.19 |
| wave [4] | -0.53 | -0.60 – -0.45 | -13.72 | <0.001 | 5926.05 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pDem R × wave [2] | 1.33 | 1.24 – 1.42 | 28.44 | <0.001 | 5771.22 |
| pDem R × wave [3] | 1.29 | 1.20 – 1.39 | 26.34 | <0.001 | 5832.62 |
| pDem R × wave [4] | 1.53 | 1.42 – 1.64 | 26.56 | <0.001 | 5918.42 |
| pDem I × wave [2] | 0.60 | 0.46 – 0.74 | 8.26 | <0.001 | 5937.49 |
| pDem I × wave [3] | 0.69 | 0.54 – 0.84 | 8.94 | <0.001 | 5986.71 |
| pDem I × wave [4] | 0.71 | 0.54 – 0.89 | 8.13 | <0.001 | 6036.28 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
PEL.m1.d.w4 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave4_1 + wave4_2 + wave4_3)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m1.d.w4, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.90 | 2.70 – 3.10 | 27.96 | <0.001 | 3132.71 |
| pDem R | 0.29 | 0.18 – 0.40 | 5.04 | <0.001 | 7995.01 |
| pDem I | -0.28 | -0.45 – -0.12 | -3.46 | 0.001 | 7666.95 |
| wave4 1 | 0.53 | 0.45 – 0.60 | 13.72 | <0.001 | 5926.05 |
| wave4 2 | 0.24 | 0.16 – 0.31 | 6.06 | <0.001 | 5727.09 |
| wave4 3 | 0.21 | 0.13 – 0.28 | 5.23 | <0.001 | 5661.83 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pDem R × wave4 1 | -1.53 | -1.64 – -1.42 | -26.56 | <0.001 | 5918.42 |
| pDem R × wave4 2 | -0.20 | -0.31 – -0.08 | -3.38 | 0.001 | 5740.45 |
| pDem R × wave4 3 | -0.24 | -0.35 – -0.12 | -4.03 | <0.001 | 5671.07 |
| pDem I × wave4 1 | -0.71 | -0.89 – -0.54 | -8.13 | <0.001 | 6036.28 |
| pDem I × wave4 2 | -0.11 | -0.29 – 0.06 | -1.25 | 0.211 | 5818.82 |
| pDem I × wave4 3 | -0.02 | -0.21 – 0.16 | -0.27 | 0.788 | 5719.44 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
PEL.m1.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.94 | 2.74 – 3.14 | 29.12 | <0.001 | 2699.41 |
| pRep D | 0.20 | 0.13 – 0.27 | 5.31 | <0.001 | 3915.77 |
| pRep I | -0.30 | -0.40 – -0.20 | -5.78 | <0.001 | 6179.33 |
| wave lin | 0.77 | 0.70 – 0.85 | 20.66 | <0.001 | 5898.56 |
| wave quad | 0.51 | 0.45 – 0.56 | 18.16 | <0.001 | 5689.24 |
| wave cub | -0.46 | -0.52 – -0.39 | -13.61 | <0.001 | 5603.09 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pRep D × wave lin | -1.21 | -1.30 – -1.11 | -23.94 | <0.001 | 5916.56 |
| pRep D × wave quad | -0.55 | -0.62 – -0.47 | -14.60 | <0.001 | 5707.11 |
| pRep D × wave cub | 0.64 | 0.56 – 0.73 | 14.20 | <0.001 | 5616.44 |
| pRep I × wave lin | -0.60 | -0.76 – -0.44 | -7.55 | <0.001 | 6035.14 |
| pRep I × wave quad | -0.26 | -0.38 – -0.14 | -4.34 | <0.001 | 5806.24 |
| pRep I × wave cub | 0.43 | 0.29 – 0.57 | 5.84 | <0.001 | 5697.51 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
PEL.m1.r.w2 <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m1.r.w2, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.23 | 3.03 – 3.43 | 31.36 | <0.001 | 2911.60 |
| pRep D | -0.09 | -0.19 – -0.00 | -1.99 | 0.046 | 6777.32 |
| pRep I | -0.49 | -0.63 – -0.35 | -7.04 | <0.001 | 8043.00 |
| wave2 1 | -1.04 | -1.11 – -0.97 | -29.88 | <0.001 | 5729.20 |
| wave2 3 | -0.07 | -0.14 – 0.00 | -1.92 | 0.055 | 5593.61 |
| wave2 4 | -0.04 | -0.12 – 0.04 | -0.93 | 0.354 | 5741.17 |
| vs race [Black] | -0.22 | -0.34 – -0.11 | -3.85 | <0.001 | 2636.40 |
| vs race [Hispanic] | -0.03 | -0.14 – 0.09 | -0.42 | 0.678 | 2606.71 |
| vs race [Other] | -0.09 | -0.23 – 0.05 | -1.26 | 0.206 | 2534.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.34 | <0.001 | 2559.11 |
| male female | -0.38 | -0.46 – -0.31 | -10.16 | <0.001 | 2540.17 |
| nonbinary mf | 0.49 | 0.05 – 0.93 | 2.18 | 0.029 | 2670.74 |
| pRep D × wave2 1 | 1.33 | 1.24 – 1.42 | 28.44 | <0.001 | 5771.22 |
| pRep D × wave2 3 | 0.04 | -0.06 – 0.14 | 0.83 | 0.405 | 5597.20 |
| pRep D × wave2 4 | -0.20 | -0.31 – -0.08 | -3.38 | 0.001 | 5740.45 |
| pRep I × wave2 1 | 0.73 | 0.59 – 0.88 | 9.84 | <0.001 | 5923.60 |
| pRep I × wave2 3 | 0.13 | -0.03 – 0.29 | 1.59 | 0.112 | 5696.15 |
| pRep I × wave2 4 | -0.08 | -0.26 – 0.10 | -0.90 | 0.369 | 5822.95 |
| Random Effects | |||||
| σ2 | 0.55 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.140 / 0.611 | ||||
3. Effect of Social Norms
a. Media-based Norms
ggplot(d[!is.na(d$party_factor),],
aes(x = pollbias.z,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm",
fullrange = T) +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Polling Partisan Bias (Normalized)",
y = "Perceived Election Legitimacy",
title = "The Influence of Media Environment Bias on Perceived Election Legitimacy,
Displayed Over Time by Partisan Identity") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) 
Summary
The impact of media environment-based social norms, as operationalized by partisan bias in polling sources, on perceived election legitimacy was evaluated in the context of a model that used perceived polling accuracy and self-reported frequency of accessing polling information as controls. Collapsing across time and partisan identity, polling bias negatively predicted perceived election legitimacy (b = -0.36, 95% CI = [-0.52, -0.21], t(2496.0) = -4.49, p < .001), such that the more conservative-tilt in a participants’ media environment, the lower their perceived election legitimacy. However, this effect occurred in the context of meaningful higher-order interactions.
The overall polling bias-perceived election legitimacy relationship changed over linear time (b = 0.34, 95% CI = [0.11 – 0.56], t(4327.0) = 2.96, p = .003), with this change primarily occurring from wave 1 to wave 2 (b = -0.35, 95% CI = [-0.56 – -0.14], t(4178.0) = -3.27, p = .001). Further, the magnitude of this change differed between Democrats and Republicans (b = -0.38, 95% CI = [-0.73 – -0.03], t(4109.0) = -2.13, p = .034), such that the change in effect of polling bias on perceived election legitimacy from wave 1 to wave 2 for Democrats (b = -0.27, 95% CI = [-0.52 – -0.01], t(4094.0) = -2.05, p = .040) was less pronounced than for Republicans (b = -0.65, 95% CI = [-0.89 – -0.41], t(4092.0) = -5.28, p < .001). Perceived polling accuracy positively predicted perceived election legitimacy overall (b = 0.08, 95% CI = [0.06 – 0.10], t(5634.0) = 8.07, p < .001), while self-reported polling frequency had a marginal negative impact on perceived election legitimacy (b = -0.02, 95% CI = [-0.04, 0.00], t(1655.0) = -1.88, p = .060).
Models
Main model
PEL.m15 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollbias.z * (wave.lin + wave.quad + wave.cub)
+ pollAcc + pollFreq.c
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1|pid),
data = d)
tab_model(PEL.m15, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.88 | 2.65 – 3.11 | 24.36 | <0.001 | 1642.16 |
| pDem Rep | -0.13 | -0.22 – -0.03 | -2.60 | 0.010 | 2907.89 |
| pParty Ind | -0.34 | -0.45 – -0.22 | -5.71 | <0.001 | 4821.95 |
| pollbias z | -0.36 | -0.52 – -0.21 | -4.49 | <0.001 | 2495.51 |
| wave lin | 0.18 | 0.10 – 0.25 | 4.78 | <0.001 | 4315.11 |
| wave quad | 0.19 | 0.13 – 0.24 | 6.80 | <0.001 | 4154.08 |
| wave cub | -0.07 | -0.14 – -0.00 | -2.01 | 0.044 | 4143.01 |
| pollAcc | 0.08 | 0.06 – 0.10 | 8.07 | <0.001 | 5634.18 |
| pollFreq c | -0.02 | -0.04 – 0.00 | -1.88 | 0.060 | 1654.89 |
| vs race [Black] | -0.26 | -0.39 – -0.13 | -3.99 | <0.001 | 1743.24 |
| vs race [Hispanic] | -0.10 | -0.24 – 0.03 | -1.47 | 0.143 | 1677.05 |
| vs race [Other] | -0.14 | -0.30 – 0.02 | -1.67 | 0.095 | 1642.37 |
| vs age | 0.01 | 0.00 – 0.01 | 4.77 | <0.001 | 1680.37 |
| male female | -0.39 | -0.47 – -0.30 | -9.00 | <0.001 | 1658.27 |
| nonbinary mf | 0.86 | 0.30 – 1.42 | 2.99 | 0.003 | 1607.59 |
| pDem Rep × pollbias z | 0.08 | -0.19 – 0.35 | 0.59 | 0.556 | 2894.35 |
| pParty Ind × pollbias z | -0.06 | -0.41 – 0.30 | -0.32 | 0.753 | 5029.97 |
| pDem Rep × wave lin | 1.14 | 1.01 – 1.27 | 17.09 | <0.001 | 4285.63 |
| pDem Rep × wave quad | 0.43 | 0.33 – 0.53 | 8.64 | <0.001 | 4159.61 |
| pDem Rep × wave cub | -0.53 | -0.65 – -0.41 | -8.89 | <0.001 | 4130.56 |
| pParty Ind × wave lin | -0.04 | -0.23 – 0.15 | -0.44 | 0.660 | 4375.60 |
| pParty Ind × wave quad | 0.01 | -0.13 – 0.16 | 0.21 | 0.838 | 4220.91 |
| pParty Ind × wave cub | 0.09 | -0.09 – 0.27 | 1.01 | 0.314 | 4210.15 |
| pollbias z × wave lin | 0.34 | 0.11 – 0.56 | 2.96 | 0.003 | 4327.37 |
| pollbias z × wave quad | 0.19 | 0.02 – 0.36 | 2.24 | 0.025 | 4165.90 |
| pollbias z × wave cub | -0.10 | -0.31 – 0.10 | -0.97 | 0.331 | 4165.38 |
|
(pDem Rep × pollbias z) × wave lin |
0.03 | -0.34 – 0.41 | 0.16 | 0.872 | 4274.70 |
|
(pDem Rep × pollbias z) × wave quad |
0.22 | -0.06 – 0.50 | 1.53 | 0.125 | 4174.66 |
|
(pDem Rep × pollbias z) × wave cub |
-0.21 | -0.54 – 0.13 | -1.20 | 0.229 | 4129.54 |
|
(pParty Ind × pollbias z) × wave lin |
0.38 | -0.22 – 0.97 | 1.25 | 0.213 | 4383.59 |
|
(pParty Ind × pollbias z) × wave quad |
-0.16 | -0.61 – 0.28 | -0.72 | 0.471 | 4233.00 |
|
(pParty Ind × pollbias z) × wave cub |
0.33 | -0.23 – 0.88 | 1.16 | 0.247 | 4235.72 |
| Random Effects | |||||
| σ2 | 0.53 | ||||
| τ00 pid | 0.57 | ||||
| ICC | 0.52 | ||||
| N pid | 1674 | ||||
| Observations | 5753 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.600 | ||||
Main model centered at Wave 2
PEL.m15.w2 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollbias.z * (wave2_1 + wave2_3 + wave2_4)
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (pollAcc + pollFreq.c | pid),
data = d)
tab_model(PEL.m15.w2, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.98 | 2.74 – 3.21 | 24.84 | <0.001 | 1521.60 |
| pDem Rep | 0.07 | -0.05 – 0.19 | 1.19 | 0.236 | 4605.32 |
| pParty Ind | -0.35 | -0.51 – -0.19 | -4.27 | <0.001 | 5375.13 |
| pollbias z | -0.30 | -0.51 – -0.10 | -2.92 | 0.004 | 4542.28 |
| wave2 1 | -0.28 | -0.35 – -0.21 | -8.19 | <0.001 | 3758.56 |
| wave2 3 | 0.02 | -0.05 – 0.10 | 0.60 | 0.550 | 4184.91 |
| wave2 4 | -0.07 | -0.15 – 0.02 | -1.57 | 0.115 | 4264.87 |
| pollAcc | 0.09 | 0.06 – 0.11 | 7.60 | <0.001 | 903.87 |
| pollFreq c | -0.02 | -0.04 – -0.00 | -1.96 | 0.050 | 981.03 |
| vs age | 0.01 | 0.00 – 0.01 | 4.59 | <0.001 | 1631.48 |
| nonbinary mf | 0.86 | 0.30 – 1.43 | 2.99 | 0.003 | 1195.74 |
| male female | -0.38 | -0.46 – -0.30 | -8.90 | <0.001 | 1614.60 |
| vs race [Black] | -0.26 | -0.38 – -0.13 | -4.00 | <0.001 | 1656.11 |
| vs race [Hispanic] | -0.10 | -0.23 – 0.04 | -1.44 | 0.150 | 1550.91 |
| vs race [Other] | -0.12 | -0.28 – 0.04 | -1.45 | 0.148 | 1607.25 |
| pDem Rep × pollbias z | 0.30 | -0.03 – 0.64 | 1.79 | 0.073 | 4577.37 |
| pParty Ind × pollbias z | -0.41 | -0.91 – 0.09 | -1.61 | 0.108 | 5352.70 |
| pDem Rep × wave2 1 | -1.12 | -1.24 – -1.00 | -18.14 | <0.001 | 3746.59 |
| pDem Rep × wave2 3 | 0.03 | -0.10 – 0.16 | 0.48 | 0.632 | 4165.80 |
| pDem Rep × wave2 4 | 0.28 | 0.13 – 0.43 | 3.73 | <0.001 | 4276.50 |
| pParty Ind × wave2 1 | 0.07 | -0.10 – 0.25 | 0.81 | 0.418 | 3878.53 |
| pParty Ind × wave2 3 | 0.08 | -0.11 – 0.27 | 0.82 | 0.413 | 4207.35 |
| pParty Ind × wave2 4 | -0.01 | -0.22 – 0.21 | -0.06 | 0.955 | 4281.31 |
| pollbias z × wave2 1 | -0.34 | -0.55 – -0.14 | -3.26 | 0.001 | 3796.54 |
| pollbias z × wave2 3 | 0.06 | -0.17 – 0.28 | 0.49 | 0.621 | 4189.05 |
| pollbias z × wave2 4 | 0.04 | -0.22 – 0.29 | 0.27 | 0.784 | 4273.35 |
|
(pDem Rep × pollbias z) × wave2 1 |
-0.38 | -0.73 – -0.04 | -2.19 | 0.028 | 3750.20 |
|
(pDem Rep × pollbias z) × wave2 3 |
-0.18 | -0.54 – 0.19 | -0.95 | 0.342 | 4138.67 |
|
(pDem Rep × pollbias z) × wave2 4 |
-0.25 | -0.68 – 0.18 | -1.14 | 0.255 | 4277.64 |
|
(pParty Ind × pollbias z) × wave2 1 |
0.35 | -0.20 – 0.90 | 1.23 | 0.218 | 3928.29 |
|
(pParty Ind × pollbias z) × wave2 3 |
0.50 | -0.11 – 1.10 | 1.62 | 0.106 | 4208.11 |
|
(pParty Ind × pollbias z) × wave2 4 |
0.59 | -0.10 – 1.28 | 1.69 | 0.092 | 4291.56 |
| Random Effects | |||||
| σ2 | 0.50 | ||||
| τ00 pid | 0.49 | ||||
| τ11 pid.pollAcc | 0.02 | ||||
| τ11 pid.pollFreq.c | 0.01 | ||||
| ρ01 | -0.23 | ||||
| 0.14 | |||||
| ICC | 0.54 | ||||
| N pid | 1674 | ||||
| Observations | 5753 | ||||
| Marginal R2 / Conditional R2 | 0.161 / 0.615 | ||||
Wave 2-centered Model with party ID-specific Effects
PEL.m15.w2.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * pollbias.z * (wave2_1 + wave2_3 + wave2_4)
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (1|pid),
data = d)
tab_model(PEL.m15.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.05 | 2.81 – 3.29 | 24.82 | <0.001 | 1810.85 |
| pDem R | 0.07 | -0.05 – 0.19 | 1.14 | 0.253 | 4885.44 |
| pDem I | -0.33 | -0.50 – -0.16 | -3.78 | <0.001 | 5710.93 |
| pollbias z | -0.31 | -0.56 – -0.06 | -2.45 | 0.014 | 4429.49 |
| wave2 1 | 0.25 | 0.17 – 0.33 | 6.08 | <0.001 | 4088.02 |
| wave2 3 | -0.02 | -0.11 – 0.06 | -0.51 | 0.607 | 4119.70 |
| wave2 4 | -0.21 | -0.31 – -0.11 | -4.23 | <0.001 | 4231.21 |
| pollAcc | 0.08 | 0.06 – 0.10 | 8.07 | <0.001 | 5634.18 |
| pollFreq c | -0.02 | -0.04 – 0.00 | -1.88 | 0.060 | 1654.89 |
| vs age | 0.01 | 0.00 – 0.01 | 4.77 | <0.001 | 1680.37 |
| nonbinary mf | 0.86 | 0.30 – 1.42 | 2.99 | 0.003 | 1607.59 |
| male female | -0.39 | -0.47 – -0.30 | -9.00 | <0.001 | 1658.27 |
| vs race [Black] | -0.26 | -0.39 – -0.13 | -3.99 | <0.001 | 1743.24 |
| vs race [Hispanic] | -0.10 | -0.24 – 0.03 | -1.47 | 0.143 | 1677.05 |
| vs race [Other] | -0.14 | -0.30 – 0.02 | -1.67 | 0.095 | 1642.37 |
| pDem R × pollbias z | 0.29 | -0.05 – 0.62 | 1.66 | 0.097 | 4885.94 |
| pDem I × pollbias z | -0.25 | -0.79 – 0.28 | -0.93 | 0.351 | 5720.81 |
| pDem R × wave2 1 | -1.11 | -1.24 – -0.99 | -17.72 | <0.001 | 4104.90 |
| pDem R × wave2 3 | 0.04 | -0.09 – 0.17 | 0.59 | 0.557 | 4147.05 |
| pDem R × wave2 4 | 0.29 | 0.15 – 0.44 | 3.88 | <0.001 | 4250.32 |
| pDem I × wave2 1 | -0.49 | -0.68 – -0.31 | -5.15 | <0.001 | 4207.98 |
| pDem I × wave2 3 | 0.09 | -0.11 – 0.29 | 0.86 | 0.391 | 4226.79 |
| pDem I × wave2 4 | 0.12 | -0.10 – 0.35 | 1.07 | 0.287 | 4308.91 |
| pollbias z × wave2 1 | -0.27 | -0.52 – -0.01 | -2.05 | 0.040 | 4093.84 |
| pollbias z × wave2 3 | -0.01 | -0.27 – 0.25 | -0.07 | 0.945 | 4108.14 |
| pollbias z × wave2 4 | -0.02 | -0.33 – 0.30 | -0.10 | 0.923 | 4247.73 |
|
(pDem R × pollbias z) × wave2 1 |
-0.38 | -0.73 – -0.03 | -2.13 | 0.034 | 4108.58 |
|
(pDem R × pollbias z) × wave2 3 |
-0.19 | -0.55 – 0.17 | -1.03 | 0.302 | 4128.83 |
|
(pDem R × pollbias z) × wave2 4 |
-0.25 | -0.67 – 0.18 | -1.13 | 0.257 | 4252.89 |
|
(pDem I × pollbias z) × wave2 1 |
0.12 | -0.47 – 0.71 | 0.41 | 0.682 | 4255.39 |
|
(pDem I × pollbias z) × wave2 3 |
0.42 | -0.22 – 1.05 | 1.30 | 0.195 | 4234.14 |
|
(pDem I × pollbias z) × wave2 4 |
0.41 | -0.32 – 1.13 | 1.09 | 0.274 | 4313.18 |
| Random Effects | |||||
| σ2 | 0.53 | ||||
| τ00 pid | 0.57 | ||||
| ICC | 0.52 | ||||
| N pid | 1674 | ||||
| Observations | 5753 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.600 | ||||
PEL.m15.w2.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * pollbias.z * (wave2_1 + wave2_3 + wave2_4)
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (1|pid),
data = d)
tab_model(PEL.m15.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.12 | 2.88 – 3.37 | 25.15 | <0.001 | 1850.29 |
| pRep D | -0.07 | -0.19 – 0.05 | -1.14 | 0.253 | 4885.44 |
| pRep I | -0.40 | -0.57 – -0.22 | -4.48 | <0.001 | 5714.14 |
| pollbias z | -0.03 | -0.26 – 0.20 | -0.24 | 0.809 | 4301.34 |
| wave2 1 | -0.86 | -0.95 – -0.77 | -18.31 | <0.001 | 4096.41 |
| wave2 3 | 0.02 | -0.08 – 0.11 | 0.33 | 0.741 | 4153.25 |
| wave2 4 | 0.08 | -0.03 – 0.19 | 1.45 | 0.148 | 4255.72 |
| pollAcc | 0.08 | 0.06 – 0.10 | 8.07 | <0.001 | 5634.18 |
| pollFreq c | -0.02 | -0.04 – 0.00 | -1.88 | 0.060 | 1654.89 |
| vs age | 0.01 | 0.00 – 0.01 | 4.77 | <0.001 | 1680.37 |
| nonbinary mf | 0.86 | 0.30 – 1.42 | 2.99 | 0.003 | 1607.59 |
| male female | -0.39 | -0.47 – -0.30 | -9.00 | <0.001 | 1658.27 |
| vs race [Black] | -0.26 | -0.39 – -0.13 | -3.99 | <0.001 | 1743.24 |
| vs race [Hispanic] | -0.10 | -0.24 – 0.03 | -1.47 | 0.143 | 1677.05 |
| vs race [Other] | -0.14 | -0.30 – 0.02 | -1.67 | 0.095 | 1642.37 |
| pRep D × pollbias z | -0.29 | -0.62 – 0.05 | -1.66 | 0.097 | 4885.94 |
| pRep I × pollbias z | -0.54 | -1.07 – -0.01 | -2.01 | 0.045 | 5720.92 |
| pRep D × wave2 1 | 1.11 | 0.99 – 1.24 | 17.72 | <0.001 | 4104.90 |
| pRep D × wave2 3 | -0.04 | -0.17 – 0.09 | -0.59 | 0.557 | 4147.05 |
| pRep D × wave2 4 | -0.29 | -0.44 – -0.15 | -3.88 | <0.001 | 4250.32 |
| pRep I × wave2 1 | 0.62 | 0.43 – 0.81 | 6.30 | <0.001 | 4199.89 |
| pRep I × wave2 3 | 0.05 | -0.16 – 0.26 | 0.47 | 0.635 | 4228.32 |
| pRep I × wave2 4 | -0.17 | -0.40 – 0.06 | -1.44 | 0.149 | 4316.10 |
| pollbias z × wave2 1 | -0.65 | -0.89 – -0.41 | -5.28 | <0.001 | 4092.40 |
| pollbias z × wave2 3 | -0.20 | -0.45 – 0.05 | -1.57 | 0.115 | 4140.43 |
| pollbias z × wave2 4 | -0.26 | -0.55 – 0.03 | -1.78 | 0.075 | 4249.10 |
|
(pRep D × pollbias z) × wave2 1 |
0.38 | 0.03 – 0.73 | 2.13 | 0.034 | 4108.58 |
|
(pRep D × pollbias z) × wave2 3 |
0.19 | -0.17 – 0.55 | 1.03 | 0.302 | 4128.83 |
|
(pRep D × pollbias z) × wave2 4 |
0.25 | -0.18 – 0.67 | 1.13 | 0.257 | 4252.89 |
|
(pRep I × pollbias z) × wave2 1 |
0.50 | -0.08 – 1.09 | 1.69 | 0.091 | 4241.95 |
|
(pRep I × pollbias z) × wave2 3 |
0.61 | -0.02 – 1.24 | 1.90 | 0.057 | 4238.88 |
|
(pRep I × pollbias z) × wave2 4 |
0.65 | -0.06 – 1.37 | 1.78 | 0.074 | 4316.08 |
| Random Effects | |||||
| σ2 | 0.53 | ||||
| τ00 pid | 0.57 | ||||
| ICC | 0.52 | ||||
| N pid | 1674 | ||||
| Observations | 5753 | ||||
| Marginal R2 / Conditional R2 | 0.163 / 0.600 | ||||
b. Geographic Norms
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = per_dem,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm",
fullrange = T) +
facet_wrap(~wave) +
labs(x = "Percentage of County voting Democratic",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(10,95)) +
scale_x_continuous(breaks = seq(0,100, 10)) 
ggplot(d[!is.na(d$party_factor),],
aes(x = per_gop,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(alpha = .2, size = .2) +
geom_smooth(method = "lm",
fullrange = T, se = F) +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Percentage of County voting for Donald Trump",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","black","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","black","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(from = 0, to = 90, length.out = 7))
ggplot(d[!is.na(d$party_factor),],
aes(x = per_gop,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm",
fullrange = T) +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Percentage of County voting for Donald Trump",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(from = 0, to = 90, length.out = 7))
Summary
Geographic social norms, as operationalized by percentage of county of residence that voted for Donald Trump, also influenced perceived election legitimacy. The overall influence of Trump’s vote share was negative (b = -0.005, 95% CI = [-0.01, -0.001], t(3023.6) = -2.92, p = .003), though this interacted with partisan identity (b = 0.01, 95% CI = [0.001, 0.01], t(3706.0) = 2.08, p = .038), such that Trump’s vote share negatively predicted perceived election legitimacy for Democrats (b = -0.01, 95% CI = [-0.01, -0.001], t(2935.3) = -4.15, p < .001), but was not significant for Republicans (p = .43). The change in impact of Trump’s vote share on perceived election legitimacy from wave 1 to wave 2 also differed by partisan identity (b = -0.02, 95% CI = [-0.02, -0.01], t(5462.7) = -4.08, p < .001), such that impact of Trump’s vote share on perceived election legitimacy became stronger and more negative for Democrats (b = -0.01, 95% CI = [-0.001, -0.01], t(5462.7) = -2.04, p = .041), while the impact became less negative–and indeed, appears to have attenuated from negative (b = -0.01, 95% CI = [-0.02, -0.001], t(5796.4) = -3.26, p = .001) in wave 1 to nonsignificance in wave 2 (p = .64)–for Republicans (b = 0.01, 95% CI = [0.01, 0.02], t(5459.0) = 3.60, p < .001).
Models
Main Model
PEL.m12 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * per_gop * (wave.lin + wave.quad + wave.cub)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.15 | 2.89 – 3.41 | 24.04 | <0.001 | 2727.43 |
| pDem Rep | -0.48 | -0.79 – -0.18 | -3.11 | 0.002 | 3810.30 |
| pParty Ind | -0.41 | -0.78 – -0.04 | -2.20 | 0.028 | 5914.15 |
| per gop | -0.00 | -0.01 – -0.00 | -2.92 | 0.004 | 3023.57 |
| wave lin | 0.16 | -0.07 – 0.40 | 1.40 | 0.163 | 5662.75 |
| wave quad | 0.09 | -0.08 – 0.26 | 1.01 | 0.314 | 5382.68 |
| wave cub | -0.10 | -0.30 – 0.11 | -0.92 | 0.356 | 5323.34 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pDem Rep × per gop | 0.01 | 0.00 – 0.01 | 2.08 | 0.038 | 3705.64 |
| pParty Ind × per gop | 0.00 | -0.01 – 0.01 | 0.12 | 0.905 | 5900.40 |
| pDem Rep × wave lin | 0.60 | 0.17 – 1.02 | 2.77 | 0.006 | 5592.23 |
| pDem Rep × wave quad | 0.11 | -0.20 – 0.43 | 0.70 | 0.485 | 5376.19 |
| pDem Rep × wave cub | -0.34 | -0.72 – 0.04 | -1.74 | 0.082 | 5304.18 |
| pParty Ind × wave lin | 0.16 | -0.44 – 0.75 | 0.51 | 0.607 | 5718.56 |
| pParty Ind × wave quad | -0.01 | -0.44 – 0.43 | -0.03 | 0.975 | 5451.94 |
| pParty Ind × wave cub | 0.07 | -0.47 – 0.60 | 0.24 | 0.809 | 5413.92 |
| per gop × wave lin | 0.00 | -0.00 – 0.00 | 0.06 | 0.954 | 5651.15 |
| per gop × wave quad | 0.00 | -0.00 – 0.01 | 1.71 | 0.087 | 5376.06 |
| per gop × wave cub | -0.00 | -0.00 – 0.00 | -0.03 | 0.973 | 5316.96 |
|
(pDem Rep × per gop) × wave lin |
0.01 | 0.00 – 0.02 | 2.90 | 0.004 | 5582.82 |
|
(pDem Rep × per gop) × wave quad |
0.01 | 0.00 – 0.01 | 2.67 | 0.008 | 5372.44 |
|
(pDem Rep × per gop) × wave cub |
-0.01 | -0.01 – 0.00 | -1.62 | 0.106 | 5301.40 |
|
(pParty Ind × per gop) × wave lin |
-0.00 | -0.01 – 0.01 | -0.37 | 0.709 | 5707.37 |
|
(pParty Ind × per gop) × wave quad |
0.00 | -0.01 – 0.01 | 0.14 | 0.890 | 5443.31 |
|
(pParty Ind × per gop) × wave cub |
0.00 | -0.01 – 0.01 | 0.04 | 0.965 | 5404.78 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
Party ID-specific Effects
PEL.m12.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * per_gop * (wave.lin + wave.quad + wave.cub)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1 | pid),
data = d)
tab_model(PEL.m12.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.53 | 3.26 – 3.80 | 25.50 | <0.001 | 2704.92 |
| pDem R | -0.48 | -0.79 – -0.18 | -3.11 | 0.002 | 3810.30 |
| pDem I | -0.65 | -1.04 – -0.27 | -3.32 | 0.001 | 5416.33 |
| per gop | -0.01 | -0.01 – -0.00 | -4.15 | <0.001 | 2935.25 |
| wave lin | -0.19 | -0.44 – 0.06 | -1.47 | 0.142 | 5545.80 |
| wave quad | 0.03 | -0.15 – 0.22 | 0.36 | 0.721 | 5364.27 |
| wave cub | 0.05 | -0.18 – 0.27 | 0.43 | 0.668 | 5271.79 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pDem R × per gop | 0.01 | 0.00 – 0.01 | 2.08 | 0.038 | 3705.64 |
| pDem I × per gop | 0.00 | -0.00 – 0.01 | 0.92 | 0.356 | 5382.16 |
| pDem R × wave lin | 0.60 | 0.17 – 1.02 | 2.77 | 0.006 | 5592.23 |
| pDem R × wave quad | 0.11 | -0.20 – 0.43 | 0.70 | 0.485 | 5376.19 |
| pDem R × wave cub | -0.34 | -0.72 – 0.04 | -1.74 | 0.082 | 5304.18 |
| pDem I × wave lin | 0.45 | -0.15 – 1.06 | 1.47 | 0.142 | 5707.58 |
| pDem I × wave quad | 0.05 | -0.40 – 0.50 | 0.21 | 0.831 | 5445.41 |
| pDem I × wave cub | -0.10 | -0.65 – 0.44 | -0.37 | 0.712 | 5394.95 |
| per gop × wave lin | -0.01 | -0.01 – -0.00 | -2.02 | 0.044 | 5535.20 |
| per gop × wave quad | -0.00 | -0.01 – 0.00 | -0.75 | 0.452 | 5360.04 |
| per gop × wave cub | 0.00 | -0.00 – 0.01 | 1.25 | 0.213 | 5270.78 |
|
(pDem R × per gop) × wave lin |
0.01 | 0.00 – 0.02 | 2.90 | 0.004 | 5582.82 |
|
(pDem R × per gop) × wave quad |
0.01 | 0.00 – 0.01 | 2.67 | 0.008 | 5372.44 |
|
(pDem R × per gop) × wave cub |
-0.01 | -0.01 – 0.00 | -1.62 | 0.106 | 5301.40 |
|
(pDem I × per gop) × wave lin |
0.00 | -0.01 – 0.02 | 0.63 | 0.528 | 5691.41 |
|
(pDem I × per gop) × wave quad |
0.00 | -0.00 – 0.01 | 1.05 | 0.292 | 5437.07 |
|
(pDem I × per gop) × wave cub |
-0.00 | -0.01 – 0.01 | -0.52 | 0.607 | 5385.94 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
PEL.m12.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * per_gop * (wave.lin + wave.quad + wave.cub)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.05 | 2.73 – 3.36 | 18.84 | <0.001 | 3054.31 |
| pRep D | 0.48 | 0.18 – 0.79 | 3.11 | 0.002 | 3810.30 |
| pRep I | -0.17 | -0.58 – 0.24 | -0.81 | 0.417 | 5723.59 |
| per gop | -0.00 | -0.01 – 0.00 | -0.79 | 0.432 | 3346.63 |
| wave lin | 0.41 | 0.07 – 0.75 | 2.36 | 0.018 | 5599.78 |
| wave quad | 0.15 | -0.11 – 0.40 | 1.13 | 0.258 | 5370.56 |
| wave cub | -0.29 | -0.59 – 0.02 | -1.85 | 0.065 | 5305.28 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pRep D × per gop | -0.01 | -0.01 – -0.00 | -2.08 | 0.038 | 3705.64 |
| pRep I × per gop | -0.00 | -0.01 – 0.01 | -0.67 | 0.501 | 5701.75 |
| pRep D × wave lin | -0.60 | -1.02 – -0.17 | -2.77 | 0.006 | 5592.23 |
| pRep D × wave quad | -0.11 | -0.43 – 0.20 | -0.70 | 0.485 | 5376.19 |
| pRep D × wave cub | 0.34 | -0.04 – 0.72 | 1.74 | 0.082 | 5304.18 |
| pRep I × wave lin | -0.14 | -0.79 – 0.51 | -0.43 | 0.666 | 5701.37 |
| pRep I × wave quad | -0.06 | -0.54 – 0.42 | -0.26 | 0.797 | 5441.40 |
| pRep I × wave cub | 0.23 | -0.35 – 0.82 | 0.78 | 0.434 | 5407.28 |
| per gop × wave lin | 0.01 | 0.00 – 0.01 | 2.11 | 0.035 | 5597.04 |
| per gop × wave quad | 0.01 | 0.00 – 0.01 | 2.81 | 0.005 | 5368.47 |
| per gop × wave cub | -0.00 | -0.01 – 0.00 | -1.08 | 0.279 | 5304.53 |
|
(pRep D × per gop) × wave lin |
-0.01 | -0.02 – -0.00 | -2.90 | 0.004 | 5582.82 |
|
(pRep D × per gop) × wave quad |
-0.01 | -0.01 – -0.00 | -2.67 | 0.008 | 5372.44 |
|
(pRep D × per gop) × wave cub |
0.01 | -0.00 – 0.01 | 1.62 | 0.106 | 5301.40 |
|
(pRep I × per gop) × wave lin |
-0.01 | -0.02 – 0.00 | -1.28 | 0.199 | 5695.45 |
|
(pRep I × per gop) × wave quad |
-0.00 | -0.01 – 0.01 | -0.75 | 0.455 | 5433.69 |
|
(pRep I × per gop) × wave cub |
0.00 | -0.01 – 0.01 | 0.57 | 0.571 | 5399.65 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
Main Model centered at Wave 2 with by party ID-specific Effects
PEL.m12.w2 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * per_gop * (wave2_1 + wave2_3 + wave2_4)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12.w2, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.20 | 2.92 – 3.49 | 22.01 | <0.001 | 3909.44 |
| pDem Rep | -0.41 | -0.79 – -0.03 | -2.10 | 0.036 | 6598.05 |
| pParty Ind | -0.49 | -0.97 – 0.00 | -1.95 | 0.051 | 7567.17 |
| per gop | -0.00 | -0.01 – 0.00 | -1.65 | 0.098 | 5680.62 |
| wave2 1 | -0.20 | -0.41 – 0.00 | -1.94 | 0.052 | 5486.30 |
| wave2 3 | -0.02 | -0.24 – 0.21 | -0.13 | 0.895 | 5347.11 |
| wave2 4 | 0.01 | -0.26 – 0.28 | 0.08 | 0.933 | 5452.44 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pDem Rep × per gop | 0.01 | 0.00 – 0.02 | 2.75 | 0.006 | 6500.76 |
| pParty Ind × per gop | 0.00 | -0.01 – 0.01 | 0.24 | 0.809 | 7564.57 |
| pDem Rep × wave2 1 | -0.51 | -0.91 – -0.12 | -2.56 | 0.010 | 5472.30 |
| pDem Rep × wave2 3 | -0.04 | -0.45 – 0.38 | -0.18 | 0.856 | 5294.31 |
| pDem Rep × wave2 4 | 0.25 | -0.23 – 0.73 | 1.03 | 0.304 | 5414.50 |
| pParty Ind × wave2 1 | 0.02 | -0.50 – 0.54 | 0.07 | 0.947 | 5618.41 |
| pParty Ind × wave2 3 | 0.14 | -0.44 – 0.73 | 0.48 | 0.631 | 5435.10 |
| pParty Ind × wave2 4 | 0.14 | -0.55 – 0.83 | 0.40 | 0.690 | 5499.67 |
| per gop × wave2 1 | -0.00 | -0.01 – 0.00 | -1.47 | 0.141 | 5471.25 |
| per gop × wave2 3 | -0.00 | -0.00 – 0.00 | -0.00 | 0.999 | 5337.03 |
| per gop × wave2 4 | -0.00 | -0.01 – 0.00 | -1.06 | 0.290 | 5448.07 |
|
(pDem Rep × per gop) × wave2 1 |
-0.02 | -0.02 – -0.01 | -4.08 | <0.001 | 5462.69 |
|
(pDem Rep × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.01 | 0.994 | 5289.97 |
|
(pDem Rep × per gop) × wave2 4 |
-0.00 | -0.01 – 0.01 | -0.15 | 0.880 | 5410.01 |
|
(pParty Ind × per gop) × wave2 1 |
0.00 | -0.01 – 0.01 | 0.02 | 0.981 | 5596.45 |
|
(pParty Ind × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.15 | 0.880 | 5423.28 |
|
(pParty Ind × per gop) × wave2 4 |
-0.00 | -0.02 – 0.01 | -0.32 | 0.749 | 5497.37 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
PEL.m12.w2.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * per_gop * (wave2_1 + wave2_3 + wave2_4)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.57 | 3.26 – 3.87 | 22.87 | <0.001 | 4110.65 |
| pDem R | -0.41 | -0.79 – -0.03 | -2.10 | 0.036 | 6598.05 |
| pDem I | -0.69 | -1.20 – -0.18 | -2.65 | 0.008 | 7482.89 |
| per gop | -0.01 | -0.01 – -0.00 | -3.68 | <0.001 | 5665.29 |
| wave2 1 | 0.05 | -0.18 – 0.28 | 0.42 | 0.676 | 5438.64 |
| wave2 3 | -0.04 | -0.29 – 0.20 | -0.35 | 0.725 | 5239.14 |
| wave2 4 | -0.16 | -0.45 – 0.13 | -1.10 | 0.271 | 5378.10 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pDem R × per gop | 0.01 | 0.00 – 0.02 | 2.75 | 0.006 | 6500.76 |
| pDem I × per gop | 0.01 | -0.00 – 0.02 | 1.25 | 0.210 | 7471.88 |
| pDem R × wave2 1 | -0.51 | -0.91 – -0.12 | -2.56 | 0.010 | 5472.30 |
| pDem R × wave2 3 | -0.04 | -0.45 – 0.38 | -0.18 | 0.856 | 5294.31 |
| pDem R × wave2 4 | 0.25 | -0.23 – 0.73 | 1.03 | 0.304 | 5414.50 |
| pDem I × wave2 1 | -0.24 | -0.77 – 0.29 | -0.88 | 0.377 | 5600.64 |
| pDem I × wave2 3 | 0.12 | -0.48 – 0.72 | 0.41 | 0.684 | 5412.82 |
| pDem I × wave2 4 | 0.27 | -0.44 – 0.97 | 0.74 | 0.460 | 5489.29 |
| per gop × wave2 1 | 0.01 | 0.00 – 0.01 | 2.04 | 0.041 | 5421.34 |
| per gop × wave2 3 | 0.00 | -0.00 – 0.01 | 0.12 | 0.904 | 5240.84 |
| per gop × wave2 4 | -0.00 | -0.01 – 0.00 | -0.58 | 0.562 | 5378.50 |
|
(pDem R × per gop) × wave2 1 |
-0.02 | -0.02 – -0.01 | -4.08 | <0.001 | 5462.69 |
|
(pDem R × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.01 | 0.994 | 5289.97 |
|
(pDem R × per gop) × wave2 4 |
-0.00 | -0.01 – 0.01 | -0.15 | 0.880 | 5410.01 |
|
(pDem I × per gop) × wave2 1 |
-0.01 | -0.02 – 0.00 | -1.46 | 0.143 | 5575.09 |
|
(pDem I × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.15 | 0.881 | 5401.17 |
|
(pDem I × per gop) × wave2 4 |
-0.00 | -0.02 – 0.01 | -0.36 | 0.719 | 5485.47 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
PEL.m12.w2.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * per_gop * (wave2_1 + wave2_3 + wave2_4)
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.16 | 2.80 – 3.52 | 17.02 | <0.001 | 4844.95 |
| pRep D | 0.41 | 0.03 – 0.79 | 2.10 | 0.036 | 6598.05 |
| pRep I | -0.28 | -0.82 – 0.26 | -1.02 | 0.306 | 7544.82 |
| per gop | 0.00 | -0.00 – 0.01 | 0.47 | 0.639 | 6084.76 |
| wave2 1 | -0.46 | -0.78 – -0.15 | -2.89 | 0.004 | 5459.44 |
| wave2 3 | -0.08 | -0.42 – 0.25 | -0.48 | 0.629 | 5315.75 |
| wave2 4 | 0.09 | -0.29 – 0.48 | 0.46 | 0.644 | 5428.90 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pRep D × per gop | -0.01 | -0.02 – -0.00 | -2.75 | 0.006 | 6500.76 |
| pRep I × per gop | -0.00 | -0.01 – 0.01 | -0.76 | 0.446 | 7539.08 |
| pRep D × wave2 1 | 0.51 | 0.12 – 0.91 | 2.56 | 0.010 | 5472.30 |
| pRep D × wave2 3 | 0.04 | -0.38 – 0.45 | 0.18 | 0.856 | 5294.31 |
| pRep D × wave2 4 | -0.25 | -0.73 – 0.23 | -1.03 | 0.304 | 5414.50 |
| pRep I × wave2 1 | 0.27 | -0.30 – 0.85 | 0.94 | 0.349 | 5599.42 |
| pRep I × wave2 3 | 0.16 | -0.48 – 0.81 | 0.50 | 0.620 | 5425.13 |
| pRep I × wave2 4 | 0.01 | -0.74 – 0.77 | 0.04 | 0.971 | 5491.38 |
| per gop × wave2 1 | -0.01 | -0.02 – -0.01 | -3.60 | <0.001 | 5459.04 |
| per gop × wave2 3 | 0.00 | -0.01 – 0.01 | 0.09 | 0.932 | 5311.98 |
| per gop × wave2 4 | -0.00 | -0.01 – 0.00 | -0.66 | 0.512 | 5426.13 |
|
(pRep D × per gop) × wave2 1 |
0.02 | 0.01 – 0.02 | 4.08 | <0.001 | 5462.69 |
|
(pRep D × per gop) × wave2 3 |
0.00 | -0.01 – 0.01 | 0.01 | 0.994 | 5289.97 |
|
(pRep D × per gop) × wave2 4 |
0.00 | -0.01 – 0.01 | 0.15 | 0.880 | 5410.01 |
|
(pRep I × per gop) × wave2 1 |
0.01 | -0.00 – 0.02 | 1.42 | 0.155 | 5583.80 |
|
(pRep I × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.14 | 0.892 | 5414.88 |
|
(pRep I × per gop) × wave2 4 |
-0.00 | -0.02 – 0.01 | -0.25 | 0.805 | 5490.36 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
PEL.m12.w1.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * per_gop * wave
+ vs_age
+ vs_race
+ nonbinary_mf
+ male_female
+ (1|pid),
data = d)
tab_model(PEL.m12.w1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.70 | 2.33 – 3.06 | 14.58 | <0.001 | 4650.47 |
| pRep D | 0.92 | 0.55 – 1.29 | 4.87 | <0.001 | 6267.99 |
| pRep I | -0.01 | -0.49 – 0.48 | -0.03 | 0.978 | 7320.51 |
| per gop | -0.01 | -0.02 – -0.00 | -3.26 | 0.001 | 5796.36 |
| wave [2] | 0.46 | 0.15 – 0.78 | 2.89 | 0.004 | 5459.44 |
| wave [3] | 0.38 | 0.05 – 0.72 | 2.23 | 0.026 | 5507.50 |
| wave [4] | 0.56 | 0.17 – 0.94 | 2.80 | 0.005 | 5581.30 |
| vs age | 0.01 | 0.00 – 0.01 | 5.24 | <0.001 | 2411.86 |
| vs race [Black] | -0.23 | -0.35 – -0.11 | -3.89 | <0.001 | 2481.64 |
| vs race [Hispanic] | -0.04 | -0.16 – 0.08 | -0.61 | 0.541 | 2440.49 |
| vs race [Other] | -0.12 | -0.26 – 0.02 | -1.62 | 0.104 | 2383.97 |
| nonbinary mf | 0.52 | 0.07 – 0.98 | 2.25 | 0.024 | 2544.11 |
| male female | -0.37 | -0.45 – -0.30 | -9.62 | <0.001 | 2391.69 |
| pRep D × per gop | 0.01 | -0.00 – 0.01 | 1.48 | 0.140 | 6166.89 |
| pRep I × per gop | 0.00 | -0.01 – 0.01 | 0.83 | 0.405 | 7319.19 |
| pRep D × wave [2] | -0.51 | -0.91 – -0.12 | -2.56 | 0.010 | 5472.30 |
| pRep D × wave [3] | -0.48 | -0.89 – -0.06 | -2.25 | 0.025 | 5511.36 |
| pRep D × wave [4] | -0.77 | -1.25 – -0.28 | -3.11 | 0.002 | 5588.22 |
| pRep I × wave [2] | -0.27 | -0.85 – 0.30 | -0.94 | 0.349 | 5599.42 |
| pRep I × wave [3] | -0.11 | -0.73 – 0.51 | -0.35 | 0.724 | 5640.53 |
| pRep I × wave [4] | -0.26 | -1.00 – 0.48 | -0.69 | 0.489 | 5678.41 |
| per gop × wave [2] | 0.01 | 0.01 – 0.02 | 3.60 | <0.001 | 5459.04 |
| per gop × wave [3] | 0.01 | 0.00 – 0.02 | 3.46 | 0.001 | 5503.48 |
| per gop × wave [4] | 0.01 | 0.00 – 0.02 | 2.28 | 0.023 | 5580.93 |
|
(pRep D × per gop) × wave [2] |
-0.02 | -0.02 – -0.01 | -4.08 | <0.001 | 5462.69 |
|
(pRep D × per gop) × wave [3] |
-0.02 | -0.02 – -0.01 | -3.87 | <0.001 | 5501.94 |
|
(pRep D × per gop) × wave [4] |
-0.02 | -0.03 – -0.01 | -3.18 | 0.001 | 5580.23 |
|
(pRep I × per gop) × wave [2] |
-0.01 | -0.02 – 0.00 | -1.42 | 0.155 | 5583.80 |
|
(pRep I × per gop) × wave [3] |
-0.01 | -0.02 – 0.00 | -1.46 | 0.144 | 5625.56 |
|
(pRep I × per gop) × wave [4] |
-0.01 | -0.02 – 0.00 | -1.35 | 0.177 | 5673.77 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 2456 | ||||
| Observations | 7616 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.613 | ||||
c. Media vs. Geography Comparison
PEL.m.normplot <- lmer(voteconfidence ~ party_factor * pollbias.z * wave * per_gop
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (1|pid),
data = d)
ggplot(d[!is.na(d$party_factor),],
aes(x = per_gop,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm",
fullrange = T) +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Percentage of County voting for Donald Trump",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(from = 0, to = 90, length.out = 7))
Summary
In a model that tested both geographic and media-based social norms together, the effect of both polling bias (b = -0.72, 95% CI = [-1.35, -0.08], t(2192.9) = -2.21, p = .027) and Donald Trump’s vote share (b = -0.01, 95% CI = [-0.01, -0.001], t(2078.5) = -2.60, p = .009) remained significant. However, the higher-order interactions involving polling bias, partisan identity, and time were no longer significant, while the three-way interaction of Trump’s vote share, partisan identity, and change over quadratic time retained significance (b = 0.01, 95% CI = [0.001, 0.02], t(3654.0) = 2.44, p = .015). Again, this change primarily occurred from wave 1 to wave 2 (b = 0.02, 95% CI = [0.01, 0.03], t(3529.7) = 3.69, p < .001). This indicates that the effect of county-level partisan norms, along with their higher-order interactions, retained explanatory power when controlling for polling bias and its higher-order interactions, while this was less true for polling bias when controlling for county-level norms.
Models
Main Model
PEL.m16 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollbias.z * per_gop * (wave.lin + wave.quad + wave.cub)
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (pollAcc + pollFreq.c | pid),
data = d)
tab_model(PEL.m16, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.15 | 2.84 – 3.47 | 19.55 | <0.001 | 1686.86 |
| pDem Rep | -0.36 | -0.74 – 0.01 | -1.92 | 0.055 | 2398.00 |
| pParty Ind | -0.17 | -0.63 – 0.29 | -0.73 | 0.466 | 4035.01 |
| pollbias z | -0.72 | -1.35 – -0.08 | -2.21 | 0.027 | 2192.92 |
| per gop | -0.01 | -0.01 – -0.00 | -2.60 | 0.009 | 2078.46 |
| wave lin | 0.15 | -0.15 – 0.45 | 0.97 | 0.334 | 4077.70 |
| wave quad | 0.01 | -0.20 – 0.23 | 0.11 | 0.912 | 3649.93 |
| wave cub | 0.01 | -0.25 – 0.28 | 0.09 | 0.928 | 3718.87 |
| pollAcc | 0.08 | 0.06 – 0.11 | 7.11 | <0.001 | 839.99 |
| pollFreq c | -0.02 | -0.04 – -0.00 | -2.23 | 0.026 | 911.08 |
| vs age | 0.01 | 0.00 – 0.01 | 4.34 | <0.001 | 1529.28 |
| nonbinary mf | 0.96 | 0.36 – 1.56 | 3.16 | 0.002 | 1249.77 |
| male female | -0.37 | -0.46 – -0.29 | -8.50 | <0.001 | 1511.51 |
| vs race [Black] | -0.25 | -0.38 – -0.12 | -3.78 | <0.001 | 1544.66 |
| vs race [Hispanic] | -0.09 | -0.23 – 0.05 | -1.31 | 0.189 | 1472.33 |
| vs race [Other] | -0.15 | -0.31 – 0.02 | -1.76 | 0.078 | 1510.85 |
| pDem Rep × pollbias z | -0.20 | -1.28 – 0.88 | -0.37 | 0.715 | 2344.60 |
| pParty Ind × pollbias z | 0.16 | -1.27 – 1.60 | 0.22 | 0.823 | 4062.53 |
| pDem Rep × per gop | 0.01 | -0.00 – 0.01 | 1.45 | 0.147 | 2329.75 |
| pParty Ind × per gop | -0.00 | -0.01 – 0.01 | -0.54 | 0.587 | 3933.05 |
| pollbias z × per gop | 0.01 | -0.01 – 0.02 | 1.15 | 0.251 | 2184.27 |
| pDem Rep × wave lin | 0.59 | 0.02 – 1.15 | 2.04 | 0.041 | 4078.28 |
| pDem Rep × wave quad | -0.07 | -0.47 – 0.33 | -0.33 | 0.743 | 3663.14 |
| pDem Rep × wave cub | -0.16 | -0.65 – 0.32 | -0.67 | 0.503 | 3748.78 |
| pParty Ind × wave lin | 0.19 | -0.58 – 0.96 | 0.49 | 0.624 | 4116.25 |
| pParty Ind × wave quad | -0.11 | -0.66 – 0.45 | -0.37 | 0.710 | 3736.38 |
| pParty Ind × wave cub | 0.10 | -0.58 – 0.79 | 0.29 | 0.773 | 3791.52 |
| pollbias z × wave lin | -0.08 | -1.04 – 0.88 | -0.17 | 0.864 | 4010.97 |
| pollbias z × wave quad | 0.13 | -0.56 – 0.81 | 0.36 | 0.715 | 3697.03 |
| pollbias z × wave cub | -0.38 | -1.21 – 0.44 | -0.91 | 0.362 | 3751.48 |
| per gop × wave lin | 0.00 | -0.01 – 0.01 | 0.20 | 0.839 | 4064.22 |
| per gop × wave quad | 0.00 | -0.00 – 0.01 | 1.53 | 0.126 | 3659.16 |
| per gop × wave cub | -0.00 | -0.01 – 0.00 | -0.59 | 0.552 | 3720.26 |
|
(pDem Rep × pollbias z) × per gop |
0.01 | -0.02 – 0.03 | 0.51 | 0.612 | 2299.95 |
|
(pParty Ind × pollbias z) × per gop |
-0.00 | -0.03 – 0.02 | -0.32 | 0.745 | 3950.24 |
|
(pDem Rep × pollbias z) × wave lin |
0.44 | -1.17 – 2.06 | 0.54 | 0.591 | 4062.54 |
|
(pDem Rep × pollbias z) × wave quad |
0.43 | -0.72 – 1.58 | 0.73 | 0.466 | 3678.84 |
|
(pDem Rep × pollbias z) × wave cub |
-1.18 | -2.56 – 0.20 | -1.67 | 0.094 | 3768.24 |
|
(pParty Ind × pollbias z) × wave lin |
-0.44 | -2.96 – 2.09 | -0.34 | 0.735 | 4062.41 |
|
(pParty Ind × pollbias z) × wave quad |
-1.60 | -3.40 – 0.21 | -1.73 | 0.084 | 3776.73 |
|
(pParty Ind × pollbias z) × wave cub |
0.78 | -1.43 – 2.98 | 0.69 | 0.488 | 3819.52 |
|
(pDem Rep × per gop) × wave lin |
0.01 | -0.00 – 0.02 | 1.94 | 0.053 | 4026.72 |
|
(pDem Rep × per gop) × wave quad |
0.01 | 0.00 – 0.02 | 2.44 | 0.015 | 3654.03 |
|
(pDem Rep × per gop) × wave cub |
-0.01 | -0.02 – 0.00 | -1.59 | 0.113 | 3738.11 |
|
(pParty Ind × per gop) × wave lin |
-0.00 | -0.02 – 0.01 | -0.49 | 0.626 | 4113.98 |
|
(pParty Ind × per gop) × wave quad |
0.00 | -0.01 – 0.01 | 0.47 | 0.641 | 3746.93 |
|
(pParty Ind × per gop) × wave cub |
-0.00 | -0.01 – 0.01 | -0.10 | 0.924 | 3787.23 |
|
(pollbias z × per gop) × wave lin |
0.01 | -0.01 – 0.03 | 0.82 | 0.411 | 4023.78 |
|
(pollbias z × per gop) × wave quad |
0.00 | -0.01 – 0.02 | 0.26 | 0.792 | 3704.07 |
|
(pollbias z × per gop) × wave cub |
0.01 | -0.01 – 0.02 | 0.64 | 0.520 | 3747.40 |
|
(pDem Rep × pollbias z × per gop) × wave lin |
-0.01 | -0.04 – 0.02 | -0.53 | 0.596 | 4013.35 |
|
(pDem Rep × pollbias z × per gop) × wave quad |
-0.00 | -0.03 – 0.02 | -0.20 | 0.839 | 3670.38 |
|
(pDem Rep × pollbias z × per gop) × wave cub |
0.02 | -0.01 – 0.05 | 1.40 | 0.162 | 3758.51 |
|
(pParty Ind × pollbias z × per gop) × wave lin |
0.01 | -0.04 – 0.07 | 0.55 | 0.584 | 4080.44 |
|
(pParty Ind × pollbias z × per gop) × wave quad |
0.03 | -0.00 – 0.07 | 1.69 | 0.090 | 3785.53 |
|
(pParty Ind × pollbias z × per gop) × wave cub |
-0.01 | -0.05 – 0.03 | -0.45 | 0.650 | 3809.99 |
| Random Effects | |||||
| σ2 | 0.50 | ||||
| τ00 pid | 0.50 | ||||
| τ11 pid.pollAcc | 0.02 | ||||
| τ11 pid.pollFreq.c | 0.01 | ||||
| ρ01 | -0.23 | ||||
| 0.17 | |||||
| ICC | 0.54 | ||||
| N pid | 1578 | ||||
| Observations | 5428 | ||||
| Marginal R2 / Conditional R2 | 0.168 / 0.619 | ||||
Main Model centered at Wave 2
PEL.m16.w2 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollbias.z * per_gop * (wave2_1 + wave2_3 + wave2_4)
+ pollAcc
+ pollFreq.c
+ vs_age
+ nonbinary_mf
+ male_female
+ vs_race
+ (pollAcc + pollFreq.c|pid),
data = d)
tab_model(PEL.m16.w2, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.12 | 2.76 – 3.47 | 17.20 | <0.001 | 2526.09 |
| pDem Rep | -0.46 | -0.93 – 0.00 | -1.94 | 0.052 | 4099.64 |
| pParty Ind | -0.32 | -0.95 – 0.31 | -1.00 | 0.316 | 5146.64 |
| pollbias z | -0.44 | -1.27 – 0.38 | -1.05 | 0.294 | 4262.75 |
| per gop | -0.00 | -0.01 – 0.00 | -1.20 | 0.232 | 3979.30 |
| wave2 1 | -0.04 | -0.30 – 0.22 | -0.30 | 0.764 | 3577.67 |
| wave2 3 | 0.09 | -0.20 – 0.38 | 0.58 | 0.559 | 3957.17 |
| wave2 4 | 0.10 | -0.24 – 0.45 | 0.58 | 0.563 | 4011.19 |
| pollAcc | 0.08 | 0.06 – 0.11 | 7.11 | <0.001 | 839.99 |
| pollFreq c | -0.02 | -0.04 – -0.00 | -2.23 | 0.026 | 911.08 |
| vs age | 0.01 | 0.00 – 0.01 | 4.34 | <0.001 | 1529.28 |
| nonbinary mf | 0.96 | 0.36 – 1.56 | 3.16 | 0.002 | 1249.77 |
| male female | -0.37 | -0.46 – -0.29 | -8.50 | <0.001 | 1511.51 |
| vs race [Black] | -0.25 | -0.38 – -0.12 | -3.78 | <0.001 | 1544.66 |
| vs race [Hispanic] | -0.09 | -0.23 – 0.05 | -1.31 | 0.189 | 1472.33 |
| vs race [Other] | -0.15 | -0.31 – 0.02 | -1.76 | 0.078 | 1510.85 |
| pDem Rep × pollbias z | 0.49 | -0.85 – 1.83 | 0.72 | 0.472 | 4027.91 |
| pParty Ind × pollbias z | -0.92 | -2.94 – 1.11 | -0.89 | 0.376 | 5059.22 |
| pDem Rep × per gop | 0.01 | 0.00 – 0.02 | 2.44 | 0.015 | 4065.70 |
| pParty Ind × per gop | 0.00 | -0.01 – 0.01 | 0.01 | 0.989 | 5098.59 |
| pollbias z × per gop | 0.00 | -0.01 – 0.02 | 0.44 | 0.661 | 4157.88 |
| pDem Rep × wave2 1 | -0.20 | -0.70 – 0.29 | -0.81 | 0.419 | 3539.83 |
| pDem Rep × wave2 3 | 0.13 | -0.40 – 0.66 | 0.47 | 0.636 | 3984.37 |
| pDem Rep × wave2 4 | 0.47 | -0.16 – 1.09 | 1.45 | 0.147 | 4066.38 |
| pParty Ind × wave2 1 | 0.13 | -0.55 – 0.81 | 0.38 | 0.701 | 3699.16 |
| pParty Ind × wave2 3 | 0.20 | -0.55 – 0.95 | 0.51 | 0.607 | 3974.53 |
| pParty Ind × wave2 4 | 0.28 | -0.62 – 1.17 | 0.60 | 0.547 | 4008.50 |
| pollbias z × wave2 1 | -0.39 | -1.22 – 0.43 | -0.94 | 0.348 | 3647.97 |
| pollbias z × wave2 3 | -0.43 | -1.32 – 0.47 | -0.93 | 0.353 | 3968.98 |
| pollbias z × wave2 4 | -0.29 | -1.41 – 0.84 | -0.50 | 0.618 | 4029.69 |
| per gop × wave2 1 | -0.00 | -0.01 – 0.00 | -1.77 | 0.076 | 3563.67 |
| per gop × wave2 3 | -0.00 | -0.01 – 0.00 | -0.44 | 0.663 | 3949.83 |
| per gop × wave2 4 | -0.00 | -0.01 – 0.00 | -0.93 | 0.352 | 4012.72 |
|
(pDem Rep × pollbias z) × per gop |
-0.00 | -0.03 – 0.02 | -0.23 | 0.814 | 4023.73 |
|
(pParty Ind × pollbias z) × per gop |
0.01 | -0.03 – 0.05 | 0.61 | 0.539 | 5002.02 |
|
(pDem Rep × pollbias z) × wave2 1 |
-1.42 | -2.82 – -0.02 | -1.99 | 0.046 | 3540.87 |
|
(pDem Rep × pollbias z) × wave2 3 |
-0.96 | -2.46 – 0.55 | -1.24 | 0.214 | 3972.08 |
|
(pDem Rep × pollbias z) × wave2 4 |
-0.39 | -2.21 – 1.43 | -0.42 | 0.673 | 4058.22 |
|
(pParty Ind × pollbias z) × wave2 1 |
2.29 | 0.10 – 4.48 | 2.05 | 0.041 | 3783.60 |
|
(pParty Ind × pollbias z) × wave2 3 |
0.56 | -1.82 – 2.95 | 0.46 | 0.644 | 3989.77 |
|
(pParty Ind × pollbias z) × wave2 4 |
1.46 | -1.53 – 4.46 | 0.96 | 0.337 | 4036.77 |
|
(pDem Rep × per gop) × wave2 1 |
-0.02 | -0.03 – -0.01 | -3.69 | <0.001 | 3529.73 |
|
(pDem Rep × per gop) × wave2 3 |
-0.00 | -0.01 – 0.01 | -0.42 | 0.674 | 3971.47 |
|
(pDem Rep × per gop) × wave2 4 |
-0.00 | -0.02 – 0.01 | -0.57 | 0.569 | 4058.57 |
|
(pParty Ind × per gop) × wave2 1 |
-0.00 | -0.02 – 0.01 | -0.32 | 0.749 | 3682.33 |
|
(pParty Ind × per gop) × wave2 3 |
-0.00 | -0.02 – 0.01 | -0.34 | 0.730 | 3976.81 |
|
(pParty Ind × per gop) × wave2 4 |
-0.01 | -0.02 – 0.01 | -0.62 | 0.534 | 4014.82 |
|
(pollbias z × per gop) × wave2 1 |
0.00 | -0.02 – 0.02 | 0.02 | 0.986 | 3628.54 |
|
(pollbias z × per gop) × wave2 3 |
0.01 | -0.01 – 0.03 | 1.05 | 0.294 | 3963.35 |
|
(pollbias z × per gop) × wave2 4 |
0.01 | -0.02 – 0.03 | 0.48 | 0.628 | 4034.63 |
|
(pDem Rep × pollbias z × per gop) × wave2 1 |
0.02 | -0.01 – 0.05 | 1.35 | 0.179 | 3532.89 |
|
(pDem Rep × pollbias z × per gop) × wave2 3 |
0.02 | -0.01 – 0.05 | 0.99 | 0.320 | 3962.48 |
|
(pDem Rep × pollbias z × per gop) × wave2 4 |
0.00 | -0.04 – 0.04 | 0.04 | 0.967 | 4055.26 |
|
(pParty Ind × pollbias z × per gop) × wave2 1 |
-0.04 | -0.09 – 0.00 | -1.95 | 0.051 | 3766.50 |
|
(pParty Ind × pollbias z × per gop) × wave2 3 |
-0.00 | -0.05 – 0.04 | -0.12 | 0.902 | 3988.34 |
|
(pParty Ind × pollbias z × per gop) × wave2 4 |
-0.02 | -0.08 – 0.04 | -0.75 | 0.454 | 4044.09 |
| Random Effects | |||||
| σ2 | 0.50 | ||||
| τ00 pid | 0.50 | ||||
| τ11 pid.pollAcc | 0.02 | ||||
| τ11 pid.pollFreq.c | 0.01 | ||||
| ρ01 | -0.23 | ||||
| 0.17 | |||||
| ICC | 0.54 | ||||
| N pid | 1578 | ||||
| Observations | 5428 | ||||
| Marginal R2 / Conditional R2 | 0.168 / 0.619 | ||||
4. Cognitive & Evaluative factors
a. Cognitive - Conspiracism and need for cognition
ggplot(d) +
geom_smooth(aes(x = GCB,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
theme_bw() +
labs(x = "Generic Conspiracist Beliefs",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
coord_cartesian(ylim = c(1,5))
ggplot(d) +
geom_smooth(aes(x = NFC,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm",
fullrange = T) +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
theme_bw() +
labs(x = "Need for Cognition",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant
Partisan Identity",
values = c("#1696d2","grey","#db2b27")) +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(-3,3,1))
PEL.m18.plot <- lmer(voteconfidence ~ party_factor * wave
* GCB * NFC
+ vs_race + vs_age + male_female + nonbinary_mf
+ (pDem_Rep + pParty_Ind | pid),
data = d)
party_label <- c("party_factor = Democrat" = "Democrat", "party_factor = Independent" = "Independent","party_factor = Republican" = "Republican")
interact_plot(PEL.m18.plot, pred = GCB, modx = NFC, interval = T,
legend.main = "Generic Conspiracist
Beliefs") +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(-3,3,1)) +
labs(x = "Need for Cognition",
y = "Perceived Election Legitimacy")
Summary
Conspiracism negatively predicted (b = -0.28, 95% CI = [-0.33, -0.23], t(1085.6) = -11.63, p < .001) and need for cognition positively predicted (b = 0.09, 95% CI = [0.04, 0.15], t(754.5) = 3.27, p = .001) election legitimacy, collapsing across time and partisan identity. There was also an interaction of conspiracism and need for cognition (b = 0.09, 95% CI = [0.04, 0.15], t(483.0) = 3.47, p = .001), such that the need for cognition-perceived election legitimacy relationship becomes less positive as conspiracism increases.
In the context of the full model, there were higher-order interactions involving partisan identity, time, and both conspiracism (b = 0.28, 95% CI = [0.17, 0.39], t(5725.9) = 5.10, p < .001) and need for cognition (b = -0.12, 95% CI = [-0.21, -0.03], t(5549.7) = -2.52, p = .012). Specifically, conspiracism became slightly more predictive for Democrats (b = 0.09, 95% CI = [0.02, 0.15], t(5462.1) = 2.66, p = .008) and less negatively predictive for Republicans (b = -0.22, 95% CI = [-0.30, -0.14], t(5482.1) = -5.54, p < .001), from wave 1 to wave 2, while need for cognition attenuated somewhat in strength for Republicans (b = 0.13, 95% CI = [0.04, 0.22], t(5463.4) = 2.85, p = .004) and did not change in predictive strength for Democrats (p = .51) from wave 1 to wave 2; changes between other waves were not significant (all p’s > .10).
Models
Main Model
PEL.m18 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub)
* GCB * NFC
+ vs_race + vs_age + male_female + nonbinary_mf
+ (GCB + NFC | pid),
data = d)
tab_model(PEL.m18, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.96 | 2.76 – 3.16 | 29.54 | <0.001 | 2070.72 |
| pDem Rep | -0.11 | -0.19 – -0.03 | -2.81 | 0.005 | 2729.30 |
| pParty Ind | -0.38 | -0.48 – -0.29 | -7.82 | <0.001 | 4898.55 |
| wave lin | 0.13 | 0.07 – 0.19 | 4.29 | <0.001 | 5785.97 |
| wave quad | 0.21 | 0.17 – 0.26 | 9.26 | <0.001 | 5592.24 |
| wave cub | -0.07 | -0.13 – -0.02 | -2.60 | 0.009 | 5562.74 |
| GCB | -0.28 | -0.33 – -0.23 | -11.63 | <0.001 | 1085.62 |
| NFC | 0.09 | 0.04 – 0.15 | 3.27 | 0.001 | 754.54 |
| vs race [Black] | -0.17 | -0.28 – -0.05 | -2.84 | 0.005 | 2223.54 |
| vs race [Hispanic] | -0.03 | -0.16 – 0.09 | -0.56 | 0.578 | 2192.89 |
| vs race [Other] | -0.06 | -0.20 – 0.08 | -0.91 | 0.365 | 2042.41 |
| vs age | 0.01 | 0.00 – 0.01 | 4.58 | <0.001 | 2079.49 |
| male female | -0.34 | -0.42 – -0.27 | -9.20 | <0.001 | 2130.21 |
| nonbinary mf | 0.53 | 0.07 – 1.00 | 2.27 | 0.023 | 1820.05 |
| pDem Rep × wave lin | 1.15 | 1.04 – 1.25 | 21.49 | <0.001 | 5752.91 |
| pDem Rep × wave quad | 0.52 | 0.44 – 0.59 | 12.96 | <0.001 | 5574.29 |
| pDem Rep × wave cub | -0.63 | -0.73 – -0.54 | -13.21 | <0.001 | 5534.67 |
| pParty Ind × wave lin | 0.04 | -0.12 – 0.19 | 0.48 | 0.632 | 5853.78 |
| pParty Ind × wave quad | 0.03 | -0.09 – 0.14 | 0.43 | 0.671 | 5679.30 |
| pParty Ind × wave cub | 0.12 | -0.02 – 0.27 | 1.65 | 0.099 | 5645.34 |
| pDem Rep × GCB | 0.07 | -0.01 – 0.15 | 1.71 | 0.088 | 1166.16 |
| pParty Ind × GCB | -0.10 | -0.20 – 0.01 | -1.71 | 0.087 | 1898.14 |
| wave lin × GCB | 0.09 | 0.03 – 0.16 | 2.87 | 0.004 | 5758.89 |
| wave quad × GCB | 0.05 | -0.00 – 0.10 | 1.94 | 0.053 | 5557.68 |
| wave cub × GCB | -0.02 | -0.08 – 0.04 | -0.68 | 0.499 | 5559.25 |
| pDem Rep × NFC | 0.05 | -0.05 – 0.14 | 0.97 | 0.333 | 804.40 |
| pParty Ind × NFC | -0.09 | -0.21 – 0.03 | -1.40 | 0.163 | 1610.51 |
| wave lin × NFC | -0.03 | -0.11 – 0.04 | -0.94 | 0.350 | 5733.05 |
| wave quad × NFC | -0.03 | -0.09 – 0.02 | -1.19 | 0.233 | 5615.94 |
| wave cub × NFC | 0.02 | -0.05 – 0.09 | 0.58 | 0.565 | 5551.96 |
| GCB × NFC | 0.09 | 0.04 – 0.15 | 3.47 | 0.001 | 483.02 |
|
(pDem Rep × wave lin) × GCB |
0.28 | 0.17 – 0.39 | 5.10 | <0.001 | 5725.87 |
|
(pDem Rep × wave quad) × GCB |
0.12 | 0.03 – 0.20 | 2.82 | 0.005 | 5571.44 |
|
(pDem Rep × wave cub) × GCB |
-0.16 | -0.26 – -0.06 | -3.28 | 0.001 | 5529.16 |
|
(pParty Ind × wave lin) × GCB |
0.03 | -0.14 – 0.20 | 0.32 | 0.746 | 5799.91 |
|
(pParty Ind × wave quad) × GCB |
0.04 | -0.09 – 0.17 | 0.60 | 0.551 | 5613.13 |
|
(pParty Ind × wave cub) × GCB |
-0.01 | -0.17 – 0.14 | -0.18 | 0.860 | 5615.16 |
|
(pDem Rep × wave lin) × NFC |
-0.11 | -0.24 – 0.01 | -1.77 | 0.077 | 5680.92 |
|
(pDem Rep × wave quad) × NFC |
-0.12 | -0.21 – -0.03 | -2.52 | 0.012 | 5549.71 |
|
(pDem Rep × wave cub) × NFC |
0.01 | -0.10 – 0.12 | 0.20 | 0.839 | 5521.51 |
|
(pParty Ind × wave lin) × NFC |
0.07 | -0.12 – 0.27 | 0.77 | 0.442 | 5775.99 |
|
(pParty Ind × wave quad) × NFC |
0.02 | -0.12 – 0.16 | 0.28 | 0.783 | 5686.53 |
|
(pParty Ind × wave cub) × NFC |
0.07 | -0.11 – 0.24 | 0.77 | 0.440 | 5614.65 |
| (pDem Rep × GCB) × NFC | -0.06 | -0.14 – 0.03 | -1.28 | 0.202 | 701.06 |
| (pParty Ind × GCB) × NFC | 0.03 | -0.09 – 0.15 | 0.44 | 0.663 | 921.31 |
| (wave lin × GCB) × NFC | -0.04 | -0.12 – 0.03 | -1.08 | 0.282 | 5658.24 |
| (wave quad × GCB) × NFC | 0.03 | -0.03 – 0.08 | 0.96 | 0.337 | 5595.32 |
| (wave cub × GCB) × NFC | -0.01 | -0.08 – 0.05 | -0.43 | 0.666 | 5604.11 |
|
(pDem Rep × wave lin × GCB) × NFC |
-0.06 | -0.18 – 0.06 | -0.98 | 0.330 | 5676.04 |
|
(pDem Rep × wave quad × GCB) × NFC |
0.02 | -0.06 – 0.11 | 0.52 | 0.600 | 5564.42 |
|
(pDem Rep × wave cub × GCB) × NFC |
0.09 | -0.01 – 0.20 | 1.78 | 0.075 | 5530.62 |
|
(pParty Ind × wave lin × GCB) × NFC |
-0.04 | -0.25 – 0.17 | -0.38 | 0.704 | 5520.34 |
|
(pParty Ind × wave quad × GCB) × NFC |
-0.07 | -0.22 – 0.07 | -1.00 | 0.319 | 5618.16 |
|
(pParty Ind × wave cub × GCB) × NFC |
-0.13 | -0.31 – 0.04 | -1.50 | 0.133 | 5620.60 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.51 | ||||
| τ11 pid.GCB | 0.04 | ||||
| τ11 pid.NFC | 0.05 | ||||
| ρ01 | 0.51 | ||||
| -0.05 | |||||
| ICC | 0.52 | ||||
| N pid | 2249 | ||||
| Observations | 7722 | ||||
| Marginal R2 / Conditional R2 | 0.204 / 0.621 | ||||
Party ID-specific Effects Centered at Wave 2
PEL.m18.w2.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave2_1 + wave2_3 + wave2_4)
* GCB * NFC
+ vs_race + vs_age + male_female + nonbinary_mf
+ (GCB + NFC | pid),
data = d)
tab_model(PEL.m18.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.13 | 2.93 – 3.33 | 30.44 | <0.001 | 2243.32 |
| pDem R | 0.18 | 0.08 – 0.27 | 3.66 | <0.001 | 5191.37 |
| pDem I | -0.35 | -0.49 – -0.21 | -4.95 | <0.001 | 7174.94 |
| wave2 1 | 0.32 | 0.26 – 0.38 | 9.96 | <0.001 | 5473.63 |
| wave2 3 | -0.02 | -0.09 – 0.04 | -0.73 | 0.466 | 5551.72 |
| wave2 4 | -0.24 | -0.31 – -0.16 | -6.00 | <0.001 | 5699.57 |
| GCB | -0.31 | -0.38 – -0.25 | -9.70 | <0.001 | 2226.70 |
| NFC | 0.11 | 0.03 – 0.19 | 2.83 | 0.005 | 1793.35 |
| vs race [Black] | -0.17 | -0.28 – -0.05 | -2.84 | 0.005 | 2223.54 |
| vs race [Hispanic] | -0.03 | -0.16 – 0.09 | -0.56 | 0.578 | 2192.89 |
| vs race [Other] | -0.06 | -0.20 – 0.08 | -0.91 | 0.364 | 2042.41 |
| vs age | 0.01 | 0.00 – 0.01 | 4.58 | <0.001 | 2079.49 |
| male female | -0.34 | -0.42 – -0.27 | -9.20 | <0.001 | 2130.21 |
| nonbinary mf | 0.53 | 0.07 – 1.00 | 2.27 | 0.023 | 1820.04 |
| pDem R × wave2 1 | -1.28 | -1.38 – -1.18 | -25.42 | <0.001 | 5491.23 |
| pDem R × wave2 3 | -0.06 | -0.16 – 0.04 | -1.18 | 0.239 | 5561.61 |
| pDem R × wave2 4 | 0.19 | 0.07 – 0.30 | 3.07 | 0.002 | 5709.67 |
| pDem I × wave2 1 | -0.58 | -0.74 – -0.43 | -7.34 | <0.001 | 5620.17 |
| pDem I × wave2 3 | 0.11 | -0.06 – 0.28 | 1.30 | 0.192 | 5678.30 |
| pDem I × wave2 4 | 0.13 | -0.06 – 0.31 | 1.34 | 0.180 | 5793.38 |
| pDem R × GCB | 0.14 | 0.04 – 0.23 | 2.74 | 0.006 | 2562.25 |
| pDem I × GCB | -0.01 | -0.16 – 0.15 | -0.10 | 0.922 | 3973.88 |
| wave2 1 × GCB | 0.09 | 0.02 – 0.15 | 2.66 | 0.008 | 5462.11 |
| wave2 3 × GCB | 0.04 | -0.03 – 0.10 | 1.10 | 0.271 | 5539.41 |
| wave2 4 × GCB | -0.00 | -0.08 – 0.08 | -0.07 | 0.948 | 5688.83 |
| pDem R × NFC | 0.01 | -0.11 – 0.12 | 0.15 | 0.880 | 1809.10 |
| pDem I × NFC | -0.12 | -0.29 – 0.05 | -1.44 | 0.150 | 2967.74 |
| wave2 1 × NFC | -0.03 | -0.10 – 0.05 | -0.67 | 0.506 | 5472.45 |
| wave2 3 × NFC | -0.01 | -0.09 – 0.07 | -0.28 | 0.782 | 5512.58 |
| wave2 4 × NFC | -0.02 | -0.12 – 0.07 | -0.52 | 0.600 | 5653.94 |
| GCB × NFC | 0.14 | 0.07 – 0.21 | 3.99 | <0.001 | 1007.75 |
| (pDem R × wave2 1) × GCB | -0.31 | -0.41 – -0.21 | -5.96 | <0.001 | 5486.85 |
| (pDem R × wave2 3) × GCB | -0.02 | -0.13 – 0.08 | -0.39 | 0.696 | 5551.60 |
| (pDem R × wave2 4) × GCB | 0.06 | -0.07 – 0.18 | 0.88 | 0.377 | 5695.24 |
| (pDem I × wave2 1) × GCB | -0.21 | -0.38 – -0.04 | -2.48 | 0.013 | 5568.71 |
| (pDem I × wave2 3) × GCB | -0.01 | -0.19 – 0.17 | -0.12 | 0.908 | 5659.30 |
| (pDem I × wave2 4) × GCB | 0.01 | -0.19 – 0.21 | 0.06 | 0.950 | 5720.02 |
| (pDem R × wave2 1) × NFC | 0.16 | 0.04 – 0.27 | 2.59 | 0.010 | 5475.42 |
| (pDem R × wave2 3) × NFC | -0.04 | -0.17 – 0.08 | -0.72 | 0.472 | 5525.94 |
| (pDem R × wave2 4) × NFC | 0.04 | -0.10 – 0.18 | 0.53 | 0.597 | 5653.65 |
| (pDem I × wave2 1) × NFC | 0.09 | -0.09 – 0.27 | 0.98 | 0.329 | 5617.29 |
| (pDem I × wave2 3) × NFC | 0.08 | -0.12 – 0.29 | 0.82 | 0.411 | 5641.28 |
| (pDem I × wave2 4) × NFC | 0.07 | -0.15 – 0.30 | 0.62 | 0.532 | 5747.61 |
| (pDem R × GCB) × NFC | -0.08 | -0.18 – 0.03 | -1.42 | 0.155 | 1480.89 |
| (pDem I × GCB) × NFC | 0.03 | -0.13 – 0.18 | 0.34 | 0.732 | 1089.66 |
| (wave2 1 × GCB) × NFC | -0.05 | -0.12 – 0.02 | -1.49 | 0.136 | 5472.53 |
| (wave2 3 × GCB) × NFC | -0.02 | -0.09 – 0.05 | -0.46 | 0.642 | 5503.14 |
| (wave2 4 × GCB) × NFC | -0.04 | -0.13 – 0.04 | -0.99 | 0.321 | 5645.28 |
|
(pDem R × wave2 1 × GCB) × NFC |
0.06 | -0.05 – 0.17 | 1.13 | 0.259 | 5493.61 |
|
(pDem R × wave2 3 × GCB) × NFC |
0.06 | -0.05 – 0.18 | 1.15 | 0.250 | 5519.94 |
|
(pDem R × wave2 4 × GCB) × NFC |
-0.04 | -0.18 – 0.09 | -0.63 | 0.526 | 5656.10 |
|
(pDem I × wave2 1 × GCB) × NFC |
0.01 | -0.15 – 0.18 | 0.18 | 0.857 | 5521.96 |
|
(pDem I × wave2 3 × GCB) × NFC |
-0.12 | -0.32 – 0.08 | -1.20 | 0.231 | 5332.29 |
|
(pDem I × wave2 4 × GCB) × NFC |
-0.01 | -0.25 – 0.23 | -0.09 | 0.925 | 5698.79 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.51 | ||||
| τ11 pid.GCB | 0.04 | ||||
| τ11 pid.NFC | 0.05 | ||||
| ρ01 | 0.51 | ||||
| -0.05 | |||||
| ICC | 0.52 | ||||
| N pid | 2249 | ||||
| Observations | 7722 | ||||
| Marginal R2 / Conditional R2 | 0.204 / 0.621 | ||||
PEL.m18.w2.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave2_1 + wave2_3 + wave2_4)
* GCB * NFC
+ vs_race + vs_age + male_female + nonbinary_mf
+ (GCB + NFC | pid),
data = d)
tab_model(PEL.m18.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.31 | 3.10 – 3.51 | 31.31 | <0.001 | 2328.75 |
| pRep D | -0.18 | -0.27 – -0.08 | -3.66 | <0.001 | 5191.44 |
| pRep I | -0.53 | -0.68 – -0.39 | -7.18 | <0.001 | 6772.24 |
| wave2 1 | -0.96 | -1.03 – -0.88 | -24.88 | <0.001 | 5479.49 |
| wave2 3 | -0.09 | -0.16 – -0.01 | -2.16 | 0.031 | 5557.01 |
| wave2 4 | -0.05 | -0.14 – 0.04 | -1.11 | 0.268 | 5706.75 |
| GCB | -0.18 | -0.25 – -0.10 | -4.57 | <0.001 | 2299.69 |
| NFC | 0.12 | 0.03 – 0.21 | 2.65 | 0.008 | 1414.70 |
| vs race [Black] | -0.17 | -0.28 – -0.05 | -2.84 | 0.005 | 2223.53 |
| vs race [Hispanic] | -0.03 | -0.16 – 0.09 | -0.56 | 0.578 | 2192.87 |
| vs race [Other] | -0.06 | -0.20 – 0.08 | -0.91 | 0.365 | 2042.40 |
| vs age | 0.01 | 0.00 – 0.01 | 4.58 | <0.001 | 2079.47 |
| male female | -0.34 | -0.42 – -0.27 | -9.20 | <0.001 | 2130.20 |
| nonbinary mf | 0.53 | 0.07 – 1.00 | 2.27 | 0.023 | 1820.04 |
| pRep D × wave2 1 | 1.28 | 1.18 – 1.38 | 25.42 | <0.001 | 5491.23 |
| pRep D × wave2 3 | 0.06 | -0.04 – 0.16 | 1.18 | 0.239 | 5561.62 |
| pRep D × wave2 4 | -0.19 | -0.30 – -0.07 | -3.07 | 0.002 | 5709.67 |
| pRep I × wave2 1 | 0.70 | 0.53 – 0.86 | 8.47 | <0.001 | 5616.70 |
| pRep I × wave2 3 | 0.17 | 0.00 – 0.34 | 1.97 | 0.049 | 5666.48 |
| pRep I × wave2 4 | -0.06 | -0.25 – 0.13 | -0.61 | 0.541 | 5792.84 |
| pRep D × GCB | -0.14 | -0.23 – -0.04 | -2.74 | 0.006 | 2562.18 |
| pRep I × GCB | -0.14 | -0.30 – 0.01 | -1.78 | 0.076 | 4041.13 |
| wave2 1 × GCB | -0.22 | -0.30 – -0.14 | -5.54 | <0.001 | 5482.07 |
| wave2 3 × GCB | 0.02 | -0.06 – 0.10 | 0.39 | 0.694 | 5549.77 |
| wave2 4 × GCB | 0.05 | -0.04 – 0.15 | 1.09 | 0.274 | 5694.61 |
| pRep D × NFC | -0.01 | -0.12 – 0.11 | -0.15 | 0.880 | 1809.03 |
| pRep I × NFC | -0.13 | -0.31 – 0.04 | -1.49 | 0.138 | 2688.62 |
| wave2 1 × NFC | 0.13 | 0.04 – 0.22 | 2.85 | 0.004 | 5463.43 |
| wave2 3 × NFC | -0.06 | -0.15 – 0.04 | -1.18 | 0.237 | 5525.65 |
| wave2 4 × NFC | 0.01 | -0.09 – 0.12 | 0.24 | 0.807 | 5649.29 |
| GCB × NFC | 0.06 | -0.02 – 0.15 | 1.46 | 0.143 | 1184.58 |
| (pRep D × wave2 1) × GCB | 0.31 | 0.21 – 0.41 | 5.96 | <0.001 | 5486.86 |
| (pRep D × wave2 3) × GCB | 0.02 | -0.08 – 0.13 | 0.39 | 0.696 | 5551.61 |
| (pRep D × wave2 4) × GCB | -0.06 | -0.18 – 0.07 | -0.88 | 0.377 | 5695.25 |
| (pRep I × wave2 1) × GCB | 0.10 | -0.08 – 0.27 | 1.10 | 0.269 | 5579.76 |
| (pRep I × wave2 3) × GCB | 0.01 | -0.18 – 0.20 | 0.11 | 0.914 | 5651.17 |
| (pRep I × wave2 4) × GCB | -0.05 | -0.26 – 0.16 | -0.46 | 0.642 | 5725.02 |
| (pRep D × wave2 1) × NFC | -0.16 | -0.27 – -0.04 | -2.59 | 0.010 | 5475.43 |
| (pRep D × wave2 3) × NFC | 0.04 | -0.08 – 0.17 | 0.72 | 0.472 | 5525.95 |
| (pRep D × wave2 4) × NFC | -0.04 | -0.18 – 0.10 | -0.53 | 0.597 | 5653.66 |
| (pRep I × wave2 1) × NFC | -0.06 | -0.25 – 0.12 | -0.68 | 0.497 | 5599.44 |
| (pRep I × wave2 3) × NFC | 0.13 | -0.08 – 0.34 | 1.22 | 0.222 | 5637.45 |
| (pRep I × wave2 4) × NFC | 0.03 | -0.20 – 0.27 | 0.29 | 0.773 | 5744.36 |
| (pRep D × GCB) × NFC | 0.08 | -0.03 – 0.18 | 1.42 | 0.155 | 1480.86 |
| (pRep I × GCB) × NFC | 0.11 | -0.06 – 0.27 | 1.26 | 0.209 | 1250.71 |
| (wave2 1 × GCB) × NFC | 0.01 | -0.07 – 0.09 | 0.23 | 0.816 | 5486.84 |
| (wave2 3 × GCB) × NFC | 0.05 | -0.04 – 0.13 | 1.11 | 0.268 | 5525.69 |
| (wave2 4 × GCB) × NFC | -0.09 | -0.19 – 0.02 | -1.64 | 0.101 | 5663.59 |
|
(pRep D × wave2 1 × GCB) × NFC |
-0.06 | -0.17 – 0.05 | -1.13 | 0.259 | 5493.62 |
|
(pRep D × wave2 3 × GCB) × NFC |
-0.06 | -0.18 – 0.05 | -1.15 | 0.250 | 5519.95 |
|
(pRep D × wave2 4 × GCB) × NFC |
0.04 | -0.09 – 0.18 | 0.63 | 0.526 | 5656.10 |
|
(pRep I × wave2 1 × GCB) × NFC |
-0.05 | -0.21 – 0.12 | -0.55 | 0.580 | 5531.56 |
|
(pRep I × wave2 3 × GCB) × NFC |
-0.19 | -0.39 – 0.02 | -1.79 | 0.073 | 5371.87 |
|
(pRep I × wave2 4 × GCB) × NFC |
0.03 | -0.22 – 0.28 | 0.25 | 0.801 | 5698.76 |
| Random Effects | |||||
| σ2 | 0.54 | ||||
| τ00 pid | 0.51 | ||||
| τ11 pid.GCB | 0.04 | ||||
| τ11 pid.NFC | 0.05 | ||||
| ρ01 | 0.51 | ||||
| -0.05 | |||||
| ICC | 0.52 | ||||
| N pid | 2249 | ||||
| Observations | 7722 | ||||
| Marginal R2 / Conditional R2 | 0.204 / 0.621 | ||||
b. Elite Representativeness
ggplot(d[!is.na(d$party_factor),], aes(x=representation_Dem, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Representation (Democratic elites)") +
scale_x_continuous(breaks = seq(1,7,1)) 
ggplot(d[!is.na(d$party_factor),], aes(x=representation_Rep, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Representation (Republican elites)") +
scale_x_continuous(breaks = seq(1,7,1)) 
ggplot(d[!is.na(d$party_factor),],
aes(x = representation_Rep,
y = representation_Dem)) +
geom_jitter(size = .2, alpha = .3) +
geom_smooth(method = "lm", color = "#69b3a2") +
theme_bw() +
labs(x = "Representation (Republican elites)",
y = "Representation (Democratic elites)") +
scale_x_continuous(breaks = seq(1,5,1)) +
scale_y_continuous(breaks = seq(1,5,1)) +
coord_cartesian(ylim = c(1,5)) +
facet_wrap(~party_factor)
ggplot(d[!is.na(d$party_factor),],
aes(x = representation_Dem,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F) +
labs(x = "Representation (Democratic elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","black","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","black","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(1,5)) +
scale_x_continuous(breaks = seq(1,5,1)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = representation_Dem,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Democratic elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = representation_Dem,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width =.2, alpha=0.4, size = .2, show.legend = F ) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Democratic elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = representation_Rep,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F) +
labs(x = "Representation (Republican elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","black","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","black","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(1,5)) +
scale_x_continuous(breaks = seq(1,5,1)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = representation_Rep,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Republican elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = representation_Rep,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width =.2, alpha=0.4, size = .2, show.legend = F ) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Republican elites)",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
Summary
The extent to which participants perceived Democratic and Republican partisan elites as representing their constituency differentially predicted perceived election legitimacy over the course of the study. Collapsing across partisan identity and time, Democratic elite representativeness positively predicted perceived election legitimacy (b = 0.18, 95% CI = [0.13, 0.23], t(1934.1) = 6.92, p < .001). The strength of this relationship changed over linear time (b = -0.10, 95% CI = [-0.16, -0.04], t(3812.3) = -3.13, p = .002), and there were differences in quadratic change over time for Democrats and Republicans (b = -0.13, 95% CI = [-0.21, -0.06], t(3775.5) = -3.48, p = .001).
The Democratic elite representativeness-perceived election legitimacy relationship weakened linearly for Democrats (b = -0.12, 95% CI = [-0.18, -0.05], t(3772.6) = -3.49, p < .001) and for Republicans (b = -0.14, 95% CI = [-0.21, -0.07], t(3778.2) = -4.12, p < .001).
Republican elite representativeness was not predictive of perceived election legitimacy when collapsing across time and partisan identity (p = .06), but the overall relationship became more predictive over linear time (b = 0.15, 95% CI = [0.10, 0.21], t(3809.6) = 5.28, p < .001). This change primarily occurred from wave 1 to wave 2 (b = 0.09, 95% CI = [0.04, 0.13], t(3775.8) = 3.56, p < .001), where Republican representativeness was predictive of perceived election legitimacy for Republicans (b = 0.15, 95% CI = [0.08, 0.21], t(3405.8) = 4.30, p < .001), though not for Democrats (p = .89).
Models
Main Model
PEL.m10 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (representation_Dem + representation_Rep) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1|pid),
data = d)
tab_model(PEL.m10, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.41 | 2.10 – 2.72 | 15.27 | <0.001 | 1275.39 |
| pDem Rep | -0.33 | -0.63 – -0.03 | -2.16 | 0.031 | 2237.66 |
| pParty Ind | -0.34 | -0.64 – -0.03 | -2.15 | 0.032 | 3778.53 |
| representation Dem | 0.18 | 0.13 – 0.23 | 6.92 | <0.001 | 1934.13 |
| representation Rep | 0.05 | -0.00 – 0.09 | 1.87 | 0.061 | 1817.89 |
| wave lin | 0.04 | -0.13 – 0.21 | 0.45 | 0.656 | 3781.08 |
| wave quad | 0.36 | 0.22 – 0.50 | 5.15 | <0.001 | 3778.23 |
| wave cub | -0.22 | -0.40 – -0.05 | -2.51 | 0.012 | 3771.77 |
| vs race [Black] | -0.38 | -0.54 – -0.22 | -4.63 | <0.001 | 1265.11 |
| vs race [Hispanic] | 0.03 | -0.14 – 0.20 | 0.37 | 0.715 | 1249.14 |
| vs race [Other] | -0.09 | -0.27 – 0.10 | -0.94 | 0.349 | 1250.01 |
| vs age | 0.01 | 0.00 – 0.01 | 4.10 | <0.001 | 1257.07 |
| male female | -0.34 | -0.44 – -0.24 | -6.67 | <0.001 | 1247.49 |
| nonbinary mf | 0.20 | -0.46 – 0.86 | 0.60 | 0.550 | 1250.28 |
|
pDem Rep × representation Dem |
-0.01 | -0.09 – 0.07 | -0.26 | 0.793 | 1895.91 |
|
pDem Rep × representation Rep |
0.05 | -0.02 – 0.13 | 1.35 | 0.178 | 2010.67 |
|
pParty Ind × representation Dem |
0.11 | -0.00 – 0.23 | 1.93 | 0.054 | 4136.04 |
|
pParty Ind × representation Rep |
-0.09 | -0.20 – 0.01 | -1.72 | 0.086 | 3938.19 |
| pDem Rep × wave lin | 1.10 | 0.74 – 1.45 | 6.06 | <0.001 | 3791.59 |
| pDem Rep × wave quad | 0.63 | 0.35 – 0.91 | 4.40 | <0.001 | 3783.47 |
| pDem Rep × wave cub | -0.59 | -0.94 – -0.24 | -3.27 | 0.001 | 3778.80 |
| pParty Ind × wave lin | -0.25 | -0.68 – 0.17 | -1.17 | 0.242 | 3841.39 |
| pParty Ind × wave quad | 0.24 | -0.10 – 0.58 | 1.39 | 0.165 | 3832.37 |
| pParty Ind × wave cub | 0.09 | -0.34 – 0.52 | 0.41 | 0.683 | 3815.34 |
|
representation Dem × wave lin |
-0.10 | -0.16 – -0.04 | -3.13 | 0.002 | 3812.25 |
|
representation Dem × wave quad |
-0.10 | -0.15 – -0.05 | -3.99 | <0.001 | 3801.24 |
|
representation Dem × wave cub |
0.07 | 0.01 – 0.13 | 2.17 | 0.030 | 3818.47 |
|
representation Rep × wave lin |
0.15 | 0.10 – 0.21 | 5.28 | <0.001 | 3809.58 |
|
representation Rep × wave quad |
0.05 | 0.00 – 0.09 | 2.07 | 0.038 | 3793.85 |
|
representation Rep × wave cub |
-0.02 | -0.08 – 0.03 | -0.86 | 0.390 | 3796.36 |
|
(pDem Rep × representation Dem) × wave lin |
-0.02 | -0.12 – 0.07 | -0.49 | 0.626 | 3779.68 |
|
(pDem Rep × representation Dem) × wave quad |
-0.13 | -0.21 – -0.06 | -3.48 | 0.001 | 3775.50 |
|
(pDem Rep × representation Dem) × wave cub |
0.07 | -0.02 – 0.16 | 1.48 | 0.139 | 3775.37 |
|
(pDem Rep × representation Rep) × wave lin |
0.00 | -0.09 – 0.09 | 0.01 | 0.992 | 3786.85 |
|
(pDem Rep × representation Rep) × wave quad |
0.08 | 0.01 – 0.16 | 2.30 | 0.021 | 3779.18 |
|
(pDem Rep × representation Rep) × wave cub |
-0.06 | -0.15 – 0.03 | -1.38 | 0.167 | 3775.03 |
|
(pParty Ind × representation Dem) × wave lin |
0.09 | -0.08 – 0.26 | 1.07 | 0.284 | 3848.67 |
|
(pParty Ind × representation Dem) × wave quad |
-0.13 | -0.26 – 0.00 | -1.96 | 0.051 | 3828.93 |
|
(pParty Ind × representation Dem) × wave cub |
-0.01 | -0.18 – 0.16 | -0.09 | 0.929 | 3861.28 |
|
(pParty Ind × representation Rep) × wave lin |
0.06 | -0.09 – 0.21 | 0.74 | 0.457 | 3848.80 |
|
(pParty Ind × representation Rep) × wave quad |
0.06 | -0.06 – 0.18 | 1.05 | 0.295 | 3833.79 |
|
(pParty Ind × representation Rep) × wave cub |
0.03 | -0.12 – 0.18 | 0.40 | 0.691 | 3829.33 |
| Random Effects | |||||
| σ2 | 0.52 | ||||
| τ00 pid | 0.64 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.173 / 0.627 | ||||
Main Model with by party ID-specific Effects
PEL.m10.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (representation_Dem + representation_Rep) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m10.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.68 | 2.35 – 3.02 | 15.77 | <0.001 | 1389.38 |
| pDem R | -0.33 | -0.63 – -0.03 | -2.16 | 0.031 | 2237.66 |
| pDem I | -0.50 | -0.84 – -0.16 | -2.92 | 0.003 | 3394.82 |
| representation Dem | 0.15 | 0.09 – 0.21 | 4.86 | <0.001 | 1586.98 |
| representation Rep | 0.05 | -0.00 – 0.10 | 1.83 | 0.068 | 1530.61 |
| wave lin | -0.43 | -0.66 – -0.19 | -3.54 | <0.001 | 3777.62 |
| wave quad | -0.03 | -0.22 – 0.15 | -0.35 | 0.726 | 3778.54 |
| wave cub | 0.04 | -0.19 – 0.28 | 0.35 | 0.727 | 3773.04 |
| vs race [Black] | -0.38 | -0.54 – -0.22 | -4.63 | <0.001 | 1265.11 |
| vs race [Hispanic] | 0.03 | -0.14 – 0.20 | 0.37 | 0.715 | 1249.14 |
| vs race [Other] | -0.09 | -0.27 – 0.10 | -0.94 | 0.349 | 1250.01 |
| vs age | 0.01 | 0.00 – 0.01 | 4.10 | <0.001 | 1257.07 |
| male female | -0.34 | -0.44 – -0.24 | -6.67 | <0.001 | 1247.49 |
| nonbinary mf | 0.20 | -0.46 – 0.86 | 0.60 | 0.550 | 1250.28 |
|
pDem R × representation Dem |
-0.01 | -0.09 – 0.07 | -0.26 | 0.793 | 1895.91 |
|
pDem R × representation Rep |
0.05 | -0.02 – 0.13 | 1.35 | 0.178 | 2010.67 |
|
pDem I × representation Dem |
0.11 | -0.02 – 0.23 | 1.71 | 0.087 | 3664.72 |
|
pDem I × representation Rep |
-0.07 | -0.18 – 0.05 | -1.15 | 0.249 | 3599.22 |
| pDem R × wave lin | 1.10 | 0.74 – 1.45 | 6.06 | <0.001 | 3791.59 |
| pDem R × wave quad | 0.63 | 0.35 – 0.91 | 4.40 | <0.001 | 3783.47 |
| pDem R × wave cub | -0.59 | -0.94 – -0.24 | -3.27 | 0.001 | 3778.80 |
| pDem I × wave lin | 0.29 | -0.16 – 0.75 | 1.28 | 0.202 | 3829.90 |
| pDem I × wave quad | 0.55 | 0.19 – 0.91 | 3.01 | 0.003 | 3826.05 |
| pDem I × wave cub | -0.20 | -0.66 – 0.25 | -0.88 | 0.380 | 3809.71 |
|
representation Dem × wave lin |
-0.12 | -0.18 – -0.05 | -3.49 | <0.001 | 3772.57 |
|
representation Dem × wave quad |
0.01 | -0.04 – 0.06 | 0.40 | 0.692 | 3770.73 |
|
representation Dem × wave cub |
0.04 | -0.03 – 0.10 | 1.08 | 0.280 | 3771.28 |
|
representation Rep × wave lin |
0.13 | 0.07 – 0.19 | 4.50 | <0.001 | 3773.81 |
|
representation Rep × wave quad |
-0.02 | -0.06 – 0.03 | -0.71 | 0.477 | 3770.43 |
|
representation Rep × wave cub |
-0.00 | -0.06 – 0.05 | -0.09 | 0.929 | 3767.99 |
|
(pDem R × representation Dem) × wave lin |
-0.02 | -0.12 – 0.07 | -0.49 | 0.626 | 3779.68 |
|
(pDem R × representation Dem) × wave quad |
-0.13 | -0.21 – -0.06 | -3.48 | 0.001 | 3775.50 |
|
(pDem R × representation Dem) × wave cub |
0.07 | -0.02 – 0.16 | 1.48 | 0.139 | 3775.37 |
|
(pDem R × representation Rep) × wave lin |
0.00 | -0.09 – 0.09 | 0.01 | 0.992 | 3786.85 |
|
(pDem R × representation Rep) × wave quad |
0.08 | 0.01 – 0.16 | 2.30 | 0.021 | 3779.18 |
|
(pDem R × representation Rep) × wave cub |
-0.06 | -0.15 – 0.03 | -1.38 | 0.167 | 3775.03 |
|
(pDem I × representation Dem) × wave lin |
0.08 | -0.09 – 0.25 | 0.90 | 0.367 | 3839.49 |
|
(pDem I × representation Dem) × wave quad |
-0.20 | -0.34 – -0.06 | -2.83 | 0.005 | 3821.80 |
|
(pDem I × representation Dem) × wave cub |
0.03 | -0.15 – 0.20 | 0.31 | 0.759 | 3853.90 |
|
(pDem I × representation Rep) × wave lin |
0.06 | -0.10 – 0.21 | 0.73 | 0.467 | 3842.68 |
|
(pDem I × representation Rep) × wave quad |
0.11 | -0.02 – 0.23 | 1.70 | 0.089 | 3829.18 |
|
(pDem I × representation Rep) × wave cub |
-0.00 | -0.16 – 0.15 | -0.02 | 0.982 | 3824.52 |
| Random Effects | |||||
| σ2 | 0.52 | ||||
| τ00 pid | 0.64 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.173 / 0.627 | ||||
PEL.m10.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (representation_Dem + representation_Rep) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m10.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.36 | 2.00 – 2.72 | 12.82 | <0.001 | 1489.77 |
| pRep D | 0.33 | 0.03 – 0.63 | 2.16 | 0.031 | 2237.66 |
| pRep I | -0.17 | -0.52 – 0.17 | -0.97 | 0.330 | 3487.80 |
| representation Dem | 0.14 | 0.08 – 0.20 | 4.45 | <0.001 | 1633.04 |
| representation Rep | 0.10 | 0.04 – 0.16 | 3.28 | 0.001 | 1767.24 |
| wave lin | 0.67 | 0.41 – 0.94 | 4.97 | <0.001 | 3791.75 |
| wave quad | 0.59 | 0.39 – 0.80 | 5.59 | <0.001 | 3776.84 |
| wave cub | -0.55 | -0.81 – -0.28 | -4.08 | <0.001 | 3775.35 |
| vs race [Black] | -0.38 | -0.54 – -0.22 | -4.63 | <0.001 | 1265.11 |
| vs race [Hispanic] | 0.03 | -0.14 – 0.20 | 0.37 | 0.715 | 1249.14 |
| vs race [Other] | -0.09 | -0.27 – 0.10 | -0.94 | 0.349 | 1250.01 |
| vs age | 0.01 | 0.00 – 0.01 | 4.10 | <0.001 | 1257.07 |
| male female | -0.34 | -0.44 – -0.24 | -6.67 | <0.001 | 1247.49 |
| nonbinary mf | 0.20 | -0.46 – 0.86 | 0.60 | 0.550 | 1250.28 |
|
pRep D × representation Dem |
0.01 | -0.07 – 0.09 | 0.26 | 0.793 | 1895.91 |
|
pRep D × representation Rep |
-0.05 | -0.13 – 0.02 | -1.35 | 0.178 | 2010.67 |
|
pRep I × representation Dem |
0.12 | -0.00 – 0.24 | 1.93 | 0.054 | 4015.06 |
|
pRep I × representation Rep |
-0.12 | -0.23 – -0.01 | -2.05 | 0.040 | 3721.63 |
| pRep D × wave lin | -1.10 | -1.45 – -0.74 | -6.06 | <0.001 | 3791.59 |
| pRep D × wave quad | -0.63 | -0.91 – -0.35 | -4.40 | <0.001 | 3783.47 |
| pRep D × wave cub | 0.59 | 0.24 – 0.94 | 3.27 | 0.001 | 3778.80 |
| pRep I × wave lin | -0.80 | -1.27 – -0.33 | -3.35 | 0.001 | 3837.84 |
| pRep I × wave quad | -0.07 | -0.45 – 0.30 | -0.39 | 0.697 | 3824.49 |
| pRep I × wave cub | 0.38 | -0.09 – 0.86 | 1.59 | 0.111 | 3810.44 |
|
representation Dem × wave lin |
-0.14 | -0.21 – -0.07 | -4.12 | <0.001 | 3778.16 |
|
representation Dem × wave quad |
-0.12 | -0.17 – -0.07 | -4.49 | <0.001 | 3772.44 |
|
representation Dem × wave cub |
0.11 | 0.04 – 0.17 | 3.13 | 0.002 | 3773.10 |
|
representation Rep × wave lin |
0.13 | 0.06 – 0.20 | 3.69 | <0.001 | 3785.13 |
|
representation Rep × wave quad |
0.07 | 0.01 – 0.12 | 2.40 | 0.017 | 3776.89 |
|
representation Rep × wave cub |
-0.07 | -0.14 – 0.00 | -1.87 | 0.062 | 3774.48 |
|
(pRep D × representation Dem) × wave lin |
0.02 | -0.07 – 0.12 | 0.49 | 0.626 | 3779.68 |
|
(pRep D × representation Dem) × wave quad |
0.13 | 0.06 – 0.21 | 3.48 | 0.001 | 3775.50 |
|
(pRep D × representation Dem) × wave cub |
-0.07 | -0.16 – 0.02 | -1.48 | 0.139 | 3775.37 |
|
(pRep D × representation Rep) × wave lin |
-0.00 | -0.09 – 0.09 | -0.01 | 0.992 | 3786.85 |
|
(pRep D × representation Rep) × wave quad |
-0.08 | -0.16 – -0.01 | -2.30 | 0.021 | 3779.18 |
|
(pRep D × representation Rep) × wave cub |
0.06 | -0.03 – 0.15 | 1.38 | 0.167 | 3775.03 |
|
(pRep I × representation Dem) × wave lin |
0.10 | -0.07 – 0.28 | 1.16 | 0.246 | 3847.78 |
|
(pRep I × representation Dem) × wave quad |
-0.07 | -0.20 – 0.07 | -0.94 | 0.345 | 3828.29 |
|
(pRep I × representation Dem) × wave cub |
-0.04 | -0.22 – 0.13 | -0.48 | 0.633 | 3856.57 |
|
(pRep I × representation Rep) × wave lin |
0.06 | -0.10 – 0.22 | 0.70 | 0.486 | 3844.50 |
|
(pRep I × representation Rep) × wave quad |
0.02 | -0.10 – 0.15 | 0.33 | 0.741 | 3829.21 |
|
(pRep I × representation Rep) × wave cub |
0.06 | -0.10 – 0.22 | 0.77 | 0.442 | 3824.98 |
| Random Effects | |||||
| σ2 | 0.52 | ||||
| τ00 pid | 0.64 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.173 / 0.627 | ||||
Main Model centered at Wave 1
PEL.m10.w1 <- lmer(voteconfidence ~ (pDem_Rep) * (representation_Dem + representation_Rep) * wave
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m10.w1, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.14 | 1.81 – 2.47 | 12.84 | <0.001 | 1569.42 |
| pDem Rep | -1.33 | -1.71 – -0.94 | -6.71 | <0.001 | 4241.59 |
| representation Dem | 0.27 | 0.22 – 0.33 | 10.33 | <0.001 | 2750.14 |
| representation Rep | -0.01 | -0.06 – 0.04 | -0.44 | 0.660 | 2765.06 |
| wave [2] | 0.53 | 0.35 – 0.70 | 5.88 | <0.001 | 3771.05 |
| wave [3] | 0.36 | 0.19 – 0.54 | 4.03 | <0.001 | 3772.36 |
| wave [4] | 0.22 | 0.04 – 0.39 | 2.42 | 0.016 | 3775.39 |
| vs race [Black] | -0.38 | -0.54 – -0.22 | -4.62 | <0.001 | 1267.54 |
| vs race [Hispanic] | 0.02 | -0.15 – 0.20 | 0.28 | 0.777 | 1249.79 |
| vs race [Other] | -0.10 | -0.28 – 0.09 | -1.01 | 0.312 | 1251.76 |
| vs age | 0.01 | 0.00 – 0.01 | 4.07 | <0.001 | 1259.18 |
| male female | -0.34 | -0.44 – -0.24 | -6.57 | <0.001 | 1249.26 |
| nonbinary mf | 0.21 | -0.46 – 0.87 | 0.61 | 0.543 | 1248.43 |
|
pDem Rep × representation Dem |
0.05 | -0.05 – 0.16 | 0.95 | 0.340 | 3784.58 |
|
pDem Rep × representation Rep |
0.03 | -0.07 – 0.12 | 0.51 | 0.611 | 3970.33 |
| pDem Rep × wave [2] | 1.33 | 0.94 – 1.73 | 6.61 | <0.001 | 3790.80 |
| pDem Rep × wave [3] | 1.29 | 0.90 – 1.69 | 6.40 | <0.001 | 3794.46 |
| pDem Rep × wave [4] | 1.39 | 1.00 – 1.79 | 6.86 | <0.001 | 3806.76 |
|
representation Dem × wave [2] |
-0.15 | -0.20 – -0.10 | -5.98 | <0.001 | 3774.34 |
|
representation Dem × wave [3] |
-0.15 | -0.20 – -0.10 | -5.75 | <0.001 | 3774.29 |
|
representation Dem × wave [4] |
-0.16 | -0.21 – -0.11 | -6.28 | <0.001 | 3776.98 |
|
representation Rep × wave [2] |
0.09 | 0.04 – 0.13 | 3.56 | <0.001 | 3775.77 |
|
representation Rep × wave [3] |
0.12 | 0.07 – 0.17 | 4.93 | <0.001 | 3776.39 |
|
representation Rep × wave [4] |
0.16 | 0.11 – 0.20 | 6.49 | <0.001 | 3778.95 |
|
(pDem Rep × representation Dem) × wave [2] |
-0.19 | -0.29 – -0.08 | -3.53 | <0.001 | 3790.77 |
|
(pDem Rep × representation Dem) × wave [3] |
-0.12 | -0.22 – -0.01 | -2.17 | 0.030 | 3787.91 |
|
(pDem Rep × representation Dem) × wave [4] |
-0.06 | -0.16 – 0.04 | -1.13 | 0.260 | 3794.15 |
|
(pDem Rep × representation Rep) × wave [2] |
0.13 | 0.03 – 0.23 | 2.55 | 0.011 | 3791.15 |
|
(pDem Rep × representation Rep) × wave [3] |
0.06 | -0.04 – 0.16 | 1.10 | 0.273 | 3791.58 |
|
(pDem Rep × representation Rep) × wave [4] |
0.04 | -0.07 – 0.14 | 0.69 | 0.488 | 3800.96 |
| Random Effects | |||||
| σ2 | 0.52 | ||||
| τ00 pid | 0.65 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.164 / 0.627 | ||||
Main Model centered at Wave 2, with by party ID-specific Effects
PEL.m10.w2.r <- lmer(voteconfidence ~ (pRep_D) * (representation_Dem + representation_Rep) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m10.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.61 | 2.25 – 2.97 | 14.16 | <0.001 | 1905.82 |
| pRep D | 0.15 | -0.19 – 0.49 | 0.85 | 0.393 | 4419.89 |
| representation Dem | 0.06 | -0.01 – 0.13 | 1.78 | 0.075 | 3265.63 |
| representation Rep | 0.15 | 0.08 – 0.21 | 4.30 | <0.001 | 3405.83 |
| wave2 1 | -0.86 | -1.10 – -0.62 | -7.07 | <0.001 | 3783.91 |
| wave2 3 | -0.17 | -0.41 – 0.07 | -1.40 | 0.162 | 3780.16 |
| wave2 4 | -0.35 | -0.59 – -0.11 | -2.90 | 0.004 | 3789.58 |
| vs race [Black] | -0.40 | -0.56 – -0.24 | -4.84 | <0.001 | 1266.66 |
| vs race [Hispanic] | 0.02 | -0.15 – 0.19 | 0.26 | 0.795 | 1249.98 |
| vs race [Other] | -0.10 | -0.28 – 0.09 | -1.03 | 0.303 | 1252.59 |
| vs age | 0.01 | 0.00 – 0.01 | 4.23 | <0.001 | 1260.42 |
| male female | -0.34 | -0.44 – -0.24 | -6.66 | <0.001 | 1249.75 |
| nonbinary mf | 0.21 | -0.45 – 0.87 | 0.63 | 0.532 | 1248.74 |
|
pRep D × representation Dem |
0.09 | -0.01 – 0.19 | 1.82 | 0.069 | 3847.09 |
|
pRep D × representation Rep |
-0.13 | -0.23 – -0.04 | -2.88 | 0.004 | 4014.36 |
| pRep D × wave2 1 | 1.03 | 0.68 – 1.39 | 5.68 | <0.001 | 3797.65 |
| pRep D × wave2 3 | 0.00 | -0.36 – 0.36 | 0.00 | 0.997 | 3790.97 |
| pRep D × wave2 4 | 0.07 | -0.28 – 0.43 | 0.40 | 0.690 | 3804.94 |
|
representation Dem × wave2 1 |
0.24 | 0.17 – 0.31 | 6.91 | <0.001 | 3787.02 |
|
representation Dem × wave2 3 |
0.04 | -0.03 – 0.11 | 1.23 | 0.220 | 3779.16 |
|
representation Dem × wave2 4 |
0.06 | -0.01 – 0.13 | 1.68 | 0.092 | 3780.21 |
|
representation Rep × wave2 1 |
-0.21 | -0.28 – -0.15 | -6.31 | <0.001 | 3787.30 |
|
representation Rep × wave2 3 |
-0.00 | -0.07 – 0.06 | -0.09 | 0.928 | 3780.15 |
|
representation Rep × wave2 4 |
0.04 | -0.03 – 0.11 | 1.21 | 0.225 | 3789.40 |
|
(pRep D × representation Dem) × wave2 1 |
-0.20 | -0.30 – -0.10 | -3.82 | <0.001 | 3793.67 |
|
(pRep D × representation Dem) × wave2 3 |
-0.07 | -0.17 – 0.04 | -1.28 | 0.201 | 3784.74 |
|
(pRep D × representation Dem) × wave2 4 |
-0.15 | -0.25 – -0.05 | -2.86 | 0.004 | 3785.22 |
|
(pRep D × representation Rep) × wave2 1 |
0.19 | 0.10 – 0.29 | 4.11 | <0.001 | 3795.33 |
|
(pRep D × representation Rep) × wave2 3 |
0.07 | -0.03 – 0.16 | 1.40 | 0.162 | 3783.17 |
|
(pRep D × representation Rep) × wave2 4 |
0.07 | -0.02 – 0.17 | 1.55 | 0.121 | 3795.82 |
| Random Effects | |||||
| σ2 | 0.53 | ||||
| τ00 pid | 0.64 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.166 / 0.622 | ||||
PEL.m10.w2.d <- lmer(voteconfidence ~ (pDem_R) * (representation_Dem + representation_Rep) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(PEL.m10.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.62 | 2.27 – 2.97 | 14.77 | <0.001 | 1779.20 |
| pDem R | 0.20 | -0.15 – 0.56 | 1.12 | 0.261 | 4436.59 |
| representation Dem | 0.20 | 0.14 – 0.27 | 6.03 | <0.001 | 3218.31 |
| representation Rep | -0.00 | -0.07 – 0.06 | -0.13 | 0.893 | 3135.68 |
| wave2 1 | -0.20 | -0.42 – 0.02 | -1.80 | 0.072 | 3784.92 |
| wave2 3 | -0.15 | -0.37 – 0.07 | -1.32 | 0.188 | 3776.33 |
| wave2 4 | -0.36 | -0.59 – -0.14 | -3.23 | 0.001 | 3779.81 |
| vs race [Black] | -0.35 | -0.51 – -0.18 | -4.18 | <0.001 | 1266.97 |
| vs race [Hispanic] | 0.04 | -0.14 – 0.21 | 0.40 | 0.687 | 1250.61 |
| vs race [Other] | -0.08 | -0.27 – 0.11 | -0.86 | 0.392 | 1251.64 |
| vs age | 0.01 | 0.00 – 0.01 | 3.91 | <0.001 | 1261.43 |
| male female | -0.33 | -0.43 – -0.23 | -6.36 | <0.001 | 1249.47 |
| nonbinary mf | 0.16 | -0.51 – 0.83 | 0.48 | 0.632 | 1249.75 |
|
pDem R × representation Dem |
-0.15 | -0.25 – -0.05 | -3.02 | 0.003 | 4096.19 |
|
pDem R × representation Rep |
0.14 | 0.04 – 0.23 | 2.75 | 0.006 | 4148.93 |
| pDem R × wave2 1 | -0.97 | -1.35 – -0.60 | -5.12 | <0.001 | 3798.71 |
| pDem R × wave2 3 | -0.06 | -0.43 – 0.31 | -0.32 | 0.750 | 3788.19 |
| pDem R × wave2 4 | 0.14 | -0.23 – 0.51 | 0.76 | 0.447 | 3799.34 |
|
representation Dem × wave2 1 |
0.14 | 0.08 – 0.21 | 4.38 | <0.001 | 3784.50 |
|
representation Dem × wave2 3 |
-0.03 | -0.09 – 0.04 | -0.86 | 0.389 | 3778.37 |
|
representation Dem × wave2 4 |
-0.06 | -0.12 – 0.01 | -1.73 | 0.085 | 3779.52 |
|
representation Rep × wave2 1 |
-0.03 | -0.09 – 0.03 | -1.07 | 0.282 | 3784.07 |
|
representation Rep × wave2 3 |
0.07 | 0.01 – 0.13 | 2.36 | 0.018 | 3774.89 |
|
representation Rep × wave2 4 |
0.11 | 0.05 – 0.17 | 3.75 | <0.001 | 3777.71 |
|
(pDem R × representation Dem) × wave2 1 |
0.09 | -0.01 – 0.19 | 1.83 | 0.067 | 3797.74 |
|
(pDem R × representation Dem) × wave2 3 |
0.07 | -0.03 – 0.17 | 1.29 | 0.197 | 3787.54 |
|
(pDem R × representation Dem) × wave2 4 |
0.10 | -0.00 – 0.20 | 1.95 | 0.052 | 3787.20 |
|
(pDem R × representation Rep) × wave2 1 |
-0.12 | -0.22 – -0.02 | -2.32 | 0.021 | 3796.32 |
|
(pDem R × representation Rep) × wave2 3 |
-0.07 | -0.17 – 0.03 | -1.44 | 0.150 | 3784.67 |
|
(pDem R × representation Rep) × wave2 4 |
-0.10 | -0.20 – -0.00 | -1.97 | 0.049 | 3792.17 |
| Random Effects | |||||
| σ2 | 0.53 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.55 | ||||
| N pid | 1265 | ||||
| Observations | 5060 | ||||
| Marginal R2 / Conditional R2 | 0.154 / 0.622 | ||||
5. Emotions
Emotions were measured with the following question:
How much do you feel each of the following emotions right now? (1 = Not at all, 5 = Very much)
1 = Fear
2 = Anger
3 = Hope
4 = Pride
(5 = Disgust)
a. Emotions Over Time
Both over time
d.emo <- d %>%
pivot_longer(cols = c(Pos_Emo, Neg_Emo),
names_to = "Emotion_Valence",
values_to = "emotion")
ggplot(d.emo[!is.na(d.emo$party_factor),],
aes(x = wave,
y = emotion,
color = Emotion_Valence,
group = Emotion_Valence,
fill = Emotion_Valence)) +
geom_jitter(height = .15, width = .3,alpha = .2, size = .2) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed") +
stat_summary(geom = "errorbar", color = "black", width = .1) +
stat_summary(geom = "point", fun = "mean") +
facet_wrap(~party_factor) +
labs(x = "Wave",
y = "Self-Reported Emotion") +
scale_color_manual("Emotion Valence",
values = c("#172869FF","#F28A8AFF"),
labels = c("Negative","Positive")) +
scale_fill_manual("Emotion Valence",
values = c("#172869FF","#F28A8AFF"),
labels = c("Negative","Positive")) +
theme_bw() 
emo.wave_label <- c("Neg_Emo" = "Negative", "Pos_Emo" = "Positive",
`1` = "Wave 1", `2` = "Wave 2", `3` = "Wave 3", `4` = "Wave 4")
d.emo$Emotion_Valence <- factor(d.emo$Emotion_Valence, levels = c("Pos_Emo","Neg_Emo"))Positive Emotions
ggplot(d[!is.na(d$party_factor),],
aes(x = Pos_Emo,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
# geom_text(data = na.omit(n_bins_govtrust), aes(label = paste0("n = ", n)),
# x = 1.4, y = 1.2,
# size = 3,
# inherit.aes = FALSE) +
labs(x = "Positive Emotions",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
theme_bw()
party.wave_label <- c("Democrat" = "Democrat", "Independent" = "Independent", "Republican" = "Republican",
`1` = "Wave 1", `2` = "Wave 2", `3` = "Wave 3", `4` = "Wave 4")Negative Emotions
ggplot(d, aes(x=Neg_Emo, y = voteconfidence)) +
geom_smooth(method="lm", color="#69b3a2") +
theme_bw() +
labs(x = "Negative Emotions",
y = "Perceived Election Legitimacy") +
coord_cartesian(ylim = c(1,5))
ggplot(d[!is.na(d$party_factor),],
aes(x = Neg_Emo,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
# geom_text(data = na.omit(n_bins_govtrust), aes(label = paste0("n = ", n)),
# x = 1.4, y = 1.2,
# size = 3,
# inherit.aes = FALSE) +
labs(x = "Negative Emotions",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
theme_bw()
Summary
Emotions over time unfold in the expected direction: Democrats experienced a sharp decrease in positive emotions (b = -0.76, 95% CI = [-0.84, -0.69], t(6055.0) = -19.94, p < .001) and increase in negative emotions over time (b = 0.85, 95% CI = [0.78, 0.93], t(6024.0) = 23.14, p < .001), with the steepest change in both positive (b = -0.89, 95% CI = [-0.96, -0.82], t(5862.4) = -25.15, p < .001) and negative (b = 0.94, 95% CI = [0.87, 1.01], t(5846.5) = 27.46, p < .001) emotions occurring from wave 1 to wave 2. Republicans showed the opposite pattern for both positive (b = 0.41, 95% CI = [0.33, 0.50], t(6030.5) = 9.74, p < .001) and negative (b = -0.50, 95% CI = [-0.59, -0.42], t(6000.1) = -12.27, p < .001) emotions over time, with the steepest changes also occurring between wave 1 and wave 2 for both positive (b = 0.90, 95% CI = [0.82, 0.98], t(5794.7) = 22.61, p < .001) and negative (b = -0.77, 95% CI = [-0.70, -0.85], t(5781.8) = -20.17, p < .001) emotions.
Relative to partisans, Independents expressed lower levels of positive (b = -0.43, 95% CI = [-0.53, -0.33], t(5927.9) = -8.72, p < .001), but not negative (p = .29), affect collapsing across time. Positive emotions trended down linearly for Independents (b = -0.33, 95% CI = [-0.48, -0.17], t(6214.9) = -4.17, p < .001), and negative emotions also decreased linearly over time (b = -0.50, 95% CI = [-0.59, -0.42], t(6000.1) = -12.27, p < .001).
Models
Main Models
posemo.m1 <- lmer(Pos_Emo ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 1.79 | 1.60 – 1.98 | 18.21 | <0.001 | 2648.28 |
| pDem Rep | 1.04 | 0.96 – 1.12 | 26.85 | <0.001 | 3616.85 |
| pParty Ind | -0.43 | -0.53 – -0.33 | -8.72 | <0.001 | 5927.91 |
| wave lin | -0.23 | -0.29 – -0.16 | -6.96 | <0.001 | 6116.97 |
| wave quad | 0.02 | -0.02 – 0.07 | 1.00 | 0.317 | 5814.35 |
| wave cub | 0.01 | -0.05 – 0.07 | 0.36 | 0.717 | 5704.81 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.20 |
| pDem Rep × wave lin | 1.17 | 1.06 – 1.29 | 20.54 | <0.001 | 6053.20 |
| pDem Rep × wave quad | 0.92 | 0.83 – 1.00 | 21.44 | <0.001 | 5793.99 |
| pDem Rep × wave cub | -0.77 | -0.87 – -0.67 | -14.95 | <0.001 | 5684.42 |
| pParty Ind × wave lin | -0.16 | -0.32 – 0.01 | -1.85 | 0.064 | 6216.29 |
| pParty Ind × wave quad | 0.00 | -0.12 – 0.13 | 0.05 | 0.963 | 5931.10 |
| pParty Ind × wave cub | 0.12 | -0.03 – 0.28 | 1.56 | 0.118 | 5804.93 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
negemo.m1 <- lmer(Neg_Emo ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.06 | 2.87 – 3.25 | 31.28 | <0.001 | 2649.65 |
| pDem Rep | -1.20 | -1.27 – -1.12 | -31.14 | <0.001 | 3684.13 |
| pParty Ind | -0.05 | -0.15 – 0.04 | -1.07 | 0.285 | 6090.63 |
| wave lin | 0.22 | 0.16 – 0.28 | 6.94 | <0.001 | 6083.34 |
| wave quad | -0.10 | -0.15 – -0.06 | -4.35 | <0.001 | 5795.54 |
| wave cub | -0.14 | -0.19 – -0.08 | -4.68 | <0.001 | 5689.86 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pDem Rep × wave lin | -1.36 | -1.47 – -1.25 | -24.55 | <0.001 | 6021.66 |
| pDem Rep × wave quad | -0.74 | -0.82 – -0.66 | -17.84 | <0.001 | 5775.02 |
| pDem Rep × wave cub | 0.85 | 0.75 – 0.95 | 17.00 | <0.001 | 5670.17 |
| pParty Ind × wave lin | 0.13 | -0.03 – 0.29 | 1.57 | 0.116 | 6177.55 |
| pParty Ind × wave quad | -0.13 | -0.26 – -0.01 | -2.17 | 0.030 | 5905.50 |
| pParty Ind × wave cub | -0.02 | -0.17 – 0.13 | -0.25 | 0.804 | 5783.76 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
Main Models with by party ID Effects
posemo.m1.d <- lmer(Pos_Emo ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1.d, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 1.41 | 1.22 – 1.61 | 14.20 | <0.001 | 2674.24 |
| pDem R | 1.04 | 0.96 – 1.12 | 26.85 | <0.001 | 3616.85 |
| pDem I | 0.09 | -0.01 – 0.19 | 1.69 | 0.092 | 5609.27 |
| wave lin | -0.76 | -0.84 – -0.69 | -19.94 | <0.001 | 6055.02 |
| wave quad | -0.43 | -0.49 – -0.38 | -15.22 | <0.001 | 5793.98 |
| wave cub | 0.36 | 0.29 – 0.42 | 10.30 | <0.001 | 5674.49 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.20 |
| pDem R × wave lin | 1.17 | 1.06 – 1.29 | 20.54 | <0.001 | 6053.20 |
| pDem R × wave quad | 0.92 | 0.83 – 1.00 | 21.44 | <0.001 | 5793.99 |
| pDem R × wave cub | -0.77 | -0.87 – -0.67 | -14.95 | <0.001 | 5684.42 |
| pDem I × wave lin | 0.43 | 0.26 – 0.60 | 4.89 | <0.001 | 6203.37 |
| pDem I × wave quad | 0.46 | 0.33 – 0.59 | 6.91 | <0.001 | 5917.81 |
| pDem I × wave cub | -0.26 | -0.42 – -0.10 | -3.20 | 0.001 | 5797.95 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
posemo.m1.r <- lmer(Pos_Emo ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.45 | 2.25 – 2.65 | 24.22 | <0.001 | 2698.83 |
| pRep D | -1.04 | -1.12 – -0.96 | -26.85 | <0.001 | 3616.85 |
| pRep I | -0.95 | -1.06 – -0.85 | -17.62 | <0.001 | 5547.74 |
| wave lin | 0.41 | 0.33 – 0.50 | 9.74 | <0.001 | 6030.49 |
| wave quad | 0.48 | 0.42 – 0.54 | 15.18 | <0.001 | 5772.86 |
| wave cub | -0.42 | -0.49 – -0.34 | -10.88 | <0.001 | 5667.11 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.20 |
| pRep D × wave lin | -1.17 | -1.29 – -1.06 | -20.54 | <0.001 | 6053.20 |
| pRep D × wave quad | -0.92 | -1.00 – -0.83 | -21.44 | <0.001 | 5793.99 |
| pRep D × wave cub | 0.77 | 0.67 – 0.87 | 14.95 | <0.001 | 5684.42 |
| pRep I × wave lin | -0.74 | -0.92 – -0.57 | -8.26 | <0.001 | 6195.80 |
| pRep I × wave quad | -0.46 | -0.59 – -0.32 | -6.68 | <0.001 | 5916.84 |
| pRep I × wave cub | 0.51 | 0.35 – 0.67 | 6.08 | <0.001 | 5788.66 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
negemo.m1.d <- lmer(Neg_Emo ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1.d, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.68 | 3.48 – 3.87 | 37.08 | <0.001 | 2676.63 |
| pDem R | -1.20 | -1.27 – -1.12 | -31.14 | <0.001 | 3684.13 |
| pDem I | -0.65 | -0.75 – -0.55 | -12.61 | <0.001 | 5756.66 |
| wave lin | 0.85 | 0.78 – 0.93 | 23.14 | <0.001 | 6024.02 |
| wave quad | 0.31 | 0.26 – 0.36 | 11.26 | <0.001 | 5775.62 |
| wave cub | -0.55 | -0.62 – -0.49 | -16.56 | <0.001 | 5661.03 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pDem R × wave lin | -1.36 | -1.47 – -1.25 | -24.55 | <0.001 | 6021.66 |
| pDem R × wave quad | -0.74 | -0.82 – -0.66 | -17.84 | <0.001 | 5775.02 |
| pDem R × wave cub | 0.85 | 0.75 – 0.95 | 17.00 | <0.001 | 5670.17 |
| pDem I × wave lin | -0.55 | -0.72 – -0.38 | -6.46 | <0.001 | 6165.17 |
| pDem I × wave quad | -0.50 | -0.63 – -0.38 | -7.80 | <0.001 | 5892.94 |
| pDem I × wave cub | 0.41 | 0.25 – 0.56 | 5.11 | <0.001 | 5777.20 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
negemo.m1.r <- lmer(Neg_Emo ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1.r, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.48 | 2.28 – 2.68 | 24.60 | <0.001 | 2702.54 |
| pRep D | 1.20 | 1.12 – 1.27 | 31.14 | <0.001 | 3684.13 |
| pRep I | 0.55 | 0.44 – 0.65 | 10.26 | <0.001 | 5695.34 |
| wave lin | -0.50 | -0.59 – -0.42 | -12.27 | <0.001 | 6000.09 |
| wave quad | -0.43 | -0.49 – -0.37 | -13.90 | <0.001 | 5754.68 |
| wave cub | 0.30 | 0.22 – 0.37 | 7.99 | <0.001 | 5653.87 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pRep D × wave lin | 1.36 | 1.25 – 1.47 | 24.55 | <0.001 | 6021.66 |
| pRep D × wave quad | 0.74 | 0.66 – 0.82 | 17.84 | <0.001 | 5775.02 |
| pRep D × wave cub | -0.85 | -0.95 – -0.75 | -17.00 | <0.001 | 5670.17 |
| pRep I × wave lin | 0.81 | 0.64 – 0.98 | 9.27 | <0.001 | 6158.01 |
| pRep I × wave quad | 0.23 | 0.10 – 0.36 | 3.55 | <0.001 | 5891.85 |
| pRep I × wave cub | -0.44 | -0.60 – -0.28 | -5.48 | <0.001 | 5768.42 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
By-party ID Main Models centered at Wave 2
posemo.m1.w2.d <- lmer(Pos_Emo ~ (pDem_R + pDem_I) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 1.21 | 1.01 – 1.41 | 11.86 | <0.001 | 2914.08 |
| pDem R | 1.59 | 1.49 – 1.69 | 31.67 | <0.001 | 6940.29 |
| pDem I | 0.34 | 0.20 – 0.49 | 4.60 | <0.001 | 8026.81 |
| wave2 1 | 0.89 | 0.82 – 0.96 | 25.20 | <0.001 | 5860.70 |
| wave2 3 | -0.02 | -0.10 – 0.05 | -0.64 | 0.520 | 5647.20 |
| wave2 4 | -0.05 | -0.13 – 0.04 | -1.06 | 0.287 | 5836.92 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.21 |
| pDem R × wave2 1 | -1.79 | -1.89 – -1.68 | -33.56 | <0.001 | 5844.87 |
| pDem R × wave2 3 | -0.19 | -0.30 – -0.07 | -3.29 | 0.001 | 5662.79 |
| pDem R × wave2 4 | -0.23 | -0.36 – -0.10 | -3.47 | 0.001 | 5853.73 |
| pDem I × wave2 1 | -0.77 | -0.93 – -0.60 | -9.29 | <0.001 | 6046.57 |
| pDem I × wave2 3 | -0.05 | -0.23 – 0.13 | -0.52 | 0.600 | 5798.00 |
| pDem I × wave2 4 | -0.20 | -0.40 – -0.00 | -1.99 | 0.047 | 5948.95 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
posemo.m1.w2.r <- lmer(Pos_Emo ~ (pRep_D + pRep_I) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.80 | 2.59 – 3.00 | 26.95 | <0.001 | 2973.35 |
| pRep D | -1.59 | -1.69 – -1.49 | -31.67 | <0.001 | 6940.29 |
| pRep I | -1.25 | -1.40 – -1.10 | -16.40 | <0.001 | 8014.47 |
| wave2 1 | -0.90 | -0.98 – -0.82 | -22.61 | <0.001 | 5794.70 |
| wave2 3 | -0.21 | -0.29 – -0.13 | -5.03 | <0.001 | 5657.62 |
| wave2 4 | -0.27 | -0.37 – -0.18 | -5.66 | <0.001 | 5854.10 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.20 |
| pRep D × wave2 1 | 1.79 | 1.68 – 1.89 | 33.56 | <0.001 | 5844.87 |
| pRep D × wave2 3 | 0.19 | 0.07 – 0.30 | 3.29 | 0.001 | 5662.79 |
| pRep D × wave2 4 | 0.23 | 0.10 – 0.36 | 3.47 | 0.001 | 5853.73 |
| pRep I × wave2 1 | 1.02 | 0.86 – 1.19 | 12.11 | <0.001 | 6031.64 |
| pRep I × wave2 3 | 0.14 | -0.04 – 0.32 | 1.49 | 0.137 | 5789.58 |
| pRep I × wave2 4 | 0.02 | -0.18 – 0.23 | 0.24 | 0.814 | 5953.56 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
negemo.m1.w2.d <- lmer(Neg_Emo ~ (pDem_R + pDem_I) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1.w2.d, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.89 | 3.70 – 4.09 | 38.46 | <0.001 | 2902.63 |
| pDem R | -1.65 | -1.75 – -1.55 | -33.45 | <0.001 | 6894.36 |
| pDem I | -0.97 | -1.11 – -0.82 | -13.30 | <0.001 | 8032.30 |
| wave2 1 | -0.94 | -1.01 – -0.87 | -27.46 | <0.001 | 5846.51 |
| wave2 3 | -0.13 | -0.20 – -0.06 | -3.48 | 0.001 | 5634.47 |
| wave2 4 | 0.19 | 0.11 – 0.28 | 4.49 | <0.001 | 5811.68 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pDem R × wave2 1 | 1.71 | 1.61 – 1.81 | 33.25 | <0.001 | 5830.03 |
| pDem R × wave2 3 | 0.17 | 0.06 – 0.28 | 3.13 | 0.002 | 5649.20 |
| pDem R × wave2 4 | -0.07 | -0.19 – 0.05 | -1.10 | 0.272 | 5827.59 |
| pDem I × wave2 1 | 0.94 | 0.79 – 1.10 | 11.84 | <0.001 | 6022.96 |
| pDem I × wave2 3 | 0.13 | -0.04 – 0.30 | 1.48 | 0.138 | 5776.70 |
| pDem I × wave2 4 | 0.19 | -0.00 – 0.39 | 1.93 | 0.054 | 5918.42 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
negemo.m1.w2.r <- lmer(Neg_Emo ~ (pRep_D + pRep_I) * (wave2_1 + wave2_3 + wave2_4)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1.w2.r, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.25 | 2.04 – 2.45 | 21.75 | <0.001 | 2961.13 |
| pRep D | 1.65 | 1.55 – 1.75 | 33.45 | <0.001 | 6894.36 |
| pRep I | 0.68 | 0.54 – 0.83 | 9.18 | <0.001 | 8020.85 |
| wave2 1 | 0.77 | 0.70 – 0.85 | 20.17 | <0.001 | 5781.80 |
| wave2 3 | 0.04 | -0.04 – 0.12 | 1.07 | 0.284 | 5644.46 |
| wave2 4 | 0.12 | 0.03 – 0.21 | 2.59 | 0.010 | 5828.07 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pRep D × wave2 1 | -1.71 | -1.81 – -1.61 | -33.25 | <0.001 | 5830.03 |
| pRep D × wave2 3 | -0.17 | -0.28 – -0.06 | -3.13 | 0.002 | 5649.20 |
| pRep D × wave2 4 | 0.07 | -0.05 – 0.19 | 1.10 | 0.272 | 5827.59 |
| pRep I × wave2 1 | -0.77 | -0.93 – -0.61 | -9.41 | <0.001 | 6008.21 |
| pRep I × wave2 3 | -0.04 | -0.21 – 0.14 | -0.44 | 0.657 | 5768.80 |
| pRep I × wave2 4 | 0.26 | 0.06 – 0.46 | 2.58 | 0.010 | 5922.91 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
Effects for Independents
posemo.m1.i <- lmer(Pos_Emo ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(posemo.m1.i, show.stat = T, show.df = T, df.method = "satterthwaite")| Pos_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.45 | 2.25 – 2.65 | 24.22 | <0.001 | 2698.83 |
| pRep D | -1.04 | -1.12 – -0.96 | -26.85 | <0.001 | 3616.85 |
| pRep I | -0.95 | -1.06 – -0.85 | -17.62 | <0.001 | 5547.74 |
| wave lin | 0.41 | 0.33 – 0.50 | 9.74 | <0.001 | 6030.49 |
| wave quad | 0.48 | 0.42 – 0.54 | 15.18 | <0.001 | 5772.86 |
| wave cub | -0.42 | -0.49 – -0.34 | -10.88 | <0.001 | 5667.11 |
| vs race [Black] | 0.53 | 0.42 – 0.65 | 9.21 | <0.001 | 2626.79 |
| vs race [Hispanic] | 0.40 | 0.28 – 0.52 | 6.60 | <0.001 | 2613.67 |
| vs race [Other] | -0.03 | -0.17 – 0.11 | -0.39 | 0.697 | 2524.15 |
| vs age | 0.01 | 0.01 – 0.01 | 7.93 | <0.001 | 2556.40 |
| male female | -0.04 | -0.11 – 0.03 | -1.03 | 0.301 | 2532.99 |
| nonbinary mf | 0.66 | 0.22 – 1.11 | 2.94 | 0.003 | 2687.20 |
| pRep D × wave lin | -1.17 | -1.29 – -1.06 | -20.54 | <0.001 | 6053.20 |
| pRep D × wave quad | -0.92 | -1.00 – -0.83 | -21.44 | <0.001 | 5793.99 |
| pRep D × wave cub | 0.77 | 0.67 – 0.87 | 14.95 | <0.001 | 5684.42 |
| pRep I × wave lin | -0.74 | -0.92 – -0.57 | -8.26 | <0.001 | 6195.80 |
| pRep I × wave quad | -0.46 | -0.59 – -0.32 | -6.68 | <0.001 | 5916.84 |
| pRep I × wave cub | 0.51 | 0.35 – 0.67 | 6.08 | <0.001 | 5788.66 |
| Random Effects | |||||
| σ2 | 0.71 | ||||
| τ00 pid | 0.60 | ||||
| ICC | 0.46 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.226 / 0.580 | ||||
negemo.m1.i <- lmer(Neg_Emo ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub)
+ vs_race + vs_age + male_female + nonbinary_mf
+ (1 | pid),
data = d)
tab_model(negemo.m1.i, show.stat = T, show.df = T, df.method = "satterthwaite")| Neg_Emo | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.48 | 2.28 – 2.68 | 24.60 | <0.001 | 2702.54 |
| pRep D | 1.20 | 1.12 – 1.27 | 31.14 | <0.001 | 3684.13 |
| pRep I | 0.55 | 0.44 – 0.65 | 10.26 | <0.001 | 5695.34 |
| wave lin | -0.50 | -0.59 – -0.42 | -12.27 | <0.001 | 6000.09 |
| wave quad | -0.43 | -0.49 – -0.37 | -13.90 | <0.001 | 5754.68 |
| wave cub | 0.30 | 0.22 – 0.37 | 7.99 | <0.001 | 5653.87 |
| vs race [Black] | -0.39 | -0.51 – -0.28 | -6.78 | <0.001 | 2632.33 |
| vs race [Hispanic] | -0.07 | -0.18 – 0.05 | -1.08 | 0.278 | 2615.48 |
| vs race [Other] | -0.08 | -0.23 – 0.06 | -1.17 | 0.242 | 2530.14 |
| vs age | -0.01 | -0.01 – -0.00 | -5.06 | <0.001 | 2560.42 |
| male female | 0.18 | 0.10 – 0.25 | 4.75 | <0.001 | 2538.07 |
| nonbinary mf | -1.00 | -1.44 – -0.56 | -4.43 | <0.001 | 2686.98 |
| pRep D × wave lin | 1.36 | 1.25 – 1.47 | 24.55 | <0.001 | 6021.66 |
| pRep D × wave quad | 0.74 | 0.66 – 0.82 | 17.84 | <0.001 | 5775.02 |
| pRep D × wave cub | -0.85 | -0.95 – -0.75 | -17.00 | <0.001 | 5670.17 |
| pRep I × wave lin | 0.81 | 0.64 – 0.98 | 9.27 | <0.001 | 6158.01 |
| pRep I × wave quad | 0.23 | 0.10 – 0.36 | 3.55 | <0.001 | 5891.85 |
| pRep I × wave cub | -0.44 | -0.60 – -0.28 | -5.48 | <0.001 | 5768.42 |
| Random Effects | |||||
| σ2 | 0.66 | ||||
| τ00 pid | 0.61 | ||||
| ICC | 0.48 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.250 / 0.609 | ||||
b. Emotions as predictor of PEL
ggplot(d.emo[!is.na(d.emo$party_factor),],
aes(x = emotion,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(Emotion_Valence~wave, labeller = as_labeller(emo.wave_label)) +
labs(x = "Emotions",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Partisan
Identity",
values = c("#1696d2","grey","#db2b27")) +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
theme_bw()
Summary
The impact of emotions on perceived election legitimacy also follows the hypothesized directions. Positive emotions are positively related to perceived election legitimacy (b = 0.22, p < .001), though more strongly for Republicans than for Democrats (b = 0.06, p = .008). The positive emotions-perceived election legitimacy relationship does not change linearly over time, though there is a 3-way interaction of positive emotions, party ID (Dem vs. Rep), and quadratic time (b = 0.08, p = .016), such that the emotions-perceived election legitimacy slope is more similar for Republicans and Democrats in waves 1 and 4 than in waves 2 and 3.
Negative emotions are negatively related to perceived election legitimacy (b = -0.13, p < .001), though more strongly for Democrats than for Republicans (b = -0.07, p = .002). The negative emotions-perceived election legitimacy relationship does not change linearly over time, but there is a 3-way interaction of negative emotions, party ID (Dem vs. Rep), and linear time (b = 0.25, p < .001), such that the emotions-perceived election legitimacy slopes for Democrats and Republicans become more similar over time.
Models
Main Models
Positive Emotions
PEL.m3 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * Pos_Emo
+ vs_age + vs_race + nonbinary_mf +
male_female
+ (1 | pid),
data = d)
tab_model(PEL.m3, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 2.51 | 2.31 – 2.70 | 25.18 | <0.001 | 2900.82 |
| pDem Rep | -0.57 | -0.70 – -0.43 | -8.18 | <0.001 | 7885.84 |
| pParty Ind | -0.27 | -0.45 – -0.10 | -3.13 | 0.002 | 8044.98 |
| wave lin | 0.19 | 0.06 – 0.33 | 2.82 | 0.005 | 6150.41 |
| wave quad | 0.12 | 0.02 – 0.22 | 2.30 | 0.022 | 5942.05 |
| wave cub | -0.01 | -0.14 – 0.11 | -0.22 | 0.822 | 5890.54 |
| Pos Emo | 0.22 | 0.19 – 0.24 | 16.91 | <0.001 | 7833.08 |
| vs age | 0.00 | 0.00 – 0.01 | 3.71 | <0.001 | 2577.83 |
| vs race [Black] | -0.32 | -0.43 – -0.21 | -5.63 | <0.001 | 2693.71 |
| vs race [Hispanic] | -0.10 | -0.22 – 0.02 | -1.70 | 0.089 | 2625.13 |
| vs race [Other] | -0.08 | -0.22 – 0.06 | -1.18 | 0.239 | 2526.89 |
| nonbinary mf | 0.37 | -0.07 – 0.80 | 1.65 | 0.099 | 2665.81 |
| male female | -0.37 | -0.45 – -0.30 | -10.16 | <0.001 | 2533.45 |
| pDem Rep × wave lin | 1.24 | 0.98 – 1.50 | 9.41 | <0.001 | 6155.45 |
| pDem Rep × wave quad | 0.11 | -0.09 – 0.30 | 1.09 | 0.274 | 6015.22 |
| pDem Rep × wave cub | -0.45 | -0.69 – -0.21 | -3.74 | <0.001 | 5888.76 |
| pParty Ind × wave lin | -0.59 | -0.94 – -0.25 | -3.40 | 0.001 | 6212.11 |
| pParty Ind × wave quad | -0.06 | -0.32 – 0.19 | -0.48 | 0.630 | 5978.96 |
| pParty Ind × wave cub | -0.02 | -0.33 – 0.29 | -0.12 | 0.901 | 5946.64 |
| pDem Rep × Pos Emo | 0.06 | 0.01 – 0.10 | 2.65 | 0.008 | 7743.08 |
| pParty Ind × Pos Emo | -0.00 | -0.07 – 0.06 | -0.06 | 0.954 | 7687.80 |
| wave lin × Pos Emo | 0.02 | -0.03 – 0.07 | 0.84 | 0.401 | 6178.76 |
| wave quad × Pos Emo | 0.03 | -0.01 – 0.07 | 1.69 | 0.091 | 5984.28 |
| wave cub × Pos Emo | -0.03 | -0.08 – 0.02 | -1.21 | 0.225 | 5953.83 |
|
(pDem Rep × wave lin) × Pos Emo |
-0.08 | -0.16 – 0.01 | -1.73 | 0.084 | 6203.44 |
|
(pDem Rep × wave quad) × Pos Emo |
0.07 | 0.01 – 0.14 | 2.28 | 0.023 | 6034.77 |
|
(pDem Rep × wave cub) × Pos Emo |
0.02 | -0.06 – 0.10 | 0.50 | 0.615 | 5914.29 |
|
(pParty Ind × wave lin) × Pos Emo |
0.26 | 0.12 – 0.40 | 3.64 | <0.001 | 6209.64 |
|
(pParty Ind × wave quad) × Pos Emo |
0.05 | -0.05 – 0.15 | 0.96 | 0.336 | 6018.18 |
|
(pParty Ind × wave cub) × Pos Emo |
0.03 | -0.09 – 0.16 | 0.54 | 0.588 | 5997.04 |
| Random Effects | |||||
| σ2 | 0.51 | ||||
| τ00 pid | 0.64 | ||||
| ICC | 0.56 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.184 / 0.640 | ||||
Negative Emotions
# Negative Emotions
PEL.m4 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * Neg_Emo
+ vs_age + vs_race + nonbinary_mf +
male_female
+ (1 | pid),
data = d)
tab_model(PEL.m4, show.stat = T, show.df = T, df.method = "satterthwaite")| voteconfidence | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 3.28 | 3.08 – 3.49 | 31.46 | <0.001 | 3131.72 |
| pDem Rep | -0.22 | -0.35 – -0.09 | -3.34 | 0.001 | 7627.98 |
| pParty Ind | -0.56 | -0.72 – -0.39 | -6.57 | <0.001 | 8048.78 |
| wave lin | 0.28 | 0.15 – 0.41 | 4.27 | <0.001 | 6151.86 |
| wave quad | 0.26 | 0.16 – 0.35 | 5.25 | <0.001 | 5894.52 |
| wave cub | -0.23 | -0.34 – -0.11 | -3.92 | <0.001 | 5837.68 |
| Neg Emo | -0.13 | -0.15 – -0.10 | -10.07 | <0.001 | 7831.34 |
| vs age | 0.01 | 0.00 – 0.01 | 4.66 | <0.001 | 2553.75 |
| vs race [Black] | -0.26 | -0.38 – -0.15 | -4.53 | <0.001 | 2647.64 |
| vs race [Hispanic] | -0.03 | -0.15 – 0.09 | -0.51 | 0.610 | 2592.45 |
| vs race [Other] | -0.10 | -0.24 – 0.04 | -1.38 | 0.167 | 2518.12 |
| nonbinary mf | 0.41 | -0.03 – 0.85 | 1.81 | 0.070 | 2663.72 |
| male female | -0.36 | -0.43 – -0.28 | -9.54 | <0.001 | 2531.67 |
| pDem Rep × wave lin | 0.46 | 0.22 – 0.70 | 3.81 | <0.001 | 6106.44 |
| pDem Rep × wave quad | 0.59 | 0.42 – 0.77 | 6.65 | <0.001 | 5911.01 |
| pDem Rep × wave cub | -0.52 | -0.73 – -0.31 | -4.83 | <0.001 | 5829.81 |
| pParty Ind × wave lin | 0.18 | -0.15 – 0.52 | 1.06 | 0.288 | 6207.15 |
| pParty Ind × wave quad | 0.05 | -0.20 – 0.29 | 0.40 | 0.691 | 5941.01 |
| pParty Ind × wave cub | 0.18 | -0.11 – 0.48 | 1.22 | 0.224 | 5902.97 |
| pDem Rep × Neg Emo | -0.07 | -0.12 – -0.03 | -3.11 | 0.002 | 7659.23 |
| pParty Ind × Neg Emo | 0.07 | 0.01 – 0.13 | 2.43 | 0.015 | 7668.92 |
| wave lin × Neg Emo | -0.02 | -0.07 – 0.03 | -0.82 | 0.414 | 6244.14 |
| wave quad × Neg Emo | -0.03 | -0.07 – 0.01 | -1.69 | 0.092 | 5967.79 |
| wave cub × Neg Emo | 0.06 | 0.01 – 0.11 | 2.55 | 0.011 | 5884.01 |
|
(pDem Rep × wave lin) × Neg Emo |
0.25 | 0.16 – 0.35 | 5.07 | <0.001 | 6197.11 |
|
(pDem Rep × wave quad) × Neg Emo |
-0.08 | -0.15 – -0.00 | -2.03 | 0.042 | 6017.66 |
|
(pDem Rep × wave cub) × Neg Emo |
0.05 | -0.04 – 0.14 | 1.01 | 0.311 | 5893.99 |
|
(pParty Ind × wave lin) × Neg Emo |
-0.10 | -0.23 – 0.03 | -1.57 | 0.116 | 6314.73 |
|
(pParty Ind × wave quad) × Neg Emo |
0.00 | -0.09 – 0.10 | 0.08 | 0.936 | 6001.01 |
|
(pParty Ind × wave cub) × Neg Emo |
-0.05 | -0.17 – 0.07 | -0.82 | 0.411 | 5930.62 |
| Random Effects | |||||
| σ2 | 0.52 | ||||
| τ00 pid | 0.66 | ||||
| ICC | 0.56 | ||||
| N pid | 2606 | ||||
| Observations | 8079 | ||||
| Marginal R2 / Conditional R2 | 0.157 / 0.628 | ||||