2024USElecStudy_Trust+Democracy
Alex Flores
2025-07-10
Nature collection on Trust & Democracy: Call for Papers
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)
d <- read.csv("/Users/af13/Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_combined_cleancases.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))
# 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))
# 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$wave, exclude = F))repeated measures
# emotions
d$Neg_Emo <- rowMeans(d[,c("Emotions_1", "Emotions_2", "Emotions_5")], na.rm = T)
psych::alpha(d[,c("Emotions_1", "Emotions_2", "Emotions_5")])
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)
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)
# 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$antidemNorms <- rowMeans(d[,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4", "demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")], na.rm = T)
psych::alpha(d[,c("demNorms_Reps_1", "demNorms_Reps_2", "demNorms_Reps_3", "demNorms_Reps_4")])
psych::alpha(d[,c("demNorms_Dems_1", "demNorms_Dems_2", "demNorms_Dems_3", "demNorms_Dems_4")])
d$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
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")Analyses
Analytic codes
### 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)
### political ID variables - Selected
d$Liberal <- ifelse(d$polID_Liberal_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Leftist <- ifelse(d$polID_Leftist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Progressive <- ifelse(d$polID_Progressive_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Conservative <- ifelse(d$polID_Conservative_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$RightWing <- ifelse(d$polID_RightWing_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Moderate <- ifelse(d$polID_Moderate_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Socialist <- ifelse(d$polID_Socialist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Nationalist <- ifelse(d$polID_Nationalist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Libertarian <- ifelse(d$polID_Libertarian_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Feminist <- ifelse(d$polID_Feminist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Prolife <- ifelse(d$polID_Prolife_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Prochoice <- ifelse(d$polID_Prochoice_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$MAGA <- ifelse(d$polID_MAGA_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$AntiTrump <- ifelse(d$polID_AntiTrump_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Populist <- ifelse(d$polID_Populist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
d$Environmentalist <- ifelse(d$polID_Environmentalist_select == 1, 1, ifelse(d$polID_Other_select == 1, -1, 0))
### political ID variables - NOT Selected
d$nLiberal <- ifelse(d$polID_Liberal_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nLeftist <- ifelse(d$polID_Leftist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nProgressive <- ifelse(d$polID_Progressive_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nConservative <- ifelse(d$polID_Conservative_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nRightWing <- ifelse(d$polID_RightWing_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nModerate <- ifelse(d$polID_Moderate_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nSocialist <- ifelse(d$polID_Socialist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nNationalist <- ifelse(d$polID_Nationalist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nLibertarian <- ifelse(d$polID_Libertarian_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nFeminist <- ifelse(d$polID_Feminist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nProlife <- ifelse(d$polID_Prolife_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nProchoice <- ifelse(d$polID_Prochoice_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nMAGA <- ifelse(d$polID_MAGA_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nAntiTrump <- ifelse(d$polID_AntiTrump_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nPopulist <- ifelse(d$polID_Populist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -1, 0))
d$nEnvironmentalist <- ifelse(d$polID_Environmentalist_not_select == 1, 1, ifelse(d$polID_Other_not_select == 1, -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] <- -1/4
d$wave.quad[d$wave == 2] <- 1/4
d$wave.quad[d$wave == 3] <- 1/4
d$wave.quad[d$wave == 4] <- -1/4
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$wave == 4] <- 1
## wave 4
d$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)DV: Perceived Election Legitimacy (PEL)
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)
These are highly correlated, but there are still interesting differences in patterns of own vs. national vote confidence.
##
## Pearson's product-moment correlation
##
## data: d$voteconf_natl and d$voteconf_self
## t = 113.95, df = 7522, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7872919 0.8038743
## sample estimates:
## cor
## 0.7957322ggplot(d, aes(x=voteconfidence)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Perceived Election Legitimacy")
wave_label <- c(`1` = "Wave 1", `2` = "Wave 2", `3` = "Wave 3", `4` = "Wave 4")
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")
1. PEL over time: Outcome bias
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconfidence,
color = wave,
group = 1)) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed", show.legend = F) +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary(geom = "point", fun = "mean", show.legend = F) +
facet_wrap(~party_factor) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
scale_x_discrete(labels = c("Wave 1","Wave 2", "Wave 3", "Wave 4")) +
theme(legend.position = "none") +
theme_bw()
Mean (and SD) for PEL by party over time
# Average PEL
kable(round(tapply(d$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
Democrats begin with higher PEL (M = 3.87) than Republicans (M = 2.67; b = 0.12, p = .002) and Independents (b = 0.49, p < .001). This falls linearly over time (b = -0.43, p < .001) until, in wave 4, Democrats express lower PEL (M = 3.39) than Republicans (M = 3.65; b = -0.37, p < .001), though Democrats’ PEL is still higher than Independents’ (M = 2.95; b = 0.27, p = .001).
Republicans’ PEL increases over time (b = 0.77, p < .001), though this increase takes place primarily from Wave 1 to Wave 2 (pre- and post-election), after which Republicans’ PEL remains stable.
Models
PEL.m1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d)
summary(PEL.m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22252.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6388 -0.5025 0.0716 0.5367 3.8673
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5456 0.7387
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.346e+00 2.172e-02 3.333e+03 154.049 < 2e-16 ***
## pDem_Rep -1.187e-01 3.761e-02 3.946e+03 -3.157 0.00161 **
## pParty_Ind -4.260e-01 4.732e-02 6.837e+03 -9.004 < 2e-16 ***
## wave.lin 1.731e-01 2.854e-02 5.967e+03 6.063 1.42e-09 ***
## wave.quad 4.668e-01 4.281e-02 5.730e+03 10.903 < 2e-16 ***
## wave.cub -1.028e-01 2.625e-02 5.641e+03 -3.916 9.10e-05 ***
## pDem_Rep:wave.lin 1.203e+00 5.036e-02 5.908e+03 23.884 < 2e-16 ***
## pDem_Rep:wave.quad 1.096e+00 7.497e-02 5.708e+03 14.616 < 2e-16 ***
## pDem_Rep:wave.cub -6.436e-01 4.528e-02 5.622e+03 -14.212 < 2e-16 ***
## pParty_Ind:wave.lin 5.468e-03 7.448e-02 6.042e+03 0.073 0.94148
## pParty_Ind:wave.quad 1.798e-02 1.124e-01 5.817e+03 0.160 0.87297
## pParty_Ind:wave.cub 9.580e-02 6.918e-02 5.715e+03 1.385 0.16618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp pPrt_I wav.ln wav.qd wav.cb pDm_Rp:wv.l
## pDem_Rep 0.067
## pParty_Ind 0.470 -0.038
## wave.lin 0.181 -0.012 0.121
## wave.quad -0.009 -0.009 0.029 -0.216
## wave.cub -0.058 0.010 -0.043 -0.072 0.173
## pDm_Rp:wv.l 0.009 0.164 0.006 0.061 -0.006 -0.008
## pDm_Rp:wv.q -0.008 -0.046 0.001 -0.008 0.058 0.009 -0.213
## pDm_Rp:wv.c -0.002 -0.053 -0.007 -0.008 0.008 0.057 -0.113
## pPrty_Ind:wv.l 0.098 -0.013 0.193 0.565 -0.124 -0.021 -0.037
## pPrty_Ind:wv.q 0.016 0.005 0.010 -0.121 0.574 0.102 0.005
## pPrty_Ind:wv.c -0.030 0.004 -0.064 -0.020 0.103 0.588 0.005
## pDm_Rp:wv.q pDm_Rp:wv.c pPrty_Ind:wv.l pPrty_Ind:wv.q
## pDem_Rep
## pParty_Ind
## wave.lin
## wave.quad
## wave.cub
## pDm_Rp:wv.l
## pDm_Rp:wv.q
## pDm_Rp:wv.c 0.166
## pPrty_Ind:wv.l 0.002 0.006
## pPrty_Ind:wv.q -0.036 -0.004 -0.213
## pPrty_Ind:wv.c -0.003 -0.031 -0.063 0.173Simple-Effects Models
PEL.m1.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d)
summary(PEL.m1.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22252.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6388 -0.5025 0.0716 0.5367 3.8673
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5456 0.7387
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.54761 0.02599 3278.96323 136.506 < 2e-16 ***
## pDem_R -0.11875 0.03761 3945.76029 -3.157 0.00161 **
## pDem_I -0.48540 0.05026 6458.38963 -9.659 < 2e-16 ***
## wave.lin -0.43016 0.03362 5912.25574 -12.796 < 2e-16 ***
## wave.quad -0.08711 0.05016 5711.50355 -1.737 0.08249 .
## wave.cub 0.18705 0.03034 5615.94669 6.165 7.55e-10 ***
## pDem_R:wave.lin 1.20278 0.05036 5907.77340 23.884 < 2e-16 ***
## pDem_R:wave.quad 1.09584 0.07498 5708.04469 14.616 < 2e-16 ***
## pDem_R:wave.cub -0.64358 0.04528 5621.54064 -14.212 < 2e-16 ***
## pDem_I:wave.lin 0.60686 0.07775 6030.66239 7.805 6.94e-15 ***
## pDem_I:wave.quad 0.56590 0.11726 5806.94293 4.826 1.43e-06 ***
## pDem_I:wave.cub -0.22599 0.07212 5710.32856 -3.134 0.00174 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDem_R pDem_I wav.ln wav.qd wav.cb pDm_R:wv.l pDm_R:wv.q
## pDem_R -0.645
## pDem_I -0.417 0.339
## wave.lin 0.178 -0.123 -0.087
## wave.quad -0.038 0.023 0.024 -0.226
## wave.cub -0.064 0.045 0.033 -0.108 0.170
## pDm_R:wv.ln -0.115 0.164 0.067 -0.670 0.150 0.073
## pDm_R:wv.qd 0.026 -0.046 -0.016 0.151 -0.671 -0.114 -0.213
## pDm_R:wv.cb 0.041 -0.053 -0.027 0.074 -0.114 -0.673 -0.113 0.166
## pDm_I:wv.ln -0.061 0.040 0.191 -0.438 0.099 0.047 0.289 -0.067
## pDm_I:wv.qd 0.012 -0.010 0.006 0.097 -0.436 -0.075 -0.063 0.286
## pDm_I:wv.cb 0.024 -0.013 -0.065 0.048 -0.073 -0.430 -0.031 0.049
## pDm_R:wv.c pDm_I:wv.l pDm_I:wv.q
## pDem_R
## pDem_I
## wave.lin
## wave.quad
## wave.cub
## pDm_R:wv.ln
## pDm_R:wv.qd
## pDm_R:wv.cb
## pDm_I:wv.ln -0.031
## pDm_I:wv.qd 0.049 -0.216
## pDm_I:wv.cb 0.284 -0.066 0.174PEL.m1.d.w4 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave4_1 + wave4_2 + wave4_3) + (1 | pid),
data = d)
summary(PEL.m1.d.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave4_1 + wave4_2 + wave4_3) +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22255
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6388 -0.5025 0.0716 0.5367 3.8673
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5456 0.7387
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.30754 0.03916 7794.37948 84.463 < 2e-16 ***
## pDem_R 0.36958 0.05732 8040.70971 6.448 1.20e-10 ***
## pDem_I -0.26695 0.08268 7653.03724 -3.229 0.00125 **
## wave4_1 0.52368 0.03835 5918.61903 13.656 < 2e-16 ***
## wave4_2 0.23230 0.03876 5725.41975 5.994 2.17e-09 ***
## wave4_3 0.20427 0.03935 5663.54152 5.191 2.16e-07 ***
## pDem_R:wave4_1 -1.52457 0.05751 5910.07827 -26.512 < 2e-16 ***
## pDem_R:wave4_2 -0.19327 0.05767 5736.33181 -3.351 0.00081 ***
## pDem_R:wave4_3 -0.23546 0.05864 5671.36641 -4.015 6.01e-05 ***
## pDem_I:wave4_1 -0.71985 0.08783 6027.56666 -8.196 3.00e-16 ***
## pDem_I:wave4_2 -0.11570 0.09018 5812.06667 -1.283 0.19956
## pDem_I:wave4_3 -0.03826 0.09205 5719.23173 -0.416 0.67770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDem_R pDem_I wav4_1 wav4_2 wav4_3 pD_R:4_1 pD_R:4_2
## pDem_R -0.662
## pDem_I -0.430 0.314
## wave4_1 -0.695 0.477 0.331
## wave4_2 -0.658 0.449 0.316 0.674
## wave4_3 -0.635 0.434 0.307 0.649 0.642
## pDm_R:wv4_1 0.462 -0.692 -0.223 -0.670 -0.450 -0.434
## pDm_R:wv4_2 0.442 -0.667 -0.213 -0.454 -0.673 -0.432 0.670
## pDm_R:wv4_3 0.426 -0.641 -0.206 -0.437 -0.432 -0.673 0.644 0.641
## pDm_I:wv4_1 0.297 -0.199 -0.743 -0.444 -0.299 -0.288 0.292 0.199
## pDm_I:wv4_2 0.278 -0.188 -0.687 -0.293 -0.436 -0.280 0.192 0.289
## pDm_I:wv4_3 0.271 -0.181 -0.661 -0.283 -0.279 -0.436 0.185 0.184
## pD_R:4_3 pD_I:4_1 pD_I:4_2
## pDem_R
## pDem_I
## wave4_1
## wave4_2
## wave4_3
## pDm_R:wv4_1
## pDm_R:wv4_2
## pDm_R:wv4_3
## pDm_I:wv4_1 0.192
## pDm_I:wv4_2 0.185 0.667
## pDm_I:wv4_3 0.288 0.635 0.618PEL.m1.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d)
summary(PEL.m1.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22252.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6388 -0.5025 0.0716 0.5367 3.8673
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5456 0.7387
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.42886 0.02879 3357.66307 119.086 < 2e-16 ***
## pRep_D 0.11875 0.03761 3945.76029 3.157 0.00161 **
## pRep_I -0.36665 0.05157 6387.73603 -7.110 1.28e-12 ***
## wave.lin 0.77262 0.03739 5892.03242 20.664 < 2e-16 ***
## wave.quad 1.00873 0.05558 5690.58870 18.149 < 2e-16 ***
## wave.cub -0.45653 0.03350 5607.98516 -13.628 < 2e-16 ***
## pRep_D:wave.lin -1.20278 0.05036 5907.77340 -23.884 < 2e-16 ***
## pRep_D:wave.quad -1.09584 0.07498 5708.04469 -14.616 < 2e-16 ***
## pRep_D:wave.cub 0.64358 0.04528 5621.54064 14.212 < 2e-16 ***
## pRep_I:wave.lin -0.59592 0.07949 6026.20443 -7.497 7.49e-14 ***
## pRep_I:wave.quad -0.52994 0.11979 5804.83277 -4.424 9.86e-06 ***
## pRep_I:wave.cub 0.41759 0.07346 5701.86096 5.685 1.38e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pRep_D pRep_I wav.ln wav.qd wav.cb pRp_D:wv.l pRp_D:wv.q
## pRep_D -0.724
## pRep_I -0.464 0.399
## wave.lin 0.149 -0.110 -0.069
## wave.quad -0.054 0.042 0.030 -0.205
## wave.cub -0.042 0.030 0.016 -0.115 0.164
## pRp_D:wv.ln -0.111 0.164 0.055 -0.745 0.152 0.086
## pRp_D:wv.qd 0.037 -0.046 -0.018 0.152 -0.743 -0.122 -0.213
## pRp_D:wv.cb 0.032 -0.053 -0.012 0.086 -0.121 -0.742 -0.113 0.166
## pRp_I:wv.ln -0.073 0.065 0.188 -0.477 0.095 0.056 0.351 -0.070
## pRp_I:wv.qd 0.021 -0.019 0.001 0.097 -0.473 -0.078 -0.072 0.346
## pRp_I:wv.cb 0.024 -0.020 -0.060 0.055 -0.075 -0.463 -0.040 0.054
## pRp_D:wv.c pRp_I:wv.l pRp_I:wv.q
## pRep_D
## pRep_I
## wave.lin
## wave.quad
## wave.cub
## pRp_D:wv.ln
## pRp_D:wv.qd
## pRp_D:wv.cb
## pRp_I:wv.ln -0.041
## pRp_I:wv.qd 0.056 -0.211
## pRp_I:wv.cb 0.338 -0.069 0.171PEL.m1.i <- lmer(voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d)
summary(PEL.m1.i)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22252.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6388 -0.5025 0.0716 0.5367 3.8673
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5456 0.7387
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.06220 0.04594 5982.29753 66.651 < 2e-16 ***
## pInd_R 0.36665 0.05157 6387.73603 7.110 1.28e-12 ***
## pInd_D 0.48540 0.05026 6458.38963 9.659 < 2e-16 ***
## wave.lin 0.17670 0.06988 6042.54678 2.529 0.01147 *
## wave.quad 0.47879 0.10552 5805.06195 4.538 5.80e-06 ***
## wave.cub -0.03894 0.06512 5706.40012 -0.598 0.54991
## pInd_R:wave.lin 0.59592 0.07949 6026.20443 7.497 7.49e-14 ***
## pInd_R:wave.quad 0.52994 0.11979 5804.83277 4.424 9.86e-06 ***
## pInd_R:wave.cub -0.41759 0.07346 5701.86095 -5.685 1.38e-08 ***
## pInd_D:wave.lin -0.60686 0.07775 6030.66239 -7.805 6.94e-15 ***
## pInd_D:wave.quad -0.56590 0.11726 5806.94293 -4.826 1.43e-06 ***
## pInd_D:wave.cub 0.22599 0.07212 5710.32856 3.134 0.00174 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pInd_R pInd_D wav.ln wav.qd wav.cb pInd_R:wv.l pInd_R:wv.q
## pInd_R -0.832
## pInd_D -0.858 0.727
## wave.lin 0.196 -0.176 -0.170
## wave.quad 0.017 -0.017 -0.018 -0.214
## wave.cub -0.064 0.060 0.057 -0.055 0.174
## pInd_R:wv.l -0.165 0.188 0.144 -0.882 0.189 0.050
## pInd_R:wv.q -0.014 0.001 0.016 0.188 -0.886 -0.153 -0.211
## pInd_R:wv.c 0.053 -0.060 -0.047 0.050 -0.155 -0.890 -0.069 0.171
## pInd_D:wv.l -0.174 0.157 0.191 -0.902 0.193 0.051 0.795 -0.169
## pInd_D:wv.q -0.013 0.013 0.006 0.193 -0.904 -0.158 -0.171 0.800
## pInd_D:wv.c 0.057 -0.054 -0.065 0.050 -0.158 -0.907 -0.045 0.139
## pInd_R:wv.c pInd_D:wv.l pInd_D:wv.q
## pInd_R
## pInd_D
## wave.lin
## wave.quad
## wave.cub
## pInd_R:wv.l
## pInd_R:wv.q
## pInd_R:wv.c
## pInd_D:wv.l -0.045
## pInd_D:wv.q 0.140 -0.216
## pInd_D:wv.c 0.807 -0.066 0.1742. Moderator: Gov’t trust
ggplot(d) +
geom_smooth(aes(x = trustGovt,
y = voteconfidence),
color = "#69b3a2",
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Perceived Election Legitimacy") +
theme_bw() +
coord_cartesian(ylim = c(1,5))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = trustGovt,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5))
ggplot(d[!is.na(d$party_factor) & !is.na(d$trustGovt_bins),]) +
geom_smooth(aes(x = trustGovt,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() 
ggplot(d[!is.na(d$party_factor) & !is.na(d$trustGovt_bins),],
aes(x = wave,
y = voteconfidence,
color = wave,
group = 1)) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed", show.legend = F) +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary(geom = "point", fun = "mean", show.legend = F) +
facet_grid(trustGovt_bins~party_factor) +
geom_text(data = na.omit(n_bins_govtrust), aes(label = paste0("n = ", n)),
x = 1.3, y = 1.2,
size = 3,
inherit.aes = FALSE) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) +
scale_x_discrete(labels = c("Wave 1","Wave 2", "Wave 3", "Wave 4")) +
theme(legend.position = "none") +
theme_bw()
Summary
Again, some differences by vote type (visible in graph above)—most notable differences occur for those high in governmental trust.
Overall, relationship between gov’t trust and PEL is positive (b = 0.26, p < .001). The strength of this trend slightly flattens over time (b = -0.08, p = .009), though it is still significant and positive in wave 4 (b = 0.25, p < .001). (Wave 1 effect: b = 0.39, p < .001.)
The trust-PEL relationship is stronger for Republicans (b = 0.53, p < .001) than for Democrats (b = 0.24, p < .001) in wave 1 (comparison: b = 0.30, p < .001); by wave 4, this relationship has flipped, such that the trust-PEL slope is steeper for Democrats (b = 0.29, p < .001) than for Republicans (b = 0.15, p < .001), though the overall gov’t trust-PEL relationship is smaller in magnitude relative to Wave 1; b = -0.14, p < .001.
Models
PEL.m2 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.386e+00 2.141e-02 3.707e+03 158.119
## pDem_Rep -6.729e-02 3.650e-02 4.100e+03 -1.844
## pParty_Ind -3.619e-01 4.842e-02 6.980e+03 -7.473
## wave.lin 2.849e-01 3.291e-02 6.218e+03 8.658
## wave.quad 3.719e-01 4.751e-02 5.708e+03 7.829
## wave.cub -6.512e-02 2.856e-02 5.630e+03 -2.280
## trustGovt.c 2.608e-01 1.519e-02 7.976e+03 17.177
## pDem_Rep:wave.lin 7.135e-01 5.610e-02 6.418e+03 12.718
## pDem_Rep:wave.quad 1.156e+00 7.982e-02 5.674e+03 14.477
## pDem_Rep:wave.cub -5.675e-01 4.769e-02 5.573e+03 -11.901
## pParty_Ind:wave.lin 7.548e-02 8.653e-02 6.184e+03 0.872
## pParty_Ind:wave.quad 5.392e-02 1.261e-01 5.791e+03 0.427
## pParty_Ind:wave.cub 1.173e-01 7.589e-02 5.707e+03 1.546
## pDem_Rep:trustGovt.c 9.052e-03 2.549e-02 7.973e+03 0.355
## pParty_Ind:trustGovt.c 3.921e-02 3.826e-02 7.864e+03 1.025
## wave.lin:trustGovt.c -8.169e-02 3.141e-02 6.620e+03 -2.601
## wave.quad:trustGovt.c -2.388e-01 4.473e-02 5.789e+03 -5.339
## wave.cub:trustGovt.c 1.193e-01 2.769e-02 5.741e+03 4.308
## pDem_Rep:wave.lin:trustGovt.c -3.265e-01 5.190e-02 6.500e+03 -6.291
## pDem_Rep:wave.quad:trustGovt.c -2.813e-01 7.383e-02 5.779e+03 -3.811
## pDem_Rep:wave.cub:trustGovt.c 2.136e-01 4.496e-02 5.695e+03 4.752
## pParty_Ind:wave.lin:trustGovt.c 1.030e-01 8.292e-02 6.626e+03 1.242
## pParty_Ind:wave.quad:trustGovt.c -1.890e-02 1.188e-01 5.837e+03 -0.159
## pParty_Ind:wave.cub:trustGovt.c 9.326e-02 7.389e-02 5.800e+03 1.262
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.06532 .
## pParty_Ind 8.77e-14 ***
## wave.lin < 2e-16 ***
## wave.quad 5.82e-15 ***
## wave.cub 0.02262 *
## trustGovt.c < 2e-16 ***
## pDem_Rep:wave.lin < 2e-16 ***
## pDem_Rep:wave.quad < 2e-16 ***
## pDem_Rep:wave.cub < 2e-16 ***
## pParty_Ind:wave.lin 0.38305
## pParty_Ind:wave.quad 0.66908
## pParty_Ind:wave.cub 0.12221
## pDem_Rep:trustGovt.c 0.72255
## pParty_Ind:trustGovt.c 0.30557
## wave.lin:trustGovt.c 0.00933 **
## wave.quad:trustGovt.c 9.68e-08 ***
## wave.cub:trustGovt.c 1.67e-05 ***
## pDem_Rep:wave.lin:trustGovt.c 3.36e-10 ***
## pDem_Rep:wave.quad:trustGovt.c 0.00014 ***
## pDem_Rep:wave.cub:trustGovt.c 2.06e-06 ***
## pParty_Ind:wave.lin:trustGovt.c 0.21427
## pParty_Ind:wave.quad:trustGovt.c 0.87366
## pParty_Ind:wave.cub:trustGovt.c 0.20694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
PEL.m2.w4 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave4_3 + wave4_2 + wave4_1) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave4_3 + wave4_2 +
## wave4_1) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.451e+00 3.643e-02 7.974e+03 94.744
## pDem_Rep 1.425e-01 5.906e-02 7.976e+03 2.412
## pParty_Ind -3.669e-01 9.240e-02 7.164e+03 -3.971
## wave4_3 6.590e-02 3.876e-02 5.689e+03 1.700
## wave4_2 -1.145e-02 3.802e-02 5.914e+03 -0.301
## wave4_1 -3.175e-01 3.726e-02 6.096e+03 -8.522
## trustGovt.c 2.499e-01 2.931e-02 6.369e+03 8.525
## pDem_Rep:wave4_3 -2.625e-02 6.137e-02 5.678e+03 -0.428
## pDem_Rep:wave4_2 1.845e-01 6.387e-02 6.048e+03 2.889
## pDem_Rep:wave4_1 -9.973e-01 6.349e-02 6.242e+03 -15.708
## pParty_Ind:wave4_3 9.607e-02 1.044e-01 5.746e+03 0.920
## pParty_Ind:wave4_2 -5.898e-02 1.005e-01 5.907e+03 -0.587
## pParty_Ind:wave4_1 -1.682e-02 9.822e-02 6.105e+03 -0.171
## pDem_Rep:trustGovt.c -1.373e-01 4.748e-02 6.324e+03 -2.891
## pParty_Ind:trustGovt.c 7.211e-02 7.741e-02 6.292e+03 0.932
## wave4_3:trustGovt.c -9.544e-03 3.628e-02 5.822e+03 -0.263
## wave4_2:trustGovt.c -8.797e-02 3.553e-02 6.225e+03 -2.476
## wave4_1:trustGovt.c 1.413e-01 3.449e-02 6.396e+03 4.097
## pDem_Rep:wave4_3:trustGovt.c 1.012e-01 5.820e-02 5.859e+03 1.739
## pDem_Rep:wave4_2:trustGovt.c 5.079e-02 5.822e-02 6.151e+03 0.872
## pDem_Rep:wave4_1:trustGovt.c 4.333e-01 5.782e-02 6.347e+03 7.494
## pParty_Ind:wave4_3:trustGovt.c 3.475e-02 9.683e-02 5.832e+03 0.359
## pParty_Ind:wave4_2:trustGovt.c -1.100e-01 9.406e-02 6.226e+03 -1.170
## pParty_Ind:wave4_1:trustGovt.c -5.636e-02 9.088e-02 6.401e+03 -0.620
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.01588 *
## pParty_Ind 7.22e-05 ***
## wave4_3 0.08917 .
## wave4_2 0.76328
## wave4_1 < 2e-16 ***
## trustGovt.c < 2e-16 ***
## pDem_Rep:wave4_3 0.66880
## pDem_Rep:wave4_2 0.00388 **
## pDem_Rep:wave4_1 < 2e-16 ***
## pParty_Ind:wave4_3 0.35752
## pParty_Ind:wave4_2 0.55721
## pParty_Ind:wave4_1 0.86401
## pDem_Rep:trustGovt.c 0.00385 **
## pParty_Ind:trustGovt.c 0.35163
## wave4_3:trustGovt.c 0.79249
## wave4_2:trustGovt.c 0.01330 *
## wave4_1:trustGovt.c 4.23e-05 ***
## pDem_Rep:wave4_3:trustGovt.c 0.08215 .
## pDem_Rep:wave4_2:trustGovt.c 0.38304
## pDem_Rep:wave4_1:trustGovt.c 7.60e-14 ***
## pParty_Ind:wave4_3:trustGovt.c 0.71971
## pParty_Ind:wave4_2:trustGovt.c 0.24220
## pParty_Ind:wave4_1:trustGovt.c 0.53517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.w1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave1_3 + wave1_2 + wave1_4) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave1_3 + wave1_2 +
## wave1_4) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.13390 0.02494 5868.47662 125.633
## pDem_Rep -0.85482 0.04679 6957.86829 -18.271
## pParty_Ind -0.38377 0.05744 7916.39550 -6.681
## wave1_3 0.38340 0.03046 6047.44429 12.589
## wave1_2 0.30605 0.02747 5743.88938 11.141
## wave1_4 0.31750 0.03726 6096.48620 8.522
## trustGovt.c 0.39122 0.02121 7889.86641 18.444
## pDem_Rep:wave1_3 0.97103 0.05191 6076.29221 18.704
## pDem_Rep:wave1_2 1.18182 0.05107 5680.51607 23.142
## pDem_Rep:wave1_4 0.99728 0.06349 6241.75871 15.708
## pParty_Ind:wave1_3 0.11290 0.08038 6090.44534 1.404
## pParty_Ind:wave1_2 -0.04216 0.07078 5884.29367 -0.596
## pParty_Ind:wave1_4 0.01682 0.09822 6104.71915 0.171
## pDem_Rep:trustGovt.c 0.29603 0.03812 7801.13655 7.766
## pParty_Ind:trustGovt.c 0.01575 0.05319 7715.46552 0.296
## wave1_3:trustGovt.c -0.15087 0.03059 6265.85995 -4.932
## wave1_2:trustGovt.c -0.22930 0.02605 5712.37243 -8.802
## wave1_4:trustGovt.c -0.14132 0.03449 6395.56604 -4.097
## pDem_Rep:wave1_3:trustGovt.c -0.33210 0.05010 6146.85558 -6.629
## pDem_Rep:wave1_2:trustGovt.c -0.38250 0.04535 5670.27930 -8.434
## pDem_Rep:wave1_4:trustGovt.c -0.43329 0.05782 6346.68932 -7.494
## pParty_Ind:wave1_3:trustGovt.c 0.09111 0.08136 6327.19459 1.120
## pParty_Ind:wave1_2:trustGovt.c -0.05365 0.06857 5823.92427 -0.782
## pParty_Ind:wave1_4:trustGovt.c 0.05636 0.09088 6401.46452 0.620
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep < 2e-16 ***
## pParty_Ind 2.54e-11 ***
## wave1_3 < 2e-16 ***
## wave1_2 < 2e-16 ***
## wave1_4 < 2e-16 ***
## trustGovt.c < 2e-16 ***
## pDem_Rep:wave1_3 < 2e-16 ***
## pDem_Rep:wave1_2 < 2e-16 ***
## pDem_Rep:wave1_4 < 2e-16 ***
## pParty_Ind:wave1_3 0.160
## pParty_Ind:wave1_2 0.551
## pParty_Ind:wave1_4 0.864
## pDem_Rep:trustGovt.c 9.16e-15 ***
## pParty_Ind:trustGovt.c 0.767
## wave1_3:trustGovt.c 8.34e-07 ***
## wave1_2:trustGovt.c < 2e-16 ***
## wave1_4:trustGovt.c 4.23e-05 ***
## pDem_Rep:wave1_3:trustGovt.c 3.68e-11 ***
## pDem_Rep:wave1_2:trustGovt.c < 2e-16 ***
## pDem_Rep:wave1_4:trustGovt.c 7.60e-14 ***
## pParty_Ind:wave1_3:trustGovt.c 0.263
## pParty_Ind:wave1_2:trustGovt.c 0.434
## pParty_Ind:wave1_4:trustGovt.c 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.540e+00 2.562e-02 3.682e+03 138.171 < 2e-16
## pDem_R -6.729e-02 3.650e-02 4.100e+03 -1.844 0.06532
## pDem_I -3.955e-01 5.139e-02 6.704e+03 -7.696 1.61e-14
## wave.lin -9.698e-02 4.101e-02 6.665e+03 -2.365 0.01808
## wave.quad -2.238e-01 5.654e-02 5.686e+03 -3.958 7.64e-05
## wave.cub 1.795e-01 3.337e-02 5.597e+03 5.381 7.71e-08
## trustGovt.c 2.433e-01 1.678e-02 7.965e+03 14.499 < 2e-16
## pDem_R:wave.lin 7.135e-01 5.610e-02 6.418e+03 12.718 < 2e-16
## pDem_R:wave.quad 1.156e+00 7.982e-02 5.674e+03 14.477 < 2e-16
## pDem_R:wave.cub -5.675e-01 4.769e-02 5.573e+03 -11.901 < 2e-16
## pDem_I:wave.lin 4.322e-01 9.154e-02 6.254e+03 4.722 2.39e-06
## pDem_I:wave.quad 6.317e-01 1.323e-01 5.779e+03 4.776 1.84e-06
## pDem_I:wave.cub -1.665e-01 7.943e-02 5.702e+03 -2.096 0.03616
## pDem_R:trustGovt.c 9.052e-03 2.549e-02 7.973e+03 0.355 0.72255
## pDem_I:trustGovt.c 4.373e-02 3.973e-02 7.860e+03 1.101 0.27111
## wave.lin:trustGovt.c 4.722e-02 3.529e-02 6.553e+03 1.338 0.18092
## wave.quad:trustGovt.c -9.188e-02 4.978e-02 5.779e+03 -1.846 0.06499
## wave.cub:trustGovt.c -1.863e-02 3.038e-02 5.723e+03 -0.613 0.53966
## pDem_R:wave.lin:trustGovt.c -3.265e-01 5.190e-02 6.500e+03 -6.291 3.36e-10
## pDem_R:wave.quad:trustGovt.c -2.813e-01 7.383e-02 5.779e+03 -3.811 0.00014
## pDem_R:wave.cub:trustGovt.c 2.136e-01 4.496e-02 5.695e+03 4.752 2.06e-06
## pDem_I:wave.lin:trustGovt.c -6.024e-02 8.631e-02 6.628e+03 -0.698 0.48523
## pDem_I:wave.quad:trustGovt.c -1.596e-01 1.234e-01 5.831e+03 -1.293 0.19620
## pDem_I:wave.cub:trustGovt.c 2.001e-01 7.675e-02 5.803e+03 2.607 0.00916
##
## (Intercept) ***
## pDem_R .
## pDem_I ***
## wave.lin *
## wave.quad ***
## wave.cub ***
## trustGovt.c ***
## pDem_R:wave.lin ***
## pDem_R:wave.quad ***
## pDem_R:wave.cub ***
## pDem_I:wave.lin ***
## pDem_I:wave.quad ***
## pDem_I:wave.cub *
## pDem_R:trustGovt.c
## pDem_I:trustGovt.c
## wave.lin:trustGovt.c
## wave.quad:trustGovt.c .
## wave.cub:trustGovt.c
## pDem_R:wave.lin:trustGovt.c ***
## pDem_R:wave.quad:trustGovt.c ***
## pDem_R:wave.cub:trustGovt.c ***
## pDem_I:wave.lin:trustGovt.c
## pDem_I:wave.quad:trustGovt.c
## pDem_I:wave.cub:trustGovt.c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.d.w1 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave1_3 + wave1_2 + wave1_4) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.d.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave1_3 + wave1_2 + wave1_4) *
## trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.68924 0.03280 6607.67744 112.462 < 2e-16
## pDem_R -0.85482 0.04679 6957.86967 -18.271 < 2e-16
## pDem_I -0.81118 0.06166 7864.41589 -13.155 < 2e-16
## wave1_3 -0.13975 0.03595 6314.29417 -3.887 0.000103
## wave1_2 -0.27081 0.03600 5687.51236 -7.522 6.24e-14
## wave1_4 -0.18675 0.04626 6393.85517 -4.037 5.48e-05
## trustGovt.c 0.23796 0.02460 7798.24032 9.672 < 2e-16
## pDem_R:wave1_3 0.97103 0.05191 6076.29180 18.704 < 2e-16
## pDem_R:wave1_2 1.18182 0.05107 5680.51567 23.142 < 2e-16
## pDem_R:wave1_4 0.99728 0.06349 6241.75836 15.708 < 2e-16
## pDem_I:wave1_3 0.59841 0.08413 6127.85043 7.113 1.27e-12
## pDem_I:wave1_2 0.54875 0.07526 5868.51842 7.292 3.47e-13
## pDem_I:wave1_4 0.51546 0.10385 6148.43355 4.964 7.11e-07
## pDem_R:trustGovt.c 0.29603 0.03812 7801.13638 7.766 9.16e-15
## pDem_I:trustGovt.c 0.16376 0.05543 7724.25557 2.954 0.003144
## wave1_3:trustGovt.c -0.01519 0.03353 6252.54772 -0.453 0.650569
## wave1_2:trustGovt.c -0.02016 0.02981 5682.38415 -0.676 0.498843
## wave1_4:trustGovt.c 0.05653 0.03912 6355.65202 1.445 0.148530
## pDem_R:wave1_3:trustGovt.c -0.33210 0.05010 6146.85524 -6.629 3.68e-11
## pDem_R:wave1_2:trustGovt.c -0.38250 0.04535 5670.27890 -8.434 < 2e-16
## pDem_R:wave1_4:trustGovt.c -0.43329 0.05782 6346.68901 -7.494 7.60e-14
## pDem_I:wave1_3:trustGovt.c -0.07494 0.08437 6334.08808 -0.888 0.374489
## pDem_I:wave1_2:trustGovt.c -0.24490 0.07125 5815.70952 -3.437 0.000592
## pDem_I:wave1_4:trustGovt.c -0.16028 0.09467 6402.97242 -1.693 0.090488
##
## (Intercept) ***
## pDem_R ***
## pDem_I ***
## wave1_3 ***
## wave1_2 ***
## wave1_4 ***
## trustGovt.c ***
## pDem_R:wave1_3 ***
## pDem_R:wave1_2 ***
## pDem_R:wave1_4 ***
## pDem_I:wave1_3 ***
## pDem_I:wave1_2 ***
## pDem_I:wave1_4 ***
## pDem_R:trustGovt.c ***
## pDem_I:trustGovt.c **
## wave1_3:trustGovt.c
## wave1_2:trustGovt.c
## wave1_4:trustGovt.c
## pDem_R:wave1_3:trustGovt.c ***
## pDem_R:wave1_2:trustGovt.c ***
## pDem_R:wave1_4:trustGovt.c ***
## pDem_I:wave1_3:trustGovt.c
## pDem_I:wave1_2:trustGovt.c ***
## pDem_I:wave1_4:trustGovt.c .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.d.w4 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave4_3 + wave4_2 + wave4_1) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.d.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave4_3 + wave4_2 + wave4_1) *
## trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.50249 0.04291 7974.24842 81.628 < 2e-16
## pDem_R 0.14246 0.05906 7975.99051 2.412 0.01588
## pDem_I -0.29572 0.09739 7267.24940 -3.036 0.00240
## wave4_3 0.04700 0.04359 5688.09766 1.078 0.28097
## wave4_2 -0.08406 0.04693 6200.37945 -1.791 0.07331
## wave4_1 0.18675 0.04626 6393.85517 4.037 5.48e-05
## trustGovt.c 0.29449 0.03274 6284.94196 8.995 < 2e-16
## pDem_R:wave4_3 -0.02625 0.06137 5678.01785 -0.428 0.66880
## pDem_R:wave4_2 0.18454 0.06387 6048.40919 2.889 0.00388
## pDem_R:wave4_1 -0.99728 0.06349 6241.75836 -15.708 < 2e-16
## pDem_I:wave4_3 0.08295 0.10886 5739.68550 0.762 0.44611
## pDem_I:wave4_2 0.03329 0.10614 5943.99575 0.314 0.75384
## pDem_I:wave4_1 -0.51546 0.10385 6148.43354 -4.964 7.11e-07
## pDem_R:trustGovt.c -0.13726 0.04748 6324.44477 -2.891 0.00385
## pDem_I:trustGovt.c 0.00348 0.08061 6282.97484 0.043 0.96556
## wave4_3:trustGovt.c -0.07172 0.03982 5838.83317 -1.801 0.07172
## wave4_2:trustGovt.c -0.07669 0.03987 6161.48313 -1.923 0.05447
## wave4_1:trustGovt.c -0.05653 0.03913 6355.65203 -1.445 0.14853
## pDem_R:wave4_3:trustGovt.c 0.10119 0.05820 5859.39291 1.739 0.08215
## pDem_R:wave4_2:trustGovt.c 0.05079 0.05822 6150.51872 0.872 0.38304
## pDem_R:wave4_1:trustGovt.c 0.43329 0.05782 6346.68901 7.494 7.60e-14
## pDem_I:wave4_3:trustGovt.c 0.08534 0.10064 5834.02489 0.848 0.39649
## pDem_I:wave4_2:trustGovt.c -0.08462 0.09791 6222.48987 -0.864 0.38748
## pDem_I:wave4_1:trustGovt.c 0.16028 0.09467 6402.97242 1.693 0.09049
##
## (Intercept) ***
## pDem_R *
## pDem_I **
## wave4_3
## wave4_2 .
## wave4_1 ***
## trustGovt.c ***
## pDem_R:wave4_3
## pDem_R:wave4_2 **
## pDem_R:wave4_1 ***
## pDem_I:wave4_3
## pDem_I:wave4_2
## pDem_I:wave4_1 ***
## pDem_R:trustGovt.c **
## pDem_I:trustGovt.c
## wave4_3:trustGovt.c .
## wave4_2:trustGovt.c .
## wave4_1:trustGovt.c
## pDem_R:wave4_3:trustGovt.c .
## pDem_R:wave4_2:trustGovt.c
## pDem_R:wave4_1:trustGovt.c ***
## pDem_I:wave4_3:trustGovt.c
## pDem_I:wave4_2:trustGovt.c
## pDem_I:wave4_1:trustGovt.c .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.473e+00 2.741e-02 3.353e+03 126.694 < 2e-16
## pRep_D 6.729e-02 3.650e-02 4.100e+03 1.844 0.06532
## pRep_I -3.282e-01 5.210e-02 6.547e+03 -6.300 3.17e-10
## wave.lin 6.165e-01 3.807e-02 6.060e+03 16.194 < 2e-16
## wave.quad 9.318e-01 5.616e-02 5.640e+03 16.590 < 2e-16
## wave.cub -3.880e-01 3.395e-02 5.528e+03 -11.429 < 2e-16
## trustGovt.c 2.523e-01 1.951e-02 7.883e+03 12.934 < 2e-16
## pRep_D:wave.lin -7.135e-01 5.610e-02 6.418e+03 -12.718 < 2e-16
## pRep_D:wave.quad -1.156e+00 7.982e-02 5.674e+03 -14.477 < 2e-16
## pRep_D:wave.cub 5.675e-01 4.769e-02 5.573e+03 11.901 < 2e-16
## pRep_I:wave.lin -2.813e-01 9.037e-02 6.158e+03 -3.112 0.00186
## pRep_I:wave.quad -5.239e-01 1.323e-01 5.782e+03 -3.959 7.62e-05
## pRep_I:wave.cub 4.011e-01 7.967e-02 5.689e+03 5.034 4.94e-07
## pRep_D:trustGovt.c -9.052e-03 2.549e-02 7.973e+03 -0.355 0.72255
## pRep_I:trustGovt.c 3.468e-02 4.092e-02 7.911e+03 0.848 0.39671
## wave.lin:trustGovt.c -2.793e-01 3.805e-02 6.469e+03 -7.339 2.42e-13
## wave.quad:trustGovt.c -3.732e-01 5.434e-02 5.758e+03 -6.869 7.17e-12
## wave.cub:trustGovt.c 1.950e-01 3.303e-02 5.652e+03 5.903 3.77e-09
## pRep_D:wave.lin:trustGovt.c 3.265e-01 5.190e-02 6.500e+03 6.291 3.36e-10
## pRep_D:wave.quad:trustGovt.c 2.813e-01 7.383e-02 5.779e+03 3.811 0.00014
## pRep_D:wave.cub:trustGovt.c -2.136e-01 4.496e-02 5.695e+03 -4.752 2.06e-06
## pRep_I:wave.lin:trustGovt.c 2.662e-01 8.747e-02 6.601e+03 3.044 0.00235
## pRep_I:wave.quad:trustGovt.c 1.218e-01 1.254e-01 5.832e+03 0.971 0.33179
## pRep_I:wave.cub:trustGovt.c -1.355e-02 7.772e-02 5.779e+03 -0.174 0.86157
##
## (Intercept) ***
## pRep_D .
## pRep_I ***
## wave.lin ***
## wave.quad ***
## wave.cub ***
## trustGovt.c ***
## pRep_D:wave.lin ***
## pRep_D:wave.quad ***
## pRep_D:wave.cub ***
## pRep_I:wave.lin **
## pRep_I:wave.quad ***
## pRep_I:wave.cub ***
## pRep_D:trustGovt.c
## pRep_I:trustGovt.c
## wave.lin:trustGovt.c ***
## wave.quad:trustGovt.c ***
## wave.cub:trustGovt.c ***
## pRep_D:wave.lin:trustGovt.c ***
## pRep_D:wave.quad:trustGovt.c ***
## pRep_D:wave.cub:trustGovt.c ***
## pRep_I:wave.lin:trustGovt.c **
## pRep_I:wave.quad:trustGovt.c
## pRep_I:wave.cub:trustGovt.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.r.w1 <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave1_3 + wave1_2 + wave1_4) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.r.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave1_3 + wave1_2 + wave1_4) *
## trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.83442 0.03436 6192.43343 82.501 < 2e-16
## pRep_D 0.85482 0.04679 6957.86967 18.271 < 2e-16
## pRep_I 0.04364 0.06238 7826.21590 0.700 0.48424
## wave1_3 0.83128 0.03730 5822.20576 22.287 < 2e-16
## wave1_2 0.91101 0.03603 5641.41879 25.283 < 2e-16
## wave1_4 0.81053 0.04321 6003.48658 18.757 < 2e-16
## trustGovt.c 0.53398 0.02934 7871.42581 18.200 < 2e-16
## pRep_D:wave1_3 -0.97103 0.05191 6076.29180 -18.704 < 2e-16
## pRep_D:wave1_2 -1.18182 0.05107 5680.51567 -23.142 < 2e-16
## pRep_D:wave1_4 -0.99728 0.06349 6241.75836 -15.708 < 2e-16
## pRep_I:wave1_3 -0.37262 0.08481 6050.95477 -4.394 1.13e-05
## pRep_I:wave1_2 -0.63307 0.07524 5853.02344 -8.414 < 2e-16
## pRep_I:wave1_4 -0.48182 0.10260 6086.19645 -4.696 2.71e-06
## pRep_D:trustGovt.c -0.29603 0.03812 7801.13638 -7.766 9.16e-15
## pRep_I:trustGovt.c -0.13226 0.05756 7727.59844 -2.298 0.02159
## wave1_3:trustGovt.c -0.34729 0.03716 6055.01150 -9.346 < 2e-16
## wave1_2:trustGovt.c -0.40267 0.03400 5627.56785 -11.843 < 2e-16
## wave1_4:trustGovt.c -0.37676 0.04248 6339.71487 -8.868 < 2e-16
## pRep_D:wave1_3:trustGovt.c 0.33210 0.05010 6146.85524 6.629 3.68e-11
## pRep_D:wave1_2:trustGovt.c 0.38250 0.04535 5670.27890 8.434 < 2e-16
## pRep_D:wave1_4:trustGovt.c 0.43329 0.05782 6346.68901 7.494 7.60e-14
## pRep_I:wave1_3:trustGovt.c 0.25717 0.08589 6289.95938 2.994 0.00276
## pRep_I:wave1_2:trustGovt.c 0.13760 0.07317 5802.13742 1.880 0.06009
## pRep_I:wave1_4:trustGovt.c 0.27301 0.09607 6390.09592 2.842 0.00450
##
## (Intercept) ***
## pRep_D ***
## pRep_I
## wave1_3 ***
## wave1_2 ***
## wave1_4 ***
## trustGovt.c ***
## pRep_D:wave1_3 ***
## pRep_D:wave1_2 ***
## pRep_D:wave1_4 ***
## pRep_I:wave1_3 ***
## pRep_I:wave1_2 ***
## pRep_I:wave1_4 ***
## pRep_D:trustGovt.c ***
## pRep_I:trustGovt.c *
## wave1_3:trustGovt.c ***
## wave1_2:trustGovt.c ***
## wave1_4:trustGovt.c ***
## pRep_D:wave1_3:trustGovt.c ***
## pRep_D:wave1_2:trustGovt.c ***
## pRep_D:wave1_4:trustGovt.c ***
## pRep_I:wave1_3:trustGovt.c **
## pRep_I:wave1_2:trustGovt.c .
## pRep_I:wave1_4:trustGovt.c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.r.w4 <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave4_3 + wave4_2 + wave4_1) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.r.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave4_3 + wave4_2 + wave4_1) *
## trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21449.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.64495 0.04149 7772.17209 87.845 < 2e-16
## pRep_D -0.14246 0.05906 7975.99051 -2.412 0.01588
## pRep_I -0.43818 0.09662 7324.11554 -4.535 5.85e-06
## wave4_3 0.02075 0.04305 5642.29192 0.482 0.62990
## wave4_2 0.10048 0.04317 5821.04146 2.328 0.01997
## wave4_1 -0.81053 0.04321 6003.48657 -18.757 < 2e-16
## trustGovt.c 0.15723 0.03447 6415.73145 4.561 5.18e-06
## pRep_D:wave4_3 0.02625 0.06136 5678.01785 0.428 0.66880
## pRep_D:wave4_2 -0.18453 0.06387 6048.40919 -2.889 0.00388
## pRep_D:wave4_1 0.99728 0.06349 6241.75836 15.708 < 2e-16
## pRep_I:wave4_3 0.10920 0.10879 5741.96950 1.004 0.31552
## pRep_I:wave4_2 -0.15125 0.10471 5894.78962 -1.444 0.14868
## pRep_I:wave4_1 0.48182 0.10260 6086.19645 4.696 2.71e-06
## pRep_D:trustGovt.c 0.13726 0.04748 6324.44478 2.891 0.00385
## pRep_I:trustGovt.c 0.14074 0.08133 6306.64647 1.730 0.08360
## wave4_3:trustGovt.c 0.02947 0.04233 5861.22465 0.696 0.48637
## wave4_2:trustGovt.c -0.02591 0.04240 6149.78189 -0.611 0.54117
## wave4_1:trustGovt.c 0.37676 0.04248 6339.71488 8.868 < 2e-16
## pRep_D:wave4_3:trustGovt.c -0.10119 0.05820 5859.39291 -1.739 0.08215
## pRep_D:wave4_2:trustGovt.c -0.05079 0.05822 6150.51872 -0.872 0.38304
## pRep_D:wave4_1:trustGovt.c -0.43329 0.05782 6346.68901 -7.494 7.60e-14
## pRep_I:wave4_3:trustGovt.c -0.01584 0.10158 5833.97983 -0.156 0.87606
## pRep_I:wave4_2:trustGovt.c -0.13541 0.09901 6217.24138 -1.368 0.17147
## pRep_I:wave4_1:trustGovt.c -0.27301 0.09607 6390.09593 -2.842 0.00450
##
## (Intercept) ***
## pRep_D *
## pRep_I ***
## wave4_3
## wave4_2 *
## wave4_1 ***
## trustGovt.c ***
## pRep_D:wave4_3
## pRep_D:wave4_2 **
## pRep_D:wave4_1 ***
## pRep_I:wave4_3
## pRep_I:wave4_2
## pRep_I:wave4_1 ***
## pRep_D:trustGovt.c **
## pRep_I:trustGovt.c .
## wave4_3:trustGovt.c
## wave4_2:trustGovt.c
## wave4_1:trustGovt.c ***
## pRep_D:wave4_3:trustGovt.c .
## pRep_D:wave4_2:trustGovt.c
## pRep_D:wave4_1:trustGovt.c ***
## pRep_I:wave4_3:trustGovt.c
## pRep_I:wave4_2:trustGovt.c
## pRep_I:wave4_1:trustGovt.c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.i <- lmer(voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.i)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.14438 0.04687 6323.70366 67.084 < 2e-16
## pInd_R 0.32824 0.05210 6546.66551 6.300 3.17e-10
## pInd_D 0.39552 0.05139 6704.02659 7.696 1.61e-14
## wave.lin 0.33526 0.08175 6160.03440 4.101 4.16e-05
## wave.quad 0.40789 0.11922 5782.89832 3.421 0.000627
## wave.cub 0.01309 0.07181 5704.93326 0.182 0.855368
## trustGovt.c 0.28699 0.03648 7890.71741 7.867 4.12e-15
## pInd_R:wave.lin 0.28127 0.09037 6157.96542 3.112 0.001865
## pInd_R:wave.quad 0.52387 0.13233 5782.06954 3.959 7.62e-05
## pInd_R:wave.cub -0.40109 0.07967 5689.05672 -5.034 4.94e-07
## pInd_D:wave.lin -0.43224 0.09154 6253.98905 -4.722 2.39e-06
## pInd_D:wave.quad -0.63170 0.13227 5779.10131 -4.776 1.84e-06
## pInd_D:wave.cub 0.16646 0.07943 5701.66655 2.096 0.036164
## pInd_R:trustGovt.c -0.03468 0.04092 7911.21341 -0.848 0.396709
## pInd_D:trustGovt.c -0.04373 0.03973 7860.01858 -1.101 0.271109
## wave.lin:trustGovt.c -0.01302 0.07873 6646.60651 -0.165 0.868608
## wave.quad:trustGovt.c -0.25144 0.11276 5831.07319 -2.230 0.025789
## wave.cub:trustGovt.c 0.18145 0.07026 5798.13629 2.583 0.009828
## pInd_R:wave.lin:trustGovt.c -0.26623 0.08747 6601.30677 -3.044 0.002345
## pInd_R:wave.quad:trustGovt.c -0.12176 0.12545 5831.89550 -0.971 0.331790
## pInd_R:wave.cub:trustGovt.c 0.01355 0.07772 5778.82809 0.174 0.861568
## pInd_D:wave.lin:trustGovt.c 0.06024 0.08631 6628.36409 0.698 0.485233
## pInd_D:wave.quad:trustGovt.c 0.15956 0.12344 5831.18426 1.293 0.196205
## pInd_D:wave.cub:trustGovt.c -0.20008 0.07675 5802.89021 -2.607 0.009156
##
## (Intercept) ***
## pInd_R ***
## pInd_D ***
## wave.lin ***
## wave.quad ***
## wave.cub
## trustGovt.c ***
## pInd_R:wave.lin **
## pInd_R:wave.quad ***
## pInd_R:wave.cub ***
## pInd_D:wave.lin ***
## pInd_D:wave.quad ***
## pInd_D:wave.cub *
## pInd_R:trustGovt.c
## pInd_D:trustGovt.c
## wave.lin:trustGovt.c
## wave.quad:trustGovt.c *
## wave.cub:trustGovt.c **
## pInd_R:wave.lin:trustGovt.c **
## pInd_R:wave.quad:trustGovt.c
## pInd_R:wave.cub:trustGovt.c
## pInd_D:wave.lin:trustGovt.c
## pInd_D:wave.quad:trustGovt.c
## pInd_D:wave.cub:trustGovt.c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m2.i <- lmer(voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(PEL.m2.i)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pInd_R + pInd_D) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.14438 0.04687 6323.70366 67.084 < 2e-16
## pInd_R 0.32824 0.05210 6546.66551 6.300 3.17e-10
## pInd_D 0.39552 0.05139 6704.02659 7.696 1.61e-14
## wave.lin 0.33526 0.08175 6160.03440 4.101 4.16e-05
## wave.quad 0.40789 0.11922 5782.89832 3.421 0.000627
## wave.cub 0.01309 0.07181 5704.93326 0.182 0.855368
## trustGovt.c 0.28699 0.03648 7890.71741 7.867 4.12e-15
## pInd_R:wave.lin 0.28127 0.09037 6157.96542 3.112 0.001865
## pInd_R:wave.quad 0.52387 0.13233 5782.06954 3.959 7.62e-05
## pInd_R:wave.cub -0.40109 0.07967 5689.05672 -5.034 4.94e-07
## pInd_D:wave.lin -0.43224 0.09154 6253.98905 -4.722 2.39e-06
## pInd_D:wave.quad -0.63170 0.13227 5779.10131 -4.776 1.84e-06
## pInd_D:wave.cub 0.16646 0.07943 5701.66655 2.096 0.036164
## pInd_R:trustGovt.c -0.03468 0.04092 7911.21341 -0.848 0.396709
## pInd_D:trustGovt.c -0.04373 0.03973 7860.01858 -1.101 0.271109
## wave.lin:trustGovt.c -0.01302 0.07873 6646.60651 -0.165 0.868608
## wave.quad:trustGovt.c -0.25144 0.11276 5831.07319 -2.230 0.025789
## wave.cub:trustGovt.c 0.18145 0.07026 5798.13629 2.583 0.009828
## pInd_R:wave.lin:trustGovt.c -0.26623 0.08747 6601.30677 -3.044 0.002345
## pInd_R:wave.quad:trustGovt.c -0.12176 0.12545 5831.89550 -0.971 0.331790
## pInd_R:wave.cub:trustGovt.c 0.01355 0.07772 5778.82809 0.174 0.861568
## pInd_D:wave.lin:trustGovt.c 0.06024 0.08631 6628.36409 0.698 0.485233
## pInd_D:wave.quad:trustGovt.c 0.15956 0.12344 5831.18426 1.293 0.196205
## pInd_D:wave.cub:trustGovt.c -0.20008 0.07675 5802.89021 -2.607 0.009156
##
## (Intercept) ***
## pInd_R ***
## pInd_D ***
## wave.lin ***
## wave.quad ***
## wave.cub
## trustGovt.c ***
## pInd_R:wave.lin **
## pInd_R:wave.quad ***
## pInd_R:wave.cub ***
## pInd_D:wave.lin ***
## pInd_D:wave.quad ***
## pInd_D:wave.cub *
## pInd_R:trustGovt.c
## pInd_D:trustGovt.c
## wave.lin:trustGovt.c
## wave.quad:trustGovt.c *
## wave.cub:trustGovt.c **
## pInd_R:wave.lin:trustGovt.c **
## pInd_R:wave.quad:trustGovt.c
## pInd_R:wave.cub:trustGovt.c
## pInd_D:wave.lin:trustGovt.c
## pInd_D:wave.quad:trustGovt.c
## pInd_D:wave.cub:trustGovt.c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1d$trustGovt.hi <- d$trustGovt - (mean(d$trustGovt, na.rm = T) + sd(d$trustGovt, na.rm = T))
d$trustGovt.lo <- d$trustGovt - (mean(d$trustGovt, na.rm = T) - sd(d$trustGovt, na.rm = T))
PEL.m2.hiGT <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * trustGovt.hi + (1 | pid),
data = d)
summary(PEL.m2.hiGT)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) * trustGovt.hi + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21443.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6615 -0.5076 0.0520 0.5374 4.0936
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6126 0.7827
## Residual 0.5228 0.7230
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.674e+00 2.964e-02 6.313e+03 123.932
## pDem_Rep -5.729e-02 4.681e-02 6.114e+03 -1.224
## pParty_Ind -3.186e-01 7.309e-02 7.929e+03 -4.359
## wave.lin 1.947e-01 5.563e-02 6.363e+03 3.500
## wave.quad 1.081e-01 7.942e-02 5.801e+03 1.361
## wave.cub 6.664e-02 4.775e-02 5.725e+03 1.396
## trustGovt.hi 2.608e-01 1.519e-02 7.976e+03 17.177
## pDem_Rep:wave.lin 3.529e-01 8.400e-02 6.343e+03 4.200
## pDem_Rep:wave.quad 8.448e-01 1.194e-01 5.756e+03 7.076
## pDem_Rep:wave.cub -3.315e-01 7.091e-02 5.648e+03 -4.675
## pParty_Ind:wave.lin 1.893e-01 1.506e-01 6.375e+03 1.257
## pParty_Ind:wave.quad 3.304e-02 2.162e-01 5.857e+03 0.153
## pParty_Ind:wave.cub 2.203e-01 1.303e-01 5.782e+03 1.691
## pDem_Rep:trustGovt.hi 9.052e-03 2.549e-02 7.973e+03 0.355
## pParty_Ind:trustGovt.hi 3.921e-02 3.826e-02 7.864e+03 1.025
## wave.lin:trustGovt.hi -8.169e-02 3.141e-02 6.620e+03 -2.601
## wave.quad:trustGovt.hi -2.388e-01 4.473e-02 5.789e+03 -5.339
## wave.cub:trustGovt.hi 1.193e-01 2.769e-02 5.741e+03 4.308
## pDem_Rep:wave.lin:trustGovt.hi -3.265e-01 5.190e-02 6.500e+03 -6.291
## pDem_Rep:wave.quad:trustGovt.hi -2.813e-01 7.383e-02 5.779e+03 -3.811
## pDem_Rep:wave.cub:trustGovt.hi 2.136e-01 4.496e-02 5.695e+03 4.752
## pParty_Ind:wave.lin:trustGovt.hi 1.030e-01 8.292e-02 6.626e+03 1.242
## pParty_Ind:wave.quad:trustGovt.hi -1.890e-02 1.188e-01 5.837e+03 -0.159
## pParty_Ind:wave.cub:trustGovt.hi 9.326e-02 7.389e-02 5.800e+03 1.262
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.221029
## pParty_Ind 1.32e-05 ***
## wave.lin 0.000468 ***
## wave.quad 0.173525
## wave.cub 0.162880
## trustGovt.hi < 2e-16 ***
## pDem_Rep:wave.lin 2.70e-05 ***
## pDem_Rep:wave.quad 1.66e-12 ***
## pDem_Rep:wave.cub 3.00e-06 ***
## pParty_Ind:wave.lin 0.208908
## pParty_Ind:wave.quad 0.878569
## pParty_Ind:wave.cub 0.090809 .
## pDem_Rep:trustGovt.hi 0.722546
## pParty_Ind:trustGovt.hi 0.305568
## wave.lin:trustGovt.hi 0.009328 **
## wave.quad:trustGovt.hi 9.68e-08 ***
## wave.cub:trustGovt.hi 1.67e-05 ***
## pDem_Rep:wave.lin:trustGovt.hi 3.36e-10 ***
## pDem_Rep:wave.quad:trustGovt.hi 0.000140 ***
## pDem_Rep:wave.cub:trustGovt.hi 2.06e-06 ***
## pParty_Ind:wave.lin:trustGovt.hi 0.214265
## pParty_Ind:wave.quad:trustGovt.hi 0.873664
## pParty_Ind:wave.cub:trustGovt.hi 0.206943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13. Moderator: Emotions
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)) +
geom_jitter(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("#A60021FF","#FECE40"),
labels = c("Negative","Positive")) +
scale_x_discrete(labels = c("Wave 1", "Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
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
Positive Emotions
Positive emotions (hope and pride; r = .75) are positively related to PEL (b = 0.22, p < .001), though more strongly for Republicans than for Democrats (b = 0.06, p < .001). The positive emotions-PEL 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 = 0.15, p = .016), such that the emotions-PEL slope is more similar for Republicans and Democrats in waves 1 and 4 than for waves 2 and 3.
Negative Emotions
Negative emotions (anger, fear, and disgust; \(\alpha = .90\)) are negatively related to PEL (b = -0.14, p < .001), though more strongly for Democrats than for Republicans (b = -0.08, p < .001). The negative emotions-PEL relationship does not change linearly over time, though it does change as a function of cubic time (b = -0.08, p < .001).
There is a 3-way interaction of negative emotions, party ID (Dem vs. Rep), and linear time (b = 0.24, p < .001), such that the emotions-PEL slope becomes stronger/more negative over time for Democrats (b = -0.11, p < .001), and attenuates/becomes less negative over time for Republicans (b = 0.13, p = .001).
Negative emotions-PEL slope for: Democrats in wave 1 (b = -0.05, p = .018) and wave 4 (b = -0.19, p < .001); Republicans in wave 1 (b = -0.20, p < .001) and wave 4 (b = -0.12, p = .004).
Models
# Positive Emotions
PEL.m3 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * Pos_Emo + (1 | pid),
data = d)
summary(PEL.m3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) * Pos_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21783
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9365 -0.5022 0.0459 0.5559 3.9174
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7006 0.8370
## Residual 0.5056 0.7111
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.762e+00 3.789e-02 7.504e+03 72.902 < 2e-16
## pDem_Rep -4.950e-01 6.971e-02 7.978e+03 -7.101 1.35e-12
## pParty_Ind -3.163e-01 8.775e-02 8.056e+03 -3.604 0.000315
## wave.lin 1.918e-01 6.918e-02 6.136e+03 2.773 0.005567
## wave.quad 2.212e-01 1.033e-01 5.937e+03 2.142 0.032243
## wave.cub -1.375e-02 6.288e-02 5.883e+03 -0.219 0.826892
## Pos_Emo 2.193e-01 1.280e-02 7.820e+03 17.136 < 2e-16
## pDem_Rep:wave.lin 1.229e+00 1.320e-01 6.136e+03 9.305 < 2e-16
## pDem_Rep:wave.quad 1.949e-01 1.987e-01 6.001e+03 0.981 0.326545
## pDem_Rep:wave.cub -4.395e-01 1.204e-01 5.885e+03 -3.652 0.000263
## pParty_Ind:wave.lin -5.841e-01 1.748e-01 6.196e+03 -3.342 0.000838
## pParty_Ind:wave.quad -1.453e-01 2.604e-01 5.968e+03 -0.558 0.576865
## pParty_Ind:wave.cub -4.502e-02 1.588e-01 5.938e+03 -0.283 0.776867
## pDem_Rep:Pos_Emo 6.216e-02 2.079e-02 7.719e+03 2.990 0.002801
## pParty_Ind:Pos_Emo 6.897e-03 3.278e-02 7.634e+03 0.210 0.833370
## wave.lin:Pos_Emo 2.377e-02 2.663e-02 6.160e+03 0.892 0.372205
## wave.quad:Pos_Emo 7.053e-02 3.932e-02 5.977e+03 1.794 0.072901
## wave.cub:Pos_Emo -2.942e-02 2.368e-02 5.941e+03 -1.242 0.214145
## pDem_Rep:wave.lin:Pos_Emo -7.081e-02 4.342e-02 6.180e+03 -1.631 0.102993
## pDem_Rep:wave.quad:Pos_Emo 1.550e-01 6.443e-02 6.019e+03 2.406 0.016170
## pDem_Rep:wave.cub:Pos_Emo 1.706e-02 3.862e-02 5.907e+03 0.442 0.658666
## pParty_Ind:wave.lin:Pos_Emo 2.566e-01 7.086e-02 6.191e+03 3.621 0.000295
## pParty_Ind:wave.quad:Pos_Emo 1.096e-01 1.047e-01 6.006e+03 1.046 0.295594
## pParty_Ind:wave.cub:Pos_Emo 3.985e-02 6.302e-02 5.983e+03 0.632 0.527236
##
## (Intercept) ***
## pDem_Rep ***
## pParty_Ind ***
## wave.lin **
## wave.quad *
## wave.cub
## Pos_Emo ***
## pDem_Rep:wave.lin ***
## pDem_Rep:wave.quad
## pDem_Rep:wave.cub ***
## pParty_Ind:wave.lin ***
## pParty_Ind:wave.quad
## pParty_Ind:wave.cub
## pDem_Rep:Pos_Emo **
## pParty_Ind:Pos_Emo
## wave.lin:Pos_Emo
## wave.quad:Pos_Emo .
## wave.cub:Pos_Emo
## pDem_Rep:wave.lin:Pos_Emo
## pDem_Rep:wave.quad:Pos_Emo *
## pDem_Rep:wave.cub:Pos_Emo
## pParty_Ind:wave.lin:Pos_Emo ***
## pParty_Ind:wave.quad:Pos_Emo
## pParty_Ind:wave.cub:Pos_Emo
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Negative Emotions
PEL.m4 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) * Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22052.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.65905 0.03641 7267.06976 100.486 < 2e-16
## pDem_Rep -0.12435 0.06525 7697.94750 -1.906 0.056733
## pParty_Ind -0.58006 0.08526 8071.17448 -6.804 1.09e-11
## wave.lin 0.28783 0.06647 6138.22914 4.330 1.51e-05
## wave.quad 0.49952 0.09731 5891.90339 5.133 2.94e-07
## wave.cub -0.23930 0.05859 5835.83647 -4.085 4.47e-05
## Neg_Emo -0.13546 0.01277 7825.73546 -10.604 < 2e-16
## pDem_Rep:wave.lin 0.46433 0.12097 6093.72699 3.838 0.000125
## pDem_Rep:wave.quad 1.20228 0.17855 5905.67486 6.734 1.81e-11
## pDem_Rep:wave.cub -0.52388 0.10735 5826.62679 -4.880 1.09e-06
## pParty_Ind:wave.lin 0.18853 0.17078 6193.33192 1.104 0.269647
## pParty_Ind:wave.quad 0.07593 0.24992 5937.51038 0.304 0.761267
## pParty_Ind:wave.cub 0.16292 0.15086 5899.55902 1.080 0.280223
## pDem_Rep:Neg_Emo -0.08219 0.02388 7618.88927 -3.442 0.000579
## pParty_Ind:Neg_Emo 0.07472 0.03085 7625.47199 2.422 0.015464
## wave.lin:Neg_Emo -0.02284 0.02602 6232.62464 -0.878 0.380144
## wave.quad:Neg_Emo -0.06505 0.03893 5964.12781 -1.671 0.094808
## wave.cub:Neg_Emo 0.06421 0.02396 5880.17972 2.680 0.007383
## pDem_Rep:wave.lin:Neg_Emo 0.24737 0.05015 6179.76111 4.933 8.32e-07
## pDem_Rep:wave.quad:Neg_Emo -0.16478 0.07565 6006.42943 -2.178 0.029426
## pDem_Rep:wave.cub:Neg_Emo 0.05242 0.04628 5888.20237 1.133 0.257327
## pParty_Ind:wave.lin:Neg_Emo -0.10276 0.06530 6304.78833 -1.574 0.115631
## pParty_Ind:wave.quad:Neg_Emo 0.01487 0.09761 5998.72272 0.152 0.878891
## pParty_Ind:wave.cub:Neg_Emo -0.04650 0.06021 5926.01730 -0.772 0.440028
##
## (Intercept) ***
## pDem_Rep .
## pParty_Ind ***
## wave.lin ***
## wave.quad ***
## wave.cub ***
## Neg_Emo ***
## pDem_Rep:wave.lin ***
## pDem_Rep:wave.quad ***
## pDem_Rep:wave.cub ***
## pParty_Ind:wave.lin
## pParty_Ind:wave.quad
## pParty_Ind:wave.cub
## pDem_Rep:Neg_Emo ***
## pParty_Ind:Neg_Emo *
## wave.lin:Neg_Emo
## wave.quad:Neg_Emo .
## wave.cub:Neg_Emo **
## pDem_Rep:wave.lin:Neg_Emo ***
## pDem_Rep:wave.quad:Neg_Emo *
## pDem_Rep:wave.cub:Neg_Emo
## pParty_Ind:wave.lin:Neg_Emo
## pParty_Ind:wave.quad:Neg_Emo
## pParty_Ind:wave.cub:Neg_Emo
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
# Negative Emotions
PEL.m4.d <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad + wave.cub) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave.lin + wave.quad +
## wave.cub) * Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22052.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.915e+00 4.903e-02 7.658e+03 79.843 < 2e-16 ***
## pDem_R -1.244e-01 6.525e-02 7.698e+03 -1.906 0.056733 .
## pDem_I -6.422e-01 9.245e-02 8.080e+03 -6.947 4.03e-12 ***
## wave.lin -7.181e-03 8.874e-02 6.045e+03 -0.081 0.935501
## wave.quad -1.269e-01 1.318e-01 5.861e+03 -0.963 0.335606
## wave.cub -3.166e-02 8.014e-02 5.806e+03 -0.395 0.692786
## Neg_Emo -1.193e-01 1.384e-02 8.012e+03 -8.617 < 2e-16 ***
## pDem_R:wave.lin 4.643e-01 1.210e-01 6.094e+03 3.838 0.000125 ***
## pDem_R:wave.quad 1.202e+00 1.786e-01 5.906e+03 6.734 1.81e-11 ***
## pDem_R:wave.cub -5.239e-01 1.073e-01 5.827e+03 -4.880 1.09e-06 ***
## pDem_I:wave.lin 4.207e-01 1.827e-01 6.168e+03 2.303 0.021338 *
## pDem_I:wave.quad 6.771e-01 2.682e-01 5.925e+03 2.525 0.011605 *
## pDem_I:wave.cub -9.902e-02 1.623e-01 5.891e+03 -0.610 0.541934
## pDem_R:Neg_Emo -8.219e-02 2.388e-02 7.619e+03 -3.442 0.000579 ***
## pDem_I:Neg_Emo 3.362e-02 3.155e-02 7.698e+03 1.066 0.286580
## wave.lin:Neg_Emo -1.123e-01 2.730e-02 6.153e+03 -4.112 3.97e-05 ***
## wave.quad:Neg_Emo 1.238e-02 4.010e-02 5.949e+03 0.309 0.757524
## wave.cub:Neg_Emo 5.349e-02 2.404e-02 5.900e+03 2.225 0.026111 *
## pDem_R:wave.lin:Neg_Emo 2.474e-01 5.015e-02 6.180e+03 4.933 8.32e-07 ***
## pDem_R:wave.quad:Neg_Emo -1.648e-01 7.565e-02 6.006e+03 -2.178 0.029426 *
## pDem_R:wave.cub:Neg_Emo 5.242e-02 4.627e-02 5.888e+03 1.133 0.257327
## pDem_I:wave.lin:Neg_Emo 2.092e-02 6.621e-02 6.297e+03 0.316 0.752008
## pDem_I:wave.quad:Neg_Emo -6.751e-02 9.859e-02 5.995e+03 -0.685 0.493504
## pDem_I:wave.cub:Neg_Emo -2.029e-02 6.056e-02 5.929e+03 -0.335 0.737675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m4.d.w1 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave1_2 + wave1_3 + wave1_4) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.d.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave1_2 + wave1_3 + wave1_4) *
## Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22058.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.942e+00 5.921e-02 8.071e+03 66.573 < 2e-16 ***
## pDem_R -7.881e-01 8.717e-02 7.980e+03 -9.040 < 2e-16 ***
## pDem_I -1.047e+00 1.237e-01 7.832e+03 -8.463 < 2e-16 ***
## wave1_2 -4.151e-02 7.863e-02 6.124e+03 -0.528 0.597549
## wave1_3 -7.677e-02 8.301e-02 6.058e+03 -0.925 0.355106
## wave1_4 8.651e-03 1.013e-01 6.056e+03 0.085 0.931942
## Neg_Emo -5.286e-02 2.234e-02 7.465e+03 -2.366 0.017996 *
## pDem_R:wave1_2 1.110e+00 1.106e-01 6.184e+03 10.038 < 2e-16 ***
## pDem_R:wave1_3 8.184e-01 1.166e-01 6.114e+03 7.019 2.48e-12 ***
## pDem_R:wave1_4 7.263e-01 1.386e-01 6.111e+03 5.239 1.67e-07 ***
## pDem_I:wave1_2 5.180e-01 1.611e-01 6.277e+03 3.215 0.001312 **
## pDem_I:wave1_3 6.293e-01 1.744e-01 6.234e+03 3.608 0.000311 ***
## pDem_I:wave1_4 4.702e-01 2.098e-01 6.184e+03 2.241 0.025079 *
## pDem_R:Neg_Emo -1.516e-01 3.269e-02 7.225e+03 -4.637 3.60e-06 ***
## pDem_I:Neg_Emo 3.497e-02 4.858e-02 7.408e+03 0.720 0.471680
## wave1_2:Neg_Emo -6.200e-02 2.666e-02 6.287e+03 -2.326 0.020060 *
## wave1_3:Neg_Emo -6.464e-02 2.817e-02 6.159e+03 -2.295 0.021784 *
## wave1_4:Neg_Emo -1.390e-01 3.148e-02 6.201e+03 -4.416 1.02e-05 ***
## pDem_R:wave1_2:Neg_Emo -5.986e-02 4.662e-02 6.322e+03 -1.284 0.199154
## pDem_R:wave1_3:Neg_Emo 1.162e-01 4.972e-02 6.217e+03 2.338 0.019406 *
## pDem_R:wave1_4:Neg_Emo 2.212e-01 5.673e-02 6.203e+03 3.898 9.79e-05 ***
## pDem_I:wave1_2:Neg_Emo -1.331e-02 6.282e-02 6.374e+03 -0.212 0.832196
## pDem_I:wave1_3:Neg_Emo -2.314e-02 6.900e-02 6.324e+03 -0.335 0.737395
## pDem_I:wave1_4:Neg_Emo 3.107e-02 7.531e-02 6.323e+03 0.412 0.679996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m4.d.w4 <- lmer(voteconfidence ~ (pDem_R + pDem_I) * (wave4_3 + wave4_2 + wave4_1) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.d.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_R + pDem_I) * (wave4_3 + wave4_2 + wave4_1) *
## Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22058.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.951e+00 9.452e-02 7.073e+03 41.799 < 2e-16 ***
## pDem_R -6.179e-02 1.239e-01 7.077e+03 -0.499 0.61800
## pDem_I -5.764e-01 1.874e-01 6.668e+03 -3.076 0.00210 **
## wave4_3 -8.542e-02 1.063e-01 5.741e+03 -0.804 0.42169
## wave4_2 -5.016e-02 1.043e-01 5.837e+03 -0.481 0.63048
## wave4_1 -8.651e-03 1.013e-01 6.056e+03 -0.085 0.93194
## Neg_Emo -1.919e-01 2.540e-02 6.536e+03 -7.553 4.83e-14 ***
## pDem_R:wave4_3 9.215e-02 1.406e-01 5.754e+03 0.655 0.51238
## pDem_R:wave4_2 3.839e-01 1.377e-01 5.857e+03 2.787 0.00533 **
## pDem_R:wave4_1 -7.263e-01 1.386e-01 6.111e+03 -5.239 1.67e-07 ***
## pDem_I:wave4_3 1.591e-01 2.165e-01 5.787e+03 0.735 0.46245
## pDem_I:wave4_2 4.777e-02 2.082e-01 5.855e+03 0.229 0.81853
## pDem_I:wave4_1 -4.702e-01 2.098e-01 6.184e+03 -2.241 0.02508 *
## pDem_R:Neg_Emo 6.958e-02 4.898e-02 6.300e+03 1.421 0.15546
## pDem_I:Neg_Emo 6.603e-02 6.175e-02 6.328e+03 1.069 0.28494
## wave4_3:Neg_Emo 7.438e-02 3.021e-02 5.758e+03 2.462 0.01384 *
## wave4_2:Neg_Emo 7.702e-02 2.908e-02 5.857e+03 2.648 0.00811 **
## wave4_1:Neg_Emo 1.390e-01 3.148e-02 6.201e+03 4.416 1.02e-05 ***
## pDem_R:wave4_3:Neg_Emo -1.049e-01 5.948e-02 5.803e+03 -1.764 0.07780 .
## pDem_R:wave4_2:Neg_Emo -2.810e-01 5.797e-02 5.929e+03 -4.848 1.28e-06 ***
## pDem_R:wave4_1:Neg_Emo -2.212e-01 5.673e-02 6.203e+03 -3.898 9.79e-05 ***
## pDem_I:wave4_3:Neg_Emo -5.420e-02 7.659e-02 5.798e+03 -0.708 0.47917
## pDem_I:wave4_2:Neg_Emo -4.438e-02 7.219e-02 5.889e+03 -0.615 0.53876
## pDem_I:wave4_1:Neg_Emo -3.107e-02 7.531e-02 6.323e+03 -0.412 0.68000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m4.r <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad + wave.cub) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave.lin + wave.quad +
## wave.cub) * Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22052.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.79023 0.04424 7071.52188 85.670 < 2e-16 ***
## pRep_D 0.12435 0.06525 7697.94749 1.906 0.056733 .
## pRep_I -0.51788 0.09011 8079.91473 -5.747 9.39e-09 ***
## wave.lin 0.45715 0.08195 6137.86171 5.578 2.54e-08 ***
## wave.quad 1.07535 0.12036 5954.87066 8.934 < 2e-16 ***
## wave.cub -0.55555 0.07137 5850.27620 -7.784 8.24e-15 ***
## Neg_Emo -0.20146 0.01964 7409.98442 -10.256 < 2e-16 ***
## pRep_D:wave.lin -0.46433 0.12097 6093.72699 -3.838 0.000125 ***
## pRep_D:wave.quad -1.20228 0.17855 5905.67485 -6.734 1.81e-11 ***
## pRep_D:wave.cub 0.52388 0.10735 5826.62679 4.880 1.09e-06 ***
## pRep_I:wave.lin -0.04363 0.17962 6197.12815 -0.243 0.808082
## pRep_I:wave.quad -0.52521 0.26257 5943.17516 -2.000 0.045518 *
## pRep_I:wave.cub 0.42486 0.15787 5891.72439 2.691 0.007140 **
## pRep_D:Neg_Emo 0.08219 0.02388 7618.88928 3.442 0.000579 ***
## pRep_I:Neg_Emo 0.11581 0.03454 7559.10432 3.353 0.000805 ***
## wave.lin:Neg_Emo 0.13510 0.04193 6173.39924 3.222 0.001279 **
## wave.quad:Neg_Emo -0.15239 0.06406 6017.64788 -2.379 0.017387 *
## wave.cub:Neg_Emo 0.10592 0.03952 5879.16837 2.680 0.007381 **
## pRep_D:wave.lin:Neg_Emo -0.24737 0.05015 6179.76111 -4.933 8.32e-07 ***
## pRep_D:wave.quad:Neg_Emo 0.16478 0.07565 6006.42943 2.178 0.029426 *
## pRep_D:wave.cub:Neg_Emo -0.05242 0.04628 5888.20237 -1.133 0.257327
## pRep_I:wave.lin:Neg_Emo -0.22645 0.07350 6281.96299 -3.081 0.002073 **
## pRep_I:wave.quad:Neg_Emo 0.09726 0.11044 6003.29446 0.881 0.378514
## pRep_I:wave.cub:Neg_Emo -0.07271 0.06822 5915.06001 -1.066 0.286573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m4.r.w1 <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave1_2 + wave1_3 + wave1_4) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.r.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave1_2 + wave1_3 + wave1_4) *
## Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22058.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.15393 0.06462 8005.94244 48.807 < 2e-16 ***
## pRep_D 0.78806 0.08717 7980.27844 9.040 < 2e-16 ***
## pRep_I -0.25855 0.12612 7761.95301 -2.050 0.040392 *
## wave1_2 1.06862 0.07759 6234.71607 13.773 < 2e-16 ***
## wave1_3 0.74165 0.08167 6159.61109 9.081 < 2e-16 ***
## wave1_4 0.73492 0.09440 6164.15780 7.785 8.13e-15 ***
## Neg_Emo -0.20443 0.02393 7035.35153 -8.544 < 2e-16 ***
## pRep_D:wave1_2 -1.11014 0.11059 6183.81092 -10.038 < 2e-16 ***
## pRep_D:wave1_3 -0.81842 0.11661 6114.05298 -7.019 2.48e-12 ***
## pRep_D:wave1_4 -0.72627 0.13864 6111.33580 -5.239 1.67e-07 ***
## pRep_I:wave1_2 -0.59216 0.16039 6293.48993 -3.692 0.000224 ***
## pRep_I:wave1_3 -0.18911 0.17384 6262.39606 -1.088 0.276711
## pRep_I:wave1_4 -0.25606 0.20661 6211.64686 -1.239 0.215270
## pRep_D:Neg_Emo 0.15158 0.03269 7225.44007 4.637 3.60e-06 ***
## pRep_I:Neg_Emo 0.18654 0.04941 7341.68102 3.775 0.000161 ***
## wave1_2:Neg_Emo -0.12186 0.03811 6313.69646 -3.197 0.001395 **
## wave1_3:Neg_Emo 0.05161 0.04083 6222.88375 1.264 0.206247
## wave1_4:Neg_Emo 0.08214 0.04704 6185.04135 1.746 0.080842 .
## pRep_D:wave1_2:Neg_Emo 0.05986 0.04662 6322.27929 1.284 0.199154
## pRep_D:wave1_3:Neg_Emo -0.11624 0.04971 6217.39202 -2.338 0.019406 *
## pRep_D:wave1_4:Neg_Emo -0.22116 0.05673 6203.45904 -3.898 9.79e-05 ***
## pRep_I:wave1_2:Neg_Emo 0.04655 0.06839 6360.01762 0.681 0.496112
## pRep_I:wave1_3:Neg_Emo -0.13938 0.07514 6324.75585 -1.855 0.063641 .
## pRep_I:wave1_4:Neg_Emo -0.19009 0.08303 6296.21573 -2.290 0.022079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PEL.m4.r.w4 <- lmer(voteconfidence ~ (pRep_D + pRep_I) * (wave4_3 + wave4_2 + wave4_1) * Neg_Emo + (1 | pid),
data = d)
summary(PEL.m4.r.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pRep_D + pRep_I) * (wave4_3 + wave4_2 + wave4_1) *
## Neg_Emo + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22058.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6457 -0.5055 0.0621 0.5514 3.6219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7198 0.8484
## Residual 0.5240 0.7239
## Number of obs: 8104, groups: pid, 2615
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.889e+00 8.073e-02 7.357e+03 48.171 < 2e-16 ***
## pRep_D 6.179e-02 1.239e-01 7.077e+03 0.499 0.617998
## pRep_I -5.146e-01 1.808e-01 6.676e+03 -2.847 0.004428 **
## wave4_3 6.729e-03 9.211e-02 5.772e+03 0.073 0.941764
## wave4_2 3.337e-01 8.986e-02 5.877e+03 3.714 0.000206 ***
## wave4_1 -7.349e-01 9.440e-02 6.164e+03 -7.785 8.13e-15 ***
## Neg_Emo -1.223e-01 4.195e-02 6.248e+03 -2.915 0.003567 **
## pRep_D:wave4_3 -9.215e-02 1.406e-01 5.754e+03 -0.655 0.512383
## pRep_D:wave4_2 -3.839e-01 1.377e-01 5.857e+03 -2.787 0.005334 **
## pRep_D:wave4_1 7.263e-01 1.386e-01 6.111e+03 5.239 1.67e-07 ***
## pRep_I:wave4_3 6.695e-02 2.097e-01 5.790e+03 0.319 0.749515
## pRep_I:wave4_2 -3.361e-01 2.013e-01 5.862e+03 -1.670 0.095020 .
## pRep_I:wave4_1 2.561e-01 2.066e-01 6.212e+03 1.239 0.215270
## pRep_D:Neg_Emo -6.958e-02 4.898e-02 6.300e+03 -1.421 0.155457
## pRep_I:Neg_Emo -3.549e-03 7.023e-02 6.279e+03 -0.051 0.959702
## wave4_3:Neg_Emo -3.054e-02 5.123e-02 5.817e+03 -0.596 0.551133
## wave4_2:Neg_Emo -2.040e-01 5.009e-02 5.948e+03 -4.072 4.71e-05 ***
## wave4_1:Neg_Emo -8.214e-02 4.704e-02 6.185e+03 -1.746 0.080842 .
## pRep_D:wave4_3:Neg_Emo 1.049e-01 5.948e-02 5.803e+03 1.764 0.077796 .
## pRep_D:wave4_2:Neg_Emo 2.810e-01 5.797e-02 5.929e+03 4.848 1.28e-06 ***
## pRep_D:wave4_1:Neg_Emo 2.212e-01 5.673e-02 6.203e+03 3.898 9.79e-05 ***
## pRep_I:wave4_3:Neg_Emo 5.071e-02 8.705e-02 5.808e+03 0.583 0.560213
## pRep_I:wave4_2:Neg_Emo 2.366e-01 8.287e-02 5.911e+03 2.856 0.004309 **
## pRep_I:wave4_1:Neg_Emo 1.901e-01 8.303e-02 6.296e+03 2.290 0.022079 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14. Expanded Model: Emotions & Trust in Gov’t
# fit model with 3-way-interaction
d$party_factor <- as.factor(as.character(d$party_factor))
PEL.m7 <- lmer(voteconfidence ~ trustGovt * Neg_Emo * wave * party_factor + (1|pid), data = d)
# select only mean, +1 SD, -1 SD for negative emotions
sjPlot::plot_model(PEL.m7, type = "eff", terms = c("trustGovt", "Neg_Emo [1.07,2.39,3.71]", "wave")) +
labs(x = "Trust in Federal Government",
y = "Perceived Election Legitimacy",
color = "Negative Emotions",
title = NULL) +
theme_bw()
# select only mean, +1 SD, -1 SD for negative emotions
sjPlot::plot_model(PEL.m7, type = "eff", terms = c("trustGovt", "Neg_Emo [1.07,2.39,3.71]", "party_factor")) +
labs(x = "Trust in Federal Government",
y = "Perceived Election Legitimacy",
color = "Negative Emotions",
title = NULL) +
theme_bw()
wave_party_labels = c("Democrat" = "Democrat",
"Independent" = "Independent",
"Republican" = "Republican",
`1` = "Wave 1",
`2` = "Wave 2",
`3` = "Wave 3",
`4` = "Wave 4")
# Slice z into categories
d <- d %>%
mutate(negemo_group = cut(Neg_Emo, breaks = quantile(Neg_Emo, probs = c(0, 0.33, 0.66, 1), na.rm = TRUE),
include.lowest = TRUE, labels = c("Low", "Medium", "High")))
d$negemo_bins <- NA
d$negemo_bins[d$Neg_Emo <= (mean(d$Neg_Emo, na.rm = T) - sd(d$Neg_Emo, na.rm = T))] <- "Low Negative Emotions"
d$negemo_bins[d$Neg_Emo> (mean(d$Neg_Emo, na.rm = T) - sd(d$Neg_Emo, na.rm = T)) & d$Neg_Emo < (mean(d$Neg_Emo, na.rm = T) + sd(d$Neg_Emo, na.rm = T))] <- "Mean Negative Emotions"
d$negemo_bins[d$Neg_Emo >= (mean(d$Neg_Emo, na.rm = T) + sd(d$Neg_Emo, na.rm = T))] <- "High Negative Emotions"
d$negemo_bins <- factor(d$negemo_bins,
levels = c("Low Negative Emotions",
"Mean Negative Emotions",
"High Negative Emotions"))
d$posemo_bins <- NA
d$posemo_bins[d$Pos_Emo <= (mean(d$Pos_Emo, na.rm = T) - sd(d$Pos_Emo, na.rm = T))] <- "Low Positive Emotions"
d$posemo_bins[d$Pos_Emo> (mean(d$Pos_Emo, na.rm = T) - sd(d$Pos_Emo, na.rm = T)) & d$Pos_Emo < (mean(d$Pos_Emo, na.rm = T) + sd(d$Pos_Emo, na.rm = T))] <- "Mean Positive Emotions"
d$posemo_bins[d$Pos_Emo >= (mean(d$Pos_Emo, na.rm = T) + sd(d$Pos_Emo, na.rm = T))] <- "High Positive Emotions"
d$posemo_bins <- factor(d$posemo_bins,
levels = c("Low Positive Emotions",
"Mean Positive Emotions",
"High Positive Emotions"))
#paletteer::paletteer_d("lisa::OskarSchlemmer")
#paletteer::paletteer_d("rcartocolor::Temps")
#paletteer::paletteer_d("khroma::BuRd")
ggplot(d[!is.na(d$party_factor),], aes(x = trustGovt, y = voteconfidence, color = negemo_group)) +
geom_jitter(height = .3, width = .3, alpha = 0.3, size = .2) +
geom_smooth(method = "lm", se = FALSE, size = .5, fullrange = T) +
labs(x = "Trust in Federal Government",
y = "Perceived Election Legitimacy",
title = "Perceived Election Legitimacy: Raw Data") +
scale_color_manual("Negative
Emotion", values = c("#009392FF","#E9E29CFF","#B2182BFF")) +
facet_grid(wave ~ party_factor, labeller = as_labeller(wave_party_labels)) +
theme_minimal()
library(ggeffects)
PEL.plot <- predict_response(PEL.m7, terms = c("trustGovt", "Neg_Emo", "party_factor", "wave"))
plot(PEL.plot) +
labs(x = "Trust in Federal Government",
y = "Perceived Election Legitimacy",
color = "Negative
Emotions",
title = "Perceived Election Legitimacy: Model Predicted Relationships") +
theme_bw()
Models
PEL.m5 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad + wave.cub) * (Pos_Emo + Neg_Emo) * trustGovt + (1 | pid),
data = d)
summary(PEL.m5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * (wave.lin + wave.quad +
## wave.cub) * (Pos_Emo + Neg_Emo) * trustGovt + (1 | pid)
## Data: d
##
## REML criterion at convergence: 21190.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7264 -0.4995 0.0414 0.5455 4.2060
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6146 0.7840
## Residual 0.4854 0.6967
## Number of obs: 8000, groups: pid, 2601
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.271e+00 7.116e-02 7.824e+03 45.966
## pDem_Rep -3.731e-01 1.129e-01 7.887e+03 -3.305
## pParty_Ind -1.138e-01 1.836e-01 7.422e+03 -0.620
## wave.lin 5.885e-01 1.626e-01 6.220e+03 3.619
## wave.quad -6.129e-02 2.345e-01 5.947e+03 -0.261
## wave.cub 1.023e-02 1.380e-01 5.950e+03 0.074
## Pos_Emo 1.370e-01 1.708e-02 7.414e+03 8.022
## Neg_Emo -6.382e-02 1.839e-02 7.626e+03 -3.470
## trustGovt 2.664e-01 4.880e-02 7.301e+03 5.458
## pDem_Rep:wave.lin 9.292e-02 2.374e-01 6.124e+03 0.391
## pDem_Rep:wave.quad 2.268e-01 3.505e-01 5.870e+03 0.647
## pDem_Rep:wave.cub -4.858e-01 2.107e-01 5.793e+03 -2.306
## pParty_Ind:wave.lin 3.187e-01 4.432e-01 6.258e+03 0.719
## pParty_Ind:wave.quad -4.529e-01 6.376e-01 5.995e+03 -0.710
## pParty_Ind:wave.cub 1.567e-01 3.735e-01 6.016e+03 0.420
## pDem_Rep:Pos_Emo 5.288e-02 2.477e-02 7.484e+03 2.135
## pDem_Rep:Neg_Emo -2.159e-02 2.817e-02 7.680e+03 -0.767
## pParty_Ind:Pos_Emo -3.176e-02 4.545e-02 7.174e+03 -0.699
## pParty_Ind:Neg_Emo -3.347e-02 4.835e-02 7.431e+03 -0.692
## wave.lin:Pos_Emo -4.679e-02 3.975e-02 6.066e+03 -1.177
## wave.lin:Neg_Emo -8.999e-02 4.133e-02 6.298e+03 -2.177
## wave.quad:Pos_Emo 1.779e-01 5.739e-02 5.927e+03 3.099
## wave.quad:Neg_Emo -1.055e-01 5.979e-02 5.888e+03 -1.764
## wave.cub:Pos_Emo -2.892e-02 3.353e-02 5.960e+03 -0.863
## wave.cub:Neg_Emo 1.306e-02 3.615e-02 5.901e+03 0.361
## pDem_Rep:trustGovt -1.506e-02 8.289e-02 7.340e+03 -0.182
## pParty_Ind:trustGovt 2.515e-01 1.256e-01 7.145e+03 2.003
## wave.lin:trustGovt 3.253e-02 1.186e-01 6.510e+03 0.274
## wave.quad:trustGovt -1.195e-01 1.682e-01 5.925e+03 -0.710
## wave.cub:trustGovt 8.201e-02 1.006e-01 5.916e+03 0.815
## Pos_Emo:trustGovt -1.170e-02 1.181e-02 7.096e+03 -0.990
## Neg_Emo:trustGovt -5.790e-03 1.219e-02 7.323e+03 -0.475
## pDem_Rep:wave.lin:Pos_Emo 8.835e-02 5.445e-02 6.173e+03 1.623
## pDem_Rep:wave.lin:Neg_Emo 9.262e-02 6.117e-02 6.137e+03 1.514
## pDem_Rep:wave.quad:Pos_Emo 1.736e-01 8.105e-02 5.935e+03 2.142
## pDem_Rep:wave.quad:Neg_Emo -1.237e-01 8.918e-02 5.878e+03 -1.387
## pDem_Rep:wave.cub:Pos_Emo 5.261e-02 4.892e-02 5.846e+03 1.075
## pDem_Rep:wave.cub:Neg_Emo 5.405e-02 5.266e-02 5.767e+03 1.026
## pParty_Ind:wave.lin:Pos_Emo 1.421e-01 1.097e-01 6.063e+03 1.295
## pParty_Ind:wave.lin:Neg_Emo -2.704e-01 1.126e-01 6.366e+03 -2.401
## pParty_Ind:wave.quad:Pos_Emo 2.475e-01 1.583e-01 5.967e+03 1.564
## pParty_Ind:wave.quad:Neg_Emo -2.814e-02 1.623e-01 5.901e+03 -0.173
## pParty_Ind:wave.cub:Pos_Emo 2.532e-02 9.158e-02 6.009e+03 0.276
## pParty_Ind:wave.cub:Neg_Emo -6.311e-02 9.887e-02 5.959e+03 -0.638
## pDem_Rep:wave.lin:trustGovt 5.801e-02 1.844e-01 6.175e+03 0.315
## pDem_Rep:wave.quad:trustGovt -2.886e-01 2.780e-01 5.933e+03 -1.038
## pDem_Rep:wave.cub:trustGovt 8.723e-02 1.705e-01 5.842e+03 0.512
## pParty_Ind:wave.lin:trustGovt 9.670e-01 3.165e-01 6.564e+03 3.055
## pParty_Ind:wave.quad:trustGovt -2.742e-01 4.461e-01 5.955e+03 -0.615
## pParty_Ind:wave.cub:trustGovt 4.416e-02 2.655e-01 5.992e+03 0.166
## pDem_Rep:Pos_Emo:trustGovt 7.290e-03 1.811e-02 7.179e+03 0.402
## pDem_Rep:Neg_Emo:trustGovt 2.274e-02 2.042e-02 7.334e+03 1.114
## pParty_Ind:Pos_Emo:trustGovt -2.471e-02 3.150e-02 6.991e+03 -0.785
## pParty_Ind:Neg_Emo:trustGovt -7.836e-02 3.149e-02 7.205e+03 -2.489
## wave.lin:Pos_Emo:trustGovt -1.763e-02 2.910e-02 6.304e+03 -0.606
## wave.lin:Neg_Emo:trustGovt -5.105e-02 2.877e-02 6.500e+03 -1.775
## wave.quad:Pos_Emo:trustGovt 1.654e-03 4.138e-02 5.922e+03 0.040
## wave.quad:Neg_Emo:trustGovt -3.954e-02 4.138e-02 5.896e+03 -0.956
## wave.cub:Pos_Emo:trustGovt 3.488e-02 2.433e-02 5.949e+03 1.434
## wave.cub:Neg_Emo:trustGovt -3.801e-02 2.517e-02 5.865e+03 -1.510
## pDem_Rep:wave.lin:Pos_Emo:trustGovt -5.123e-02 4.089e-02 6.220e+03 -1.253
## pDem_Rep:wave.lin:Neg_Emo:trustGovt -1.003e-01 4.679e-02 6.166e+03 -2.143
## pDem_Rep:wave.quad:Pos_Emo:trustGovt 1.551e-03 6.216e-02 5.985e+03 0.025
## pDem_Rep:wave.quad:Neg_Emo:trustGovt 2.105e-02 6.911e-02 5.960e+03 0.305
## pDem_Rep:wave.cub:Pos_Emo:trustGovt 3.476e-05 3.847e-02 5.900e+03 0.001
## pDem_Rep:wave.cub:Neg_Emo:trustGovt -2.730e-02 4.101e-02 5.820e+03 -0.666
## pParty_Ind:wave.lin:Pos_Emo:trustGovt -2.237e-01 7.969e-02 6.317e+03 -2.807
## pParty_Ind:wave.lin:Neg_Emo:trustGovt -1.892e-01 7.616e-02 6.583e+03 -2.484
## pParty_Ind:wave.quad:Pos_Emo:trustGovt 4.627e-02 1.122e-01 5.929e+03 0.412
## pParty_Ind:wave.quad:Neg_Emo:trustGovt -2.161e-02 1.092e-01 5.903e+03 -0.198
## pParty_Ind:wave.cub:Pos_Emo:trustGovt 3.292e-02 6.531e-02 6.002e+03 0.504
## pParty_Ind:wave.cub:Neg_Emo:trustGovt -1.037e-02 6.710e-02 5.921e+03 -0.155
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.000953 ***
## pParty_Ind 0.535430
## wave.lin 0.000298 ***
## wave.quad 0.793804
## wave.cub 0.940942
## Pos_Emo 1.20e-15 ***
## Neg_Emo 0.000523 ***
## trustGovt 4.97e-08 ***
## pDem_Rep:wave.lin 0.695532
## pDem_Rep:wave.quad 0.517676
## pDem_Rep:wave.cub 0.021162 *
## pParty_Ind:wave.lin 0.472156
## pParty_Ind:wave.quad 0.477555
## pParty_Ind:wave.cub 0.674790
## pDem_Rep:Pos_Emo 0.032803 *
## pDem_Rep:Neg_Emo 0.443350
## pParty_Ind:Pos_Emo 0.484722
## pParty_Ind:Neg_Emo 0.488737
## wave.lin:Pos_Emo 0.239208
## wave.lin:Neg_Emo 0.029498 *
## wave.quad:Pos_Emo 0.001948 **
## wave.quad:Neg_Emo 0.077856 .
## wave.cub:Pos_Emo 0.388361
## wave.cub:Neg_Emo 0.717907
## pDem_Rep:trustGovt 0.855860
## pParty_Ind:trustGovt 0.045245 *
## wave.lin:trustGovt 0.783768
## wave.quad:trustGovt 0.477552
## wave.cub:trustGovt 0.415166
## Pos_Emo:trustGovt 0.322017
## Neg_Emo:trustGovt 0.634697
## pDem_Rep:wave.lin:Pos_Emo 0.104734
## pDem_Rep:wave.lin:Neg_Emo 0.130030
## pDem_Rep:wave.quad:Pos_Emo 0.032253 *
## pDem_Rep:wave.quad:Neg_Emo 0.165425
## pDem_Rep:wave.cub:Pos_Emo 0.282250
## pDem_Rep:wave.cub:Neg_Emo 0.304822
## pParty_Ind:wave.lin:Pos_Emo 0.195413
## pParty_Ind:wave.lin:Neg_Emo 0.016388 *
## pParty_Ind:wave.quad:Pos_Emo 0.117846
## pParty_Ind:wave.quad:Neg_Emo 0.862343
## pParty_Ind:wave.cub:Pos_Emo 0.782225
## pParty_Ind:wave.cub:Neg_Emo 0.523298
## pDem_Rep:wave.lin:trustGovt 0.753060
## pDem_Rep:wave.quad:trustGovt 0.299152
## pDem_Rep:wave.cub:trustGovt 0.608922
## pParty_Ind:wave.lin:trustGovt 0.002261 **
## pParty_Ind:wave.quad:trustGovt 0.538803
## pParty_Ind:wave.cub:trustGovt 0.867904
## pDem_Rep:Pos_Emo:trustGovt 0.687341
## pDem_Rep:Neg_Emo:trustGovt 0.265466
## pParty_Ind:Pos_Emo:trustGovt 0.432725
## pParty_Ind:Neg_Emo:trustGovt 0.012834 *
## wave.lin:Pos_Emo:trustGovt 0.544660
## wave.lin:Neg_Emo:trustGovt 0.075991 .
## wave.quad:Pos_Emo:trustGovt 0.968120
## wave.quad:Neg_Emo:trustGovt 0.339318
## wave.cub:Pos_Emo:trustGovt 0.151728
## wave.cub:Neg_Emo:trustGovt 0.131022
## pDem_Rep:wave.lin:Pos_Emo:trustGovt 0.210288
## pDem_Rep:wave.lin:Neg_Emo:trustGovt 0.032165 *
## pDem_Rep:wave.quad:Pos_Emo:trustGovt 0.980093
## pDem_Rep:wave.quad:Neg_Emo:trustGovt 0.760651
## pDem_Rep:wave.cub:Pos_Emo:trustGovt 0.999279
## pDem_Rep:wave.cub:Neg_Emo:trustGovt 0.505691
## pParty_Ind:wave.lin:Pos_Emo:trustGovt 0.005009 **
## pParty_Ind:wave.lin:Neg_Emo:trustGovt 0.013028 *
## pParty_Ind:wave.quad:Pos_Emo:trustGovt 0.680071
## pParty_Ind:wave.quad:Neg_Emo:trustGovt 0.843149
## pParty_Ind:wave.cub:Pos_Emo:trustGovt 0.614255
## pParty_Ind:wave.cub:Neg_Emo:trustGovt 0.877194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15. Moderator: Polling Info
d$pollsource <- as.factor(d$pollsource)
d$pollsource <- recode_factor(d$pollsource,
`1` = "NYT",
`2` = "Fox News",
`3` = "538/ABC",
`4` = "Silver Bulletin",
`5` = "CNN",
`6` = "NPR",
`7` = "Other")
d$pollsource_other <- as.factor(as.character(d$pollsource_other))
# Using if_else for a single replacement condition:
d$pollsource_combined <- NA
d <- d %>%
dplyr::mutate(pollsource_combined = if_else(pollsource == "Other", pollsource_other, pollsource))
table(d$pollsource_combined)##
## AP friends and family Google
## 11 17 32
## Left-wing sources Major Networks Multiple
## 3 65 18
## newspaper/local news Other Right-wing sources
## 64 105 22
## Social Media YouTube NYT
## 76 14 628
## Fox News 538/ABC Silver Bulletin
## 1878 646 113
## CNN NPR
## 1456 554d <- d %>%
group_by(pid) %>%
fill(pollsource_combined, .direction = "downup") %>%
ungroup()
addmargins(table(d$pollsource_combined,d$wave, exclude = F))##
## 1 2 3 4 Sum
## AP 11 11 11 7 40
## friends and family 17 17 14 13 61
## Google 32 32 27 17 108
## Left-wing sources 3 3 3 0 9
## Major Networks 65 65 61 40 231
## Multiple 18 18 17 13 66
## newspaper/local news 64 64 57 43 228
## Other 105 105 95 70 375
## Right-wing sources 22 22 20 13 77
## Social Media 76 76 63 43 258
## YouTube 14 14 10 6 44
## NYT 185 185 157 101 628
## Fox News 545 545 485 303 1878
## 538/ABC 186 186 168 106 646
## Silver Bulletin 35 35 28 15 113
## CNN 428 428 363 237 1456
## NPR 159 159 141 95 554
## <NA> 650 290 245 148 1333
## Sum 2615 2255 1965 1270 8105d$pollTilt <- NA
d$pollTilt[d$pollsource_combined == "Left-wing sources"] <- "Left-wing"
d$pollTilt[d$pollsource_combined == "NYT" | d$pollsource_combined == "NPR" | d$pollsource_combined == "Silver Bulletin"] <- "Skews Left"
d$pollTilt[d$pollsource_combined == "538/ABC" | d$pollsource_combined == "CNN" | d$pollsource_combined == "AP" | d$pollsource_combined == "Major Networks"] <- "Center"
d$pollTilt[d$pollsource_combined == "Fox News" ] <- "Skews Right"
d$pollTilt[d$pollsource_combined == "Right-wing sources" ] <- "Right-wing"
d$pollTilt[d$pollsource_combined == "Other" | d$pollsource_combined == "newspaper/local news" | d$pollsource_combined == "friends and family" | d$pollsource_combined == "Google" | d$pollsource_combined == "YouTube"] <- NA
d$pollTilt <- factor(d$pollTilt, levels = c("Left-wing",
"Skews Left",
"Center",
"Skews Right",
"Right-wing"))
d$pollTilt_3 <- NA
d$pollTilt_3[d$pollTilt == "Skews Left" | d$pollTilt == "Left-wing"] <- "Skews Left"
d$pollTilt_3[d$pollTilt == "Center"] <- "Center"
d$pollTilt_3[d$pollTilt == "Skews Right" | d$pollTilt == "Right-wing"] <- "Skews Right"
d$pollTilt_3 <- factor(d$pollTilt_3, levels = c("Skews Left",
"Center",
"Skews Right"))
table(d$pollTilt_3)##
## Skews Left Center Skews Right
## 1304 2373 1955n_pollTilt_3_party <- d %>%
filter(!is.na(party_factor) & !is.na(pollTilt_3)) %>%
group_by(party_factor, pollTilt_3) %>%
tally()
n_pollTilt_3 <- d %>%
filter(!is.na(party_factor) & !is.na(pollTilt_3)) %>%
group_by(pollTilt_3) %>%
tally()ggplot(d, aes(x=pollAcc)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Poll Accuracy") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor),], aes(x=pollAcc, 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 = "Perceived Poll Accuracy") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$pollTilt),], aes(x=pollAcc, fill = pollTilt_3)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Poll Source Tilt",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Perceived Poll Accuracy") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d, aes(x=pollFreq)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Poll Frequency") +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),], aes(x=pollFreq, 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 = "Poll Frequency") +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$pollTilt_3),]) +
geom_smooth(aes(x = pollAcc,
y = voteconfidence),
color = "#69b3a2",
method = "lm") +
facet_wrap(~pollTilt_3) +
labs(x = "Perceived Poll Accuracy",
y = "Perceived Election Legitimacy") +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor) & !is.na(d$pollTilt_3) & d$wave == 2,]) +
geom_smooth(aes(x = pollAcc,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~pollTilt_3) +
labs(x = "Perceived Polling Accuracy",
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(-3,3,1))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = pollFreq,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Attention to Polling",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
Summary
Models
d$poll_LR <- NA
d$poll_LR[d$pollTilt_3 == "Skews Left"] <- -1/2
d$poll_LR[d$pollTilt_3 == "Center"] <- 0
d$poll_LR[d$pollTilt_3 == "Skews Right"] <- 1/2
d$poll_LR_C <- NA
d$poll_LR_C[d$pollTilt_3 == "Skews Left"] <- -1/3
d$poll_LR_C[d$pollTilt_3 == "Center"] <- 2/3
d$poll_LR_C[d$pollTilt_3 == "Skews Right"] <- -1/3
PEL.m6 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollFreq * pollAcc + (1|pid),
data = d)
summary(PEL.m6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * pollFreq * pollAcc +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22297.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6487 -0.5118 0.1154 0.5633 3.2757
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6440 0.8025
## Residual 0.6825 0.8261
## Number of obs: 7740, groups: pid, 2255
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.564e+00 5.719e-02 3.661e+03 62.312 < 2e-16
## pDem_Rep -1.340e-01 8.375e-02 3.284e+03 -1.599 0.1098
## pParty_Ind -3.102e-01 1.377e-01 6.754e+03 -2.252 0.0244
## pollFreq -4.584e-02 1.085e-02 3.223e+03 -4.226 2.45e-05
## pollAcc 2.362e-02 2.989e-02 7.663e+03 0.790 0.4294
## pDem_Rep:pollFreq -2.355e-02 1.790e-02 3.250e+03 -1.315 0.1885
## pParty_Ind:pollFreq -2.315e-02 2.485e-02 6.483e+03 -0.932 0.3516
## pDem_Rep:pollAcc -5.448e-02 3.889e-02 7.608e+03 -1.401 0.1613
## pParty_Ind:pollAcc -2.019e-01 7.954e-02 7.711e+03 -2.538 0.0112
## pollFreq:pollAcc 7.813e-03 5.587e-03 7.600e+03 1.398 0.1621
## pDem_Rep:pollFreq:pollAcc 9.654e-03 8.529e-03 7.523e+03 1.132 0.2577
## pParty_Ind:pollFreq:pollAcc 4.727e-02 1.437e-02 7.725e+03 3.290 0.0010
##
## (Intercept) ***
## pDem_Rep
## pParty_Ind *
## pollFreq ***
## pollAcc
## pDem_Rep:pollFreq
## pParty_Ind:pollFreq
## pDem_Rep:pollAcc
## pParty_Ind:pollAcc *
## pollFreq:pollAcc
## pDem_Rep:pollFreq:pollAcc
## pParty_Ind:pollFreq:pollAcc **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp pPrt_I pllFrq pllAcc pDm_R:F pPr_I:F pD_R:A pP_I:A
## pDem_Rep 0.050
## pParty_Ind 0.646 -0.035
## pollFreq -0.913 -0.051 -0.548
## pollAcc 0.125 0.009 0.131 -0.123
## pDm_Rp:pllF -0.045 -0.885 0.029 0.059 -0.011
## pPrty_Ind:F -0.575 0.034 -0.927 0.542 -0.124 -0.034
## pDm_Rp:pllA -0.005 -0.012 -0.002 0.000 0.029 -0.012 0.004
## pPrty_Ind:A 0.129 0.009 0.152 -0.117 0.752 -0.006 -0.149 -0.004
## pllFrq:pllA -0.126 -0.012 -0.120 0.150 -0.909 0.017 0.129 -0.029 -0.651
## pDm_Rp:pF:A -0.001 -0.017 0.003 0.009 -0.025 0.062 -0.009 -0.863 0.004
## pPrty_I:F:A -0.122 -0.005 -0.150 0.127 -0.675 -0.001 0.177 0.006 -0.923
## pllF:A pD_R:F:
## pDem_Rep
## pParty_Ind
## pollFreq
## pollAcc
## pDm_Rp:pllF
## pPrty_Ind:F
## pDm_Rp:pllA
## pPrty_Ind:A
## pllFrq:pllA
## pDm_Rp:pF:A 0.033
## pPrty_I:F:A 0.657 -0.011Simple-effects Models
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ pollFreq * pollAcc + wave + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22220
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5567 -0.5087 0.0891 0.5598 3.6003
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6953 0.8339
## Residual 0.6617 0.8135
## Number of obs: 7740, groups: pid, 2255
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.507e+00 4.668e-02 2.721e+03 75.118 < 2e-16 ***
## pollFreq -5.229e-02 9.204e-03 2.264e+03 -5.681 1.51e-08 ***
## pollAcc 7.727e-02 1.963e-02 7.446e+03 3.937 8.33e-05 ***
## wave2 3.019e-01 2.423e-02 5.477e+03 12.459 < 2e-16 ***
## wave3 2.678e-01 2.542e-02 5.586e+03 10.537 < 2e-16 ***
## wave4 1.629e-01 2.962e-02 5.739e+03 5.501 3.94e-08 ***
## pollFreq:pollAcc -3.165e-03 4.158e-03 7.423e+03 -0.761 0.447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pllFrq pllAcc wave2 wave3 wave4
## pollFreq -0.850
## pollAcc 0.027 -0.047
## wave2 -0.260 0.000 0.000
## wave3 -0.249 0.003 0.021 0.477
## wave4 -0.213 0.001 0.021 0.409 0.410
## pllFrq:pllA -0.045 0.087 -0.872 0.000 -0.010 -0.008PEL.m6.1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * pollFreq + (pDem_Rep + pParty_Ind) * pollAcc + wave + (1|pid),
data = d)
summary(PEL.m6.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * pollFreq + (pDem_Rep +
## pParty_Ind) * pollAcc + wave + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22127.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6191 -0.5107 0.0914 0.5645 3.5330
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6507 0.8066
## Residual 0.6623 0.8138
## Number of obs: 7740, groups: pid, 2255
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.41215 0.05820 3920.40171 58.627 < 2e-16 ***
## pDem_Rep -0.13555 0.08354 3313.80317 -1.623 0.105
## pParty_Ind -0.21845 0.13514 6624.71796 -1.616 0.106
## pollFreq -0.05020 0.01070 3085.76953 -4.693 2.80e-06 ***
## pollAcc 0.07241 0.01195 7610.19184 6.057 1.45e-09 ***
## wave2 0.29946 0.02426 5476.87662 12.344 < 2e-16 ***
## wave3 0.26348 0.02544 5587.89569 10.358 < 2e-16 ***
## wave4 0.16135 0.02963 5750.12304 5.446 5.36e-08 ***
## pDem_Rep:pollFreq -0.02541 0.01782 3249.47708 -1.426 0.154
## pParty_Ind:pollFreq -0.03754 0.02429 6301.79316 -1.546 0.122
## pDem_Rep:pollAcc -0.01561 0.01946 7577.14908 -0.802 0.422
## pParty_Ind:pollAcc 0.03761 0.02997 7716.14196 1.255 0.210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp pPrt_I pllFrq pllAcc wave2 wave3 wave4 pD_R:F
## pDem_Rep 0.050
## pParty_Ind 0.615 -0.037
## pollFreq -0.885 -0.050 -0.536
## pollAcc 0.009 -0.004 0.030 0.045
## wave2 -0.195 -0.002 0.019 -0.008 0.005
## wave3 -0.195 -0.011 0.008 0.002 0.020 0.477
## wave4 -0.178 -0.013 -0.011 0.010 0.018 0.409 0.411
## pDm_Rp:pllF -0.044 -0.886 0.029 0.058 0.007 -0.007 0.006 0.006
## pPrty_Ind:F -0.548 0.036 -0.925 0.530 0.016 -0.008 0.002 0.017 -0.034
## pDm_Rp:pllA -0.016 -0.054 0.001 0.017 0.032 -0.007 0.023 0.015 0.082
## pPrty_Ind:A 0.031 0.010 0.030 0.014 0.604 0.008 -0.001 -0.009 -0.013
## pP_I:F pD_R:A
## pDem_Rep
## pParty_Ind
## pollFreq
## pollAcc
## wave2
## wave3
## wave4
## pDm_Rp:pllF
## pPrty_Ind:F
## pDm_Rp:pllA -0.006
## pPrty_Ind:A 0.040 -0.016PEL.m6.2 <- lmer(voteconfidence ~ (poll_LR + poll_LR_C) + pollFreq + pollAcc + wave + (1|pid),
data = d)
summary(PEL.m6.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (poll_LR + poll_LR_C) + pollFreq + pollAcc +
## wave + (1 | pid)
## Data: d
##
## REML criterion at convergence: 15883.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6745 -0.5081 0.0996 0.5561 3.6281
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6361 0.7975
## Residual 0.6363 0.7977
## Number of obs: 5629, groups: pid, 1639
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.53615 0.05141 2010.55695 68.781 < 2e-16 ***
## poll_LR -0.38325 0.06032 1628.55221 -6.354 2.72e-10 ***
## poll_LR_C 0.12214 0.04593 1624.83887 2.659 0.007904 **
## pollFreq -0.03171 0.01067 1628.45974 -2.972 0.003002 **
## pollAcc 0.06224 0.01088 5458.50942 5.720 1.12e-08 ***
## wave2 0.23154 0.02787 3986.97019 8.309 < 2e-16 ***
## wave3 0.21119 0.02916 4062.72592 7.243 5.22e-13 ***
## wave4 0.11347 0.03412 4182.99044 3.325 0.000891 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pll_LR p_LR_C pllFrq pllAcc wave2 wave3
## poll_LR -0.089
## poll_LR_C -0.087 0.129
## pollFreq -0.833 0.015 0.001
## pollAcc -0.028 -0.030 0.002 0.050
## wave2 -0.271 0.000 0.000 0.000 0.000
## wave3 -0.259 -0.003 0.000 0.000 0.014 0.478
## wave4 -0.222 -0.001 -0.002 0.001 0.016 0.408 0.409PEL.m6.3 <- lmer(voteconfidence ~ (poll_LR + poll_LR_C) + pollFreq + pollAcc + (pDem_Rep + pParty_Ind) + wave + (1|pid),
data = d)
summary(PEL.m6.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (poll_LR + poll_LR_C) + pollFreq + pollAcc +
## (pDem_Rep + pParty_Ind) + wave + (1 | pid)
## Data: d
##
## REML criterion at convergence: 15842.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6956 -0.5126 0.0985 0.5655 3.5874
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6104 0.7813
## Residual 0.6370 0.7981
## Number of obs: 5629, groups: pid, 1639
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.40341 0.05422 2213.33892 62.765 < 2e-16 ***
## poll_LR -0.33058 0.06371 1717.62394 -5.189 2.37e-07 ***
## poll_LR_C 0.10358 0.04688 1673.72918 2.209 0.027286 *
## pollFreq -0.02529 0.01056 1639.40233 -2.395 0.016735 *
## pollAcc 0.06132 0.01082 5427.28317 5.666 1.54e-08 ***
## pDem_Rep -0.11202 0.04920 2567.59083 -2.277 0.022869 *
## pParty_Ind -0.40662 0.06243 4330.95251 -6.513 8.22e-11 ***
## wave2 0.22809 0.02790 3991.11653 8.175 3.92e-16 ***
## wave3 0.20883 0.02918 4065.12957 7.157 9.76e-13 ***
## wave4 0.11546 0.03413 4188.99663 3.383 0.000724 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pll_LR p_LR_C pllFrq pllAcc pDm_Rp pPrt_I wave2 wave3
## poll_LR -0.092
## poll_LR_C -0.079 0.021
## pollFreq -0.808 0.003 0.012
## pollAcc -0.018 -0.031 0.003 0.048
## pDem_Rep 0.041 -0.362 0.263 0.030 0.006
## pParty_Ind 0.349 0.023 -0.047 -0.102 0.023 -0.063
## wave2 -0.248 0.009 -0.008 -0.003 0.000 -0.025 0.029
## wave3 -0.239 0.004 -0.005 -0.002 0.014 -0.018 0.020 0.478
## wave4 -0.212 0.005 -0.006 0.001 0.015 -0.019 0.000 0.409 0.4096. Moderator: GCB-5
Generic Conspiracist Beliefs are measured by agreement (-2 = Definitely false, +2 = Definitely true) with the following items:
The government permits or perpetrates acts of terrorism on its own soil, disguising its involvement
Evidence of alien contact is being concealed from the public
New and advanced technology which would harm current industry is being suppressed
Certain significant events have been the result of the activity of a small group who secretly manipulate world events
Experiments involving new drugs or technologies are routinely carried out on the public without their knowledge or consent
(Kay & Slovic, 2023)
ggplot(d, aes(x=GCB)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Generic Conspiracist Beliefs") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor),], aes(x=GCB, 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 = "Generic Conspiracist Beliefs") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = GCB,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Generic Conspiracist Beliefs",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(-2,2,1))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = GCB,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Generic Conspiracist Beliefs",
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(-2,2,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = GCB,
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 = "Generic Conspiracist Beliefs",
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)) +
scale_x_continuous(breaks = seq(-2,2,1))
Summary
Models
PEL.m7 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(PEL.m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB * (wave.lin +
## wave.quad + wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22064.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6565 -0.5093 0.0581 0.5483 3.7626
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6635 0.8146
## Residual 0.5405 0.7352
## Number of obs: 8102, groups: pid, 2613
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.371e+00 2.161e-02 3.298e+03 155.962 < 2e-16 ***
## pDem_Rep -4.632e-02 3.840e-02 3.758e+03 -1.206 0.22775
## pParty_Ind -3.980e-01 4.726e-02 6.617e+03 -8.422 < 2e-16 ***
## GCB -2.664e-01 2.344e-02 3.457e+03 -11.368 < 2e-16 ***
## wave.lin 1.385e-01 2.891e-02 5.960e+03 4.791 1.70e-06 ***
## wave.quad 4.424e-01 4.344e-02 5.721e+03 10.184 < 2e-16 ***
## wave.cub -8.769e-02 2.665e-02 5.630e+03 -3.291 0.00101 **
## pDem_Rep:GCB 1.076e-01 3.966e-02 3.745e+03 2.713 0.00671 **
## pParty_Ind:GCB -9.788e-02 5.354e-02 6.361e+03 -1.828 0.06757 .
## pDem_Rep:wave.lin 1.148e+00 5.209e-02 5.902e+03 22.033 < 2e-16 ***
## pDem_Rep:wave.quad 1.045e+00 7.768e-02 5.697e+03 13.456 < 2e-16 ***
## pDem_Rep:wave.cub -6.209e-01 4.695e-02 5.607e+03 -13.224 < 2e-16 ***
## pParty_Ind:wave.lin 2.154e-02 7.485e-02 6.041e+03 0.288 0.77354
## pParty_Ind:wave.quad 2.745e-02 1.133e-01 5.816e+03 0.242 0.80853
## pParty_Ind:wave.cub 7.832e-02 6.969e-02 5.708e+03 1.124 0.26110
## GCB:wave.lin 7.678e-02 3.171e-02 5.975e+03 2.422 0.01548 *
## GCB:wave.quad 7.300e-02 4.731e-02 5.681e+03 1.543 0.12290
## GCB:wave.cub -5.552e-03 2.917e-02 5.639e+03 -0.190 0.84904
## pDem_Rep:GCB:wave.lin 2.536e-01 5.410e-02 5.912e+03 4.688 2.82e-06 ***
## pDem_Rep:GCB:wave.quad 1.886e-01 8.050e-02 5.704e+03 2.342 0.01919 *
## pDem_Rep:GCB:wave.cub -1.571e-01 4.857e-02 5.612e+03 -3.234 0.00123 **
## pParty_Ind:GCB:wave.lin 2.371e-02 8.349e-02 6.040e+03 0.284 0.77644
## pParty_Ind:GCB:wave.quad 6.863e-02 1.251e-01 5.742e+03 0.549 0.58321
## pParty_Ind:GCB:wave.cub 2.682e-02 7.753e-02 5.701e+03 0.346 0.72942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects Models
PEL.m7.0 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB + (1|pid),
data = d)
summary(PEL.m7.0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB + (1 | pid)
## Data: d
##
## REML criterion at convergence: 23306.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5381 -0.5199 0.0979 0.5607 3.3561
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6156 0.7846
## Residual 0.6831 0.8265
## Number of obs: 8102, groups: pid, 2613
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.34503 0.02148 3095.59821 155.736 < 2e-16 ***
## pDem_Rep -0.19022 0.03851 3369.86973 -4.939 8.22e-07 ***
## pParty_Ind -0.46573 0.04854 5753.39743 -9.595 < 2e-16 ***
## GCB -0.28365 0.02327 3211.07762 -12.188 < 2e-16 ***
## pDem_Rep:GCB 0.07192 0.03974 3357.92863 1.810 0.0704 .
## pParty_Ind:GCB -0.12838 0.05461 5506.64832 -2.351 0.0188 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp pPrt_I GCB pD_R:G
## pDem_Rep 0.115
## pParty_Ind 0.467 -0.066
## GCB -0.167 -0.156 -0.086
## pDem_Rp:GCB -0.166 -0.202 0.095 0.137
## pPrty_I:GCB -0.083 0.091 -0.176 0.528 -0.067PEL.m7.w1 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB * (wave1_2 + wave1_3 + wave1_4) + (1|pid),
data = d)
summary(PEL.m7.w1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB * (wave1_2 + wave1_3 +
## wave1_4) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22069.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6565 -0.5093 0.0581 0.5483 3.7626
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6635 0.8146
## Residual 0.5405 0.7352
## Number of obs: 8102, groups: pid, 2613
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.16895 0.02528 5582.28287 125.335 < 2e-16 ***
## pDem_Rep -1.03667 0.04619 6327.61928 -22.442 < 2e-16 ***
## pParty_Ind -0.39605 0.05857 7995.19287 -6.762 1.46e-11 ***
## GCB -0.32445 0.02728 5750.56636 -11.892 < 2e-16 ***
## wave1_2 0.32160 0.02708 5822.29057 11.875 < 2e-16 ***
## wave1_3 0.30316 0.02854 5887.68835 10.623 < 2e-16 ***
## wave1_4 0.18233 0.03269 5951.93540 5.578 2.55e-08 ***
## pDem_Rep:GCB -0.10563 0.04761 6291.21192 -2.218 0.02656 *
## pParty_Ind:GCB -0.12019 0.06516 7936.28258 -1.845 0.06513 .
## pDem_Rep:wave1_2 1.27521 0.04854 5753.21974 26.273 < 2e-16 ***
## pDem_Rep:wave1_3 1.22811 0.05079 5816.27556 24.182 < 2e-16 ***
## pDem_Rep:wave1_4 1.45809 0.05943 5901.14699 24.535 < 2e-16 ***
## pParty_Ind:wave1_2 -0.03963 0.07069 5951.24269 -0.561 0.57506
## pParty_Ind:wave1_3 0.04946 0.07451 5997.72256 0.664 0.50684
## pParty_Ind:wave1_4 -0.01762 0.08452 6040.26282 -0.208 0.83485
## GCB:wave1_2 0.05986 0.02934 5797.66177 2.041 0.04134 *
## GCB:wave1_3 0.09270 0.03123 5884.80484 2.968 0.00301 **
## GCB:wave1_4 0.07956 0.03577 5948.09336 2.224 0.02619 *
## pDem_Rep:GCB:wave1_2 0.27549 0.05012 5763.63808 5.497 4.03e-08 ***
## pDem_Rep:GCB:wave1_3 0.24524 0.05237 5824.45712 4.683 2.90e-06 ***
## pDem_Rep:GCB:wave1_4 0.33217 0.06182 5913.85382 5.373 8.03e-08 ***
## pParty_Ind:GCB:wave1_2 0.02013 0.07757 5885.38597 0.259 0.79527
## pParty_Ind:GCB:wave1_3 0.05880 0.08299 5973.61777 0.709 0.47861
## pParty_Ind:GCB:wave1_4 0.01030 0.09391 6005.72698 0.110 0.91268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary( lmer(voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB + (wave1_2 + wave1_3 + wave1_4) + (1|pid),
data = d) )## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB + (wave1_2 + wave1_3 +
## wave1_4) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 23143.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5694 -0.5132 0.0778 0.5640 3.6261
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6208 0.7879
## Residual 0.6623 0.8138
## Number of obs: 8102, groups: pid, 2613
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.19048 0.02475 5199.19156 128.929 < 2e-16 ***
## pDem_Rep -0.20229 0.03841 3396.46442 -5.267 1.47e-07 ***
## pParty_Ind -0.44091 0.04824 5825.54476 -9.140 < 2e-16 ***
## GCB -0.28024 0.02321 3222.64460 -12.073 < 2e-16 ***
## wave1_2 0.29643 0.02383 5802.06437 12.439 < 2e-16 ***
## wave1_3 0.25710 0.02500 5893.38065 10.282 < 2e-16 ***
## wave1_4 0.15053 0.02924 6009.75169 5.148 2.72e-07 ***
## pDem_Rep:GCB 0.06739 0.03962 3384.64848 1.701 0.0890 .
## pParty_Ind:GCB -0.12219 0.05427 5579.50877 -2.251 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp pPrt_I GCB wav1_2 wav1_3 wav1_4 pD_R:G
## pDem_Rep 0.114
## pParty_Ind 0.385 -0.067
## GCB -0.153 -0.157 -0.085
## wave1_2 -0.419 -0.024 0.031 0.009
## wave1_3 -0.400 -0.020 0.028 0.013 0.460
## wave1_4 -0.347 -0.023 0.014 0.018 0.393 0.394
## pDem_Rp:GCB -0.138 -0.202 0.095 0.137 -0.008 -0.008 -0.010
## pPrty_I:GCB -0.078 0.091 -0.176 0.526 0.008 0.009 0.011 -0.067##
## Call:
## lm(formula = voteconfidence ~ (pDem_Rep + pParty_Ind) * GCB,
## data = d[d$wave == 1, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.03023 -0.78634 0.08107 0.78248 2.96906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.13994 0.02594 121.024 < 2e-16 ***
## pDem_Rep -1.05660 0.04822 -21.912 < 2e-16 ***
## pParty_Ind -0.52410 0.06568 -7.979 2.18e-15 ***
## GCB -0.31513 0.02813 -11.202 < 2e-16 ***
## pDem_Rep:GCB -0.09736 0.04967 -1.960 0.0501 .
## pParty_Ind:GCB -0.09701 0.07261 -1.336 0.1817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.092 on 2607 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2619, Adjusted R-squared: 0.2605
## F-statistic: 185 on 5 and 2607 DF, p-value: < 2.2e-167. Moderator: Affective Polarization
Outgroup Rating Version
ggplot(d, aes(x=FT_Outgroup)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Perception of Outparty") 
ggplot(d[!is.na(d$party_factor),], aes(x=FT_Outgroup, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Perception of Outparty") 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = FT_Outgroup,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Perception of Outparty",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = FT_Outgroup,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perception of Outparty",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = FT_Outgroup,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width =.2, alpha=0.4, size = .2, show.legend = F ) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perception of Outparty",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5))
#### Ingroup - Outgroup Version
ggplot(d, aes(x=affPol)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Affective Polarization") 
ggplot(d, aes(x=affPol, y = voteconfidence)) +
geom_jitter(alpha = .4, size = .2) +
geom_smooth(method = "lm", color="#69b3a2", se = F) +
theme_bw() +
labs(x = "Affective Polarization",
y = "Perceived Election Legitimacy") +
coord_cartesian(ylim = c(1,5), xlim = c(0,100))
ggplot(d[!is.na(d$party_factor),], aes(x=affPol, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Affective Polarization") 
ggplot(d[!is.na(d$party_factor),], aes(x=affPol, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
facet_grid(wave~., labeller = as_labeller(wave_label)) +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Affective Polarization") 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = affPol,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Affective Polarization",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(0,100)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = affPol,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(alpha = .4, size = .2) +
geom_smooth(method = "lm", se = F, fullrange = T) +
labs(x = "Affective Polarization",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw()# +
# coord_cartesian(ylim = c(1,5), xlim = c(0,100))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = affPol,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Affective Polarization",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(0,100)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = affPol,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width =.2, alpha=0.4, size = .2, show.legend = F ) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Affective Polarization",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5), xlim = c(0,100)) 
Displaying ingroup and outgroup ratings separately
d.FT <- d %>%
pivot_longer(cols = c(FT_parties_1, FT_parties_2, FT_pols_1, FT_pols_2),
names_to = "FTgroup",
values_to = "FT")
d.FT$FTgroup <- recode_factor(d.FT$FTgroup,
"FT_parties_1" = "Democrats",
"FT_parties_2" = "Republicans",
"FT_pols_1" = "Harris",
"FT_pols_2" = "Trump")
d.FT$FTgroup <- factor(d.FT$FTgroup,
levels = c("Democrats",
"Republicans",
"Harris",
"Trump"))
# Ingroup & Outgroup Plotted
ggplot(d.FT[!is.na(d.FT$party_factor) & d.FT$FTgroup != "Trump" & d.FT$FTgroup != "Harris",],
aes(x = wave,
y = FT,
color = FTgroup,
group = FTgroup)) +
geom_jitter(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 = "Participant Party ID",
y = "Feeling Thermometer Ratings") +
scale_color_manual("Target Party",
values = c("#1696d2","#db2b27")) +
coord_cartesian(ylim = c(0,100)) +
scale_x_discrete(labels = c("Wave 1", "Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
ggplot(d.FT[!is.na(d.FT$party_factor) & d.FT$FTgroup != "Democrats" & d.FT$FTgroup != "Republicans",],
aes(x = wave,
y = FT,
color = FTgroup,
group = FTgroup)) +
geom_jitter(alpha = .2, size = .2) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed") +
stat_summary(geom = "errorbar", color = "black", width = .1) +
stat_summary(geom = "point", fun = "mean") +
facet_wrap(~party_factor) +
labs(x = "Participant Party ID",
y = "Feeling Thermometer Ratings") +
scale_color_manual("Target Candidate",
values = c("darkblue","darkred")) +
coord_cartesian(ylim = c(0,100)) +
scale_x_discrete(labels = c("Wave 1", "Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
ggplot(d.FT[!is.na(d.FT$party_factor),],
aes(x = wave,
y = FT,
color = FTgroup,
group = FTgroup)) +
# geom_jitter(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 = "Participant Party ID",
y = "Feeling Thermometer Ratings") +
scale_color_manual("Partisan Target",
values = c("#1696d2","#db2b27", "darkblue", "darkred")) +
coord_cartesian(ylim = c(0,100)) +
scale_x_discrete(labels = c("Wave 1", "Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
Summary
Models
PEL.m8 <- lmer(voteconfidence ~ (pDem_Rep) * affPol * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(PEL.m8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * affPol * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 19318.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6017 -0.5009 0.0580 0.5456 3.6555
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6912 0.8314
## Residual 0.5066 0.7118
## Number of obs: 7162, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.541e+00 2.807e-02 4.631e+03 126.161 < 2e-16 ***
## pDem_Rep -1.366e-01 5.214e-02 6.076e+03 -2.620 0.008822 **
## affPol -8.816e-04 3.877e-04 7.026e+03 -2.274 0.023019 *
## wave.lin 2.691e-01 4.401e-02 5.346e+03 6.114 1.04e-09 ***
## wave.quad 2.192e-01 6.540e-02 5.172e+03 3.352 0.000809 ***
## wave.cub -1.118e-01 3.938e-02 5.082e+03 -2.839 0.004541 **
## pDem_Rep:affPol -5.855e-04 7.638e-04 7.067e+03 -0.767 0.443358
## pDem_Rep:wave.lin 4.202e-01 8.865e-02 5.388e+03 4.740 2.20e-06 ***
## pDem_Rep:wave.quad 6.248e-01 1.318e-01 5.216e+03 4.741 2.18e-06 ***
## pDem_Rep:wave.cub -2.889e-01 7.925e-02 5.119e+03 -3.646 0.000269 ***
## affPol:wave.lin -1.099e-03 6.900e-04 5.353e+03 -1.593 0.111115
## affPol:wave.quad 5.012e-03 1.028e-03 5.185e+03 4.876 1.11e-06 ***
## affPol:wave.cub -7.415e-04 6.213e-04 5.110e+03 -1.194 0.232721
## pDem_Rep:affPol:wave.lin 1.482e-02 1.385e-03 5.376e+03 10.696 < 2e-16 ***
## pDem_Rep:affPol:wave.quad 1.021e-02 2.064e-03 5.211e+03 4.947 7.77e-07 ***
## pDem_Rep:affPol:wave.cub -6.971e-03 1.244e-03 5.122e+03 -5.603 2.22e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models Y ~ X m ~ X Y ~ m + X
PEL.m8.w <- lmer(voteconfidence ~ (pDem_Rep) * affPol * wave + (1|pid),
data = d[d$party_factor != "Independent",])
summary(PEL.m8.w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * affPol * wave + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 19322.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6017 -0.5009 0.0580 0.5456 3.6555
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.6912 0.8314
## Residual 0.5066 0.7118
## Number of obs: 7162, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.324e+00 3.467e-02 6.608e+03 95.867 < 2e-16 ***
## pDem_Rep -5.751e-01 6.659e-02 7.133e+03 -8.636 < 2e-16 ***
## affPol -1.770e-03 5.227e-04 7.023e+03 -3.387 0.000711 ***
## wave2 2.607e-01 4.002e-02 5.317e+03 6.514 8.00e-11 ***
## wave3 2.834e-01 4.214e-02 5.326e+03 6.726 1.92e-11 ***
## wave4 3.250e-01 5.036e-02 5.360e+03 6.453 1.20e-10 ***
## pDem_Rep:affPol -1.229e-02 1.041e-03 7.005e+03 -11.803 < 2e-16 ***
## pDem_Rep:wave2 6.342e-01 8.096e-02 5.388e+03 7.833 5.68e-15 ***
## pDem_Rep:wave3 5.553e-01 8.502e-02 5.381e+03 6.532 7.11e-11 ***
## pDem_Rep:wave4 5.647e-01 1.015e-01 5.406e+03 5.562 2.79e-08 ***
## affPol:wave2 2.787e-03 6.371e-04 5.347e+03 4.375 1.24e-05 ***
## affPol:wave3 1.496e-03 6.758e-04 5.336e+03 2.214 0.026868 *
## affPol:wave4 -7.288e-04 7.888e-04 5.368e+03 -0.924 0.355591
## pDem_Rep:affPol:wave2 1.404e-02 1.280e-03 5.381e+03 10.968 < 2e-16 ***
## pDem_Rep:affPol:wave3 1.447e-02 1.357e-03 5.365e+03 10.668 < 2e-16 ***
## pDem_Rep:affPol:wave4 1.830e-02 1.582e-03 5.389e+03 11.567 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18. Moderator: Process variable
ggplot(d, aes(x=govt_process)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Governmental Process Preference") +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),], aes(x=govt_process, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Governmental Process Preference") +
scale_x_continuous(breaks = seq(1,7,1)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = govt_process,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Governmental Process Preference",
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$party_factor),]) +
geom_smooth(aes(x = govt_process,
y = voteconfidence,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Governmental Process Preference",
y = "Perceived Election Legitimacy") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = govt_process,
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 = "Governmental Process Preference",
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
Models
PEL.m9 <- lmer(voteconfidence ~ (pDem_Rep) * govt_process * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(PEL.m9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * govt_process * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13666.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7917 -0.5032 0.0590 0.5514 4.0064
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7418 0.8613
## Residual 0.5341 0.7308
## Number of obs: 5076, groups: pid, 1269
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.076e+00 9.396e-02 1.262e+03 32.740
## pDem_Rep -1.573e-01 1.795e-01 2.183e+03 -0.876
## govt_process 9.569e-02 2.255e-02 1.262e+03 4.243
## wave.lin 2.570e-01 9.290e-02 3.796e+03 2.766
## wave.quad 7.317e-01 1.468e-01 3.792e+03 4.983
## wave.cub -2.950e-01 9.287e-02 3.793e+03 -3.176
## pDem_Rep:govt_process -3.515e-04 4.290e-02 2.155e+03 -0.008
## pDem_Rep:wave.lin 1.559e+00 2.023e-01 3.815e+03 7.708
## pDem_Rep:wave.quad 1.425e+00 3.185e-01 3.808e+03 4.476
## pDem_Rep:wave.cub -9.841e-01 2.011e-01 3.808e+03 -4.894
## govt_process:wave.lin -2.205e-02 2.230e-02 3.797e+03 -0.989
## govt_process:wave.quad -5.384e-02 3.525e-02 3.792e+03 -1.527
## govt_process:wave.cub 3.109e-02 2.229e-02 3.793e+03 1.395
## pDem_Rep:govt_process:wave.lin -7.777e-02 4.819e-02 3.815e+03 -1.614
## pDem_Rep:govt_process:wave.quad -6.664e-02 7.587e-02 3.807e+03 -0.878
## pDem_Rep:govt_process:wave.cub 7.982e-02 4.792e-02 3.807e+03 1.666
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.3809
## govt_process 2.36e-05 ***
## wave.lin 0.0057 **
## wave.quad 6.53e-07 ***
## wave.cub 0.0015 **
## pDem_Rep:govt_process 0.9935
## pDem_Rep:wave.lin 1.62e-14 ***
## pDem_Rep:wave.quad 7.84e-06 ***
## pDem_Rep:wave.cub 1.03e-06 ***
## govt_process:wave.lin 0.3229
## govt_process:wave.quad 0.1267
## govt_process:wave.cub 0.1632
## pDem_Rep:govt_process:wave.lin 0.1067
## pDem_Rep:govt_process:wave.quad 0.3798
## pDem_Rep:govt_process:wave.cub 0.0959 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
# Wave 4, govt process midpoint
d$gov_proc.4 <- d$govt_process - 4
PEL.m9.W4 <- lm(voteconfidence ~ (pDem_Rep) + govt_process,
data = d[d$wave == 4,])
summary(PEL.m9.W4)##
## Call:
## lm(formula = voteconfidence ~ (pDem_Rep) + govt_process, data = d[d$wave ==
## 4, ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8438 -0.5961 0.1701 0.9224 1.9362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.09896 0.11693 26.502 < 2e-16 ***
## pDem_Rep 0.24772 0.06982 3.548 0.000403 ***
## govt_process 0.08871 0.02806 3.161 0.001608 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.164 on 1266 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01707, Adjusted R-squared: 0.01552
## F-statistic: 10.99 on 2 and 1266 DF, p-value: 1.849e-05PEL.m9 <- lmer(voteconfidence ~ (pDem_Rep) * gov_proc.4 * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(PEL.m9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * gov_proc.4 * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13666.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7917 -0.5032 0.0590 0.5514 4.0064
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7418 0.8613
## Residual 0.5341 0.7308
## Number of obs: 5076, groups: pid, 1269
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.459e+00 2.638e-02 1.267e+03 131.106 < 2e-16
## pDem_Rep -1.587e-01 5.006e-02 2.130e+03 -3.171 0.00154
## gov_proc.4 9.569e-02 2.255e-02 1.262e+03 4.243 2.36e-05
## wave.lin 1.688e-01 2.612e-02 3.794e+03 6.464 1.15e-10
## wave.quad 5.164e-01 4.128e-02 3.792e+03 12.510 < 2e-16
## wave.cub -1.706e-01 2.610e-02 3.792e+03 -6.537 7.10e-11
## pDem_Rep:gov_proc.4 -3.515e-04 4.290e-02 2.155e+03 -0.008 0.99346
## pDem_Rep:wave.lin 1.248e+00 5.603e-02 3.813e+03 22.271 < 2e-16
## pDem_Rep:wave.quad 1.159e+00 8.830e-02 3.808e+03 13.123 < 2e-16
## pDem_Rep:wave.cub -6.648e-01 5.578e-02 3.807e+03 -11.917 < 2e-16
## gov_proc.4:wave.lin -2.205e-02 2.230e-02 3.797e+03 -0.989 0.32288
## gov_proc.4:wave.quad -5.384e-02 3.525e-02 3.792e+03 -1.527 0.12673
## gov_proc.4:wave.cub 3.109e-02 2.229e-02 3.793e+03 1.395 0.16322
## pDem_Rep:gov_proc.4:wave.lin -7.777e-02 4.819e-02 3.815e+03 -1.614 0.10665
## pDem_Rep:gov_proc.4:wave.quad -6.664e-02 7.587e-02 3.807e+03 -0.878 0.37984
## pDem_Rep:gov_proc.4:wave.cub 7.982e-02 4.792e-02 3.807e+03 1.666 0.09586
##
## (Intercept) ***
## pDem_Rep **
## gov_proc.4 ***
## wave.lin ***
## wave.quad ***
## wave.cub ***
## pDem_Rep:gov_proc.4
## pDem_Rep:wave.lin ***
## pDem_Rep:wave.quad ***
## pDem_Rep:wave.cub ***
## gov_proc.4:wave.lin
## gov_proc.4:wave.quad
## gov_proc.4:wave.cub
## pDem_Rep:gov_proc.4:wave.lin
## pDem_Rep:gov_proc.4:wave.quad
## pDem_Rep:gov_proc.4:wave.cub .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 19. Moderator: Representation
ggplot(d, aes(x=representation_Dem)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Representation (Democratic elites)") +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),], aes(x=representation_Dem, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","grey","#db2b27")) +
labs(x = "Representation (Democratic elites)") +
scale_x_continuous(breaks = seq(1,7,1)) 
ggplot(d, aes(x=representation_Rep)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Representation (Republican elites)") +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$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
Models
PEL.m10 <- lmer(voteconfidence ~ (pDem_Rep) * (representation_Dem + representation_Rep) * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(PEL.m10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## voteconfidence ~ (pDem_Rep) * (representation_Dem + representation_Rep) *
## (wave.lin + wave.quad + wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13551.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7723 -0.4901 0.0511 0.5526 3.9093
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7111 0.8433
## Residual 0.5222 0.7226
## Number of obs: 5072, groups: pid, 1268
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.83426 0.07991 1281.94068 35.467
## pDem_Rep -0.15197 0.15500 2378.47435 -0.981
## representation_Dem 0.14858 0.02226 1340.71752 6.675
## representation_Rep 0.07453 0.02128 1339.00659 3.502
## wave.lin 0.10669 0.08010 3786.68883 1.332
## wave.quad 0.67254 0.12655 3781.69043 5.314
## wave.cub -0.22251 0.08001 3781.96041 -2.781
## pDem_Rep:representation_Dem -0.08389 0.04280 1967.51050 -1.960
## pDem_Rep:representation_Rep 0.09381 0.04059 2105.13157 2.311
## pDem_Rep:wave.lin 1.10436 0.18054 3811.84915 6.117
## pDem_Rep:wave.quad 1.23725 0.28393 3804.81534 4.358
## pDem_Rep:wave.cub -0.58854 0.17880 3801.01526 -3.292
## representation_Dem:wave.lin -0.12648 0.02260 3787.71937 -5.598
## representation_Dem:wave.quad -0.14066 0.03572 3785.25083 -3.938
## representation_Dem:wave.cub 0.06927 0.02260 3784.38430 3.065
## representation_Rep:wave.lin 0.13856 0.02161 3789.93910 6.411
## representation_Rep:wave.quad 0.04834 0.03416 3785.24094 1.415
## representation_Rep:wave.cub -0.03621 0.02161 3785.30136 -1.676
## pDem_Rep:representation_Dem:wave.lin -0.02489 0.04738 3800.25501 -0.525
## pDem_Rep:representation_Dem:wave.quad -0.25092 0.07474 3795.25798 -3.357
## pDem_Rep:representation_Dem:wave.cub 0.08281 0.04728 3796.93371 1.752
## pDem_Rep:representation_Rep:wave.lin -0.00125 0.04578 3806.55662 -0.027
## pDem_Rep:representation_Rep:wave.quad 0.15247 0.07221 3801.16405 2.111
## pDem_Rep:representation_Rep:wave.cub -0.07391 0.04559 3796.25040 -1.621
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.326934
## representation_Dem 3.61e-11 ***
## representation_Rep 0.000477 ***
## wave.lin 0.182939
## wave.quad 1.13e-07 ***
## wave.cub 0.005445 **
## pDem_Rep:representation_Dem 0.050130 .
## pDem_Rep:representation_Rep 0.020917 *
## pDem_Rep:wave.lin 1.05e-09 ***
## pDem_Rep:wave.quad 1.35e-05 ***
## pDem_Rep:wave.cub 0.001005 **
## representation_Dem:wave.lin 2.33e-08 ***
## representation_Dem:wave.quad 8.37e-05 ***
## representation_Dem:wave.cub 0.002189 **
## representation_Rep:wave.lin 1.62e-10 ***
## representation_Rep:wave.quad 0.157157
## representation_Rep:wave.cub 0.093786 .
## pDem_Rep:representation_Dem:wave.lin 0.599332
## pDem_Rep:representation_Dem:wave.quad 0.000795 ***
## pDem_Rep:representation_Dem:wave.cub 0.079914 .
## pDem_Rep:representation_Rep:wave.lin 0.978212
## pDem_Rep:representation_Rep:wave.quad 0.034798 *
## pDem_Rep:representation_Rep:wave.cub 0.105059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
PEL.m10.w <- lmer(voteconfidence ~ (pDem_Rep) * (representation_Dem + representation_Rep) * wave + (pDem_Rep | pid),
data = d)
summary(PEL.m10.w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## voteconfidence ~ (pDem_Rep) * (representation_Dem + representation_Rep) *
## wave + (pDem_Rep | pid)
## Data: d
##
## REML criterion at convergence: 13546.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7708 -0.4922 0.0553 0.5473 3.9583
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pid (Intercept) 0.6777 0.8232
## pDem_Rep 0.1391 0.3729 -0.36
## Residual 0.5192 0.7206
## Number of obs: 5072, groups: pid, 1268
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.544e+00 9.632e-02 2.554e+03 26.416
## pDem_Rep -1.158e+00 2.009e-01 2.128e+03 -5.762
## representation_Dem 2.656e-01 2.696e-02 2.567e+03 9.853
## representation_Rep -1.476e-02 2.572e-02 2.559e+03 -0.574
## wave2 5.279e-01 8.944e-02 3.752e+03 5.903
## wave3 3.627e-01 8.939e-02 3.763e+03 4.057
## wave4 2.138e-01 8.952e-02 3.791e+03 2.389
## pDem_Rep:representation_Dem 4.198e-03 5.400e-02 2.170e+03 0.078
## pDem_Rep:representation_Rep 4.426e-02 5.141e-02 2.005e+03 0.861
## pDem_Rep:wave2 1.331e+00 2.007e-01 3.698e+03 6.631
## pDem_Rep:wave3 1.292e+00 2.010e-01 3.712e+03 6.429
## pDem_Rep:wave4 1.391e+00 2.022e-01 3.744e+03 6.879
## representation_Dem:wave2 -1.535e-01 2.519e-02 3.765e+03 -6.095
## representation_Dem:wave3 -1.482e-01 2.521e-02 3.751e+03 -5.879
## representation_Dem:wave4 -1.602e-01 2.524e-02 3.773e+03 -6.348
## representation_Rep:wave2 8.656e-02 2.413e-02 3.756e+03 3.588
## representation_Rep:wave3 1.191e-01 2.408e-02 3.759e+03 4.945
## representation_Rep:wave4 1.573e-01 2.414e-02 3.788e+03 6.516
## pDem_Rep:representation_Dem:wave2 -1.919e-01 5.286e-02 3.703e+03 -3.631
## pDem_Rep:representation_Dem:wave3 -1.211e-01 5.291e-02 3.701e+03 -2.288
## pDem_Rep:representation_Dem:wave4 -6.479e-02 5.294e-02 3.722e+03 -1.224
## pDem_Rep:representation_Rep:wave2 1.315e-01 5.099e-02 3.697e+03 2.579
## pDem_Rep:representation_Rep:wave3 5.707e-02 5.100e-02 3.703e+03 1.119
## pDem_Rep:representation_Rep:wave4 3.703e-02 5.116e-02 3.729e+03 0.724
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 9.55e-09 ***
## representation_Dem < 2e-16 ***
## representation_Rep 0.566151
## wave2 3.89e-09 ***
## wave3 5.06e-05 ***
## wave4 0.016957 *
## pDem_Rep:representation_Dem 0.938036
## pDem_Rep:representation_Rep 0.389399
## pDem_Rep:wave2 3.81e-11 ***
## pDem_Rep:wave3 1.45e-10 ***
## pDem_Rep:wave4 7.05e-12 ***
## representation_Dem:wave2 1.20e-09 ***
## representation_Dem:wave3 4.50e-09 ***
## representation_Dem:wave4 2.44e-10 ***
## representation_Rep:wave2 0.000338 ***
## representation_Rep:wave3 7.96e-07 ***
## representation_Rep:wave4 8.18e-11 ***
## pDem_Rep:representation_Dem:wave2 0.000286 ***
## pDem_Rep:representation_Dem:wave3 0.022201 *
## pDem_Rep:representation_Dem:wave4 0.221107
## pDem_Rep:representation_Rep:wave2 0.009942 **
## pDem_Rep:representation_Rep:wave3 0.263218
## pDem_Rep:representation_Rep:wave4 0.469211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1misc. other plots
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconf_natl,
fill = party_factor)) +
geom_bar(stat = "summary",
fun = "mean",
position = position_dodge(.9)) +
stat_summary(geom = "errorbar",position = position_dodge(.9), color = "black", width = .1) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy (National)") +
coord_cartesian(ylim = c(1,5)) 
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconf_natl,
color = wave,
group = 1)) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed") +
stat_summary(geom = "errorbar",position = position_dodge(.9), color = "black", width = .1) +
stat_summary(geom = "point", fun = "mean", position = position_dodge(.9)) +
facet_wrap(~party_factor) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy (National)") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconf_self,
fill = party_factor)) +
geom_bar(stat = "summary",
fun = "mean",
position = position_dodge(.9)) +
stat_summary(geom = "errorbar",position = position_dodge(.9), color = "black", width = .1) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy (Own Vote)") +
coord_cartesian(ylim = c(1,5)) 
ggplot(d[!is.na(d$party_factor),],
aes(x = wave,
y = voteconf_self,
color = wave,
group = 1)) +
stat_summary(geom = "path", fun = "mean", color = "black", linetype = "dashed") +
stat_summary(geom = "errorbar",position = position_dodge(.9), color = "black", width = .1) +
stat_summary(geom = "point", fun = "mean", position = position_dodge(.9)) +
facet_wrap(~party_factor) +
labs(x = "Participant Party ID",
y = "Perceived Election Legitimacy (Own Vote)") +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1))
10. Full Model
Model
d$FT_Diff.100 <- d$FT_Diff/100
PEL.m11 <- lmer(voteconfidence ~ (pDem_Rep + pParty_Ind)
* wave
* trustGovt.c
* (Pos_Emo.c + Neg_Emo.c)
* FT_Diff.100
+ (govt_process + representation_Dem + representation_Rep)
+ GCB.c
+ pollAcc
+ pollFreq.c
+ vs_age
+ vs_race
+ male_female
+ nonbinary_mf
+ (1 | pid),
data = d)
summary(PEL.m11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep + pParty_Ind) * wave * trustGovt.c *
## (Pos_Emo.c + Neg_Emo.c) * FT_Diff.100 + (govt_process + representation_Dem +
## representation_Rep) + GCB.c + pollAcc + pollFreq.c + vs_age +
## vs_race + male_female + nonbinary_mf + (1 | pid)
## Data: d
##
## REML criterion at convergence: 11295.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7996 -0.5210 0.0347 0.5595 3.5720
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.5102 0.7143
## Residual 0.4501 0.6709
## Number of obs: 4423, groups: pid, 1159
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.729e+00 1.821e-01
## pDem_Rep -6.852e-01 1.018e-01
## wave2 2.048e-01 6.437e-02
## wave3 1.981e-01 6.438e-02
## wave4 3.302e-01 6.422e-02
## trustGovt.c 2.405e-01 4.586e-02
## Pos_Emo.c 2.016e-01 4.116e-02
## Neg_Emo.c -2.495e-02 4.365e-02
## FT_Diff.100 -1.691e-01 8.358e-02
## govt_process 4.027e-02 2.103e-02
## representation_Dem 5.335e-02 2.173e-02
## representation_Rep 4.162e-02 2.011e-02
## GCB.c -2.314e-01 2.664e-02
## pollAcc 3.780e-02 1.082e-02
## pollFreq.c -3.399e-03 1.095e-02
## vs_age 5.300e-03 1.765e-03
## vs_raceBlack -3.299e-01 7.953e-02
## vs_raceHispanic 3.445e-02 8.428e-02
## vs_raceOther -1.310e-05 8.983e-02
## male_female -2.566e-01 4.858e-02
## nonbinary_mf -2.802e-02 3.633e-01
## pDem_Rep:wave2 5.087e-01 1.301e-01
## pDem_Rep:wave3 6.205e-01 1.297e-01
## pDem_Rep:wave4 6.377e-01 1.298e-01
## pDem_Rep:trustGovt.c 2.179e-01 9.077e-02
## wave2:trustGovt.c -1.220e-01 5.855e-02
## wave3:trustGovt.c -8.621e-02 6.508e-02
## wave4:trustGovt.c -1.773e-02 6.549e-02
## pDem_Rep:Pos_Emo.c -7.716e-02 8.181e-02
## pDem_Rep:Neg_Emo.c -6.210e-02 8.661e-02
## wave2:Pos_Emo.c -6.019e-02 5.580e-02
## wave3:Pos_Emo.c -1.453e-01 5.495e-02
## wave4:Pos_Emo.c -1.916e-01 5.628e-02
## wave2:Neg_Emo.c -6.865e-02 5.639e-02
## wave3:Neg_Emo.c -8.053e-02 5.625e-02
## wave4:Neg_Emo.c 3.417e-02 5.601e-02
## trustGovt.c:Pos_Emo.c -7.277e-02 3.382e-02
## trustGovt.c:Neg_Emo.c 2.366e-03 4.007e-02
## pDem_Rep:FT_Diff.100 -7.123e-01 1.680e-01
## wave2:FT_Diff.100 9.005e-02 1.156e-01
## wave3:FT_Diff.100 1.784e-01 1.117e-01
## wave4:FT_Diff.100 -6.686e-02 1.154e-01
## trustGovt.c:FT_Diff.100 -5.871e-02 7.530e-02
## Pos_Emo.c:FT_Diff.100 -2.485e-02 6.297e-02
## Neg_Emo.c:FT_Diff.100 -3.541e-02 6.946e-02
## pDem_Rep:wave2:trustGovt.c -1.369e-01 1.181e-01
## pDem_Rep:wave3:trustGovt.c -2.142e-01 1.301e-01
## pDem_Rep:wave4:trustGovt.c -2.563e-01 1.314e-01
## pDem_Rep:wave2:Pos_Emo.c 1.404e-01 1.129e-01
## pDem_Rep:wave3:Pos_Emo.c 2.386e-01 1.102e-01
## pDem_Rep:wave4:Pos_Emo.c 2.536e-02 1.133e-01
## pDem_Rep:wave2:Neg_Emo.c 1.771e-02 1.137e-01
## pDem_Rep:wave3:Neg_Emo.c 7.510e-02 1.131e-01
## pDem_Rep:wave4:Neg_Emo.c 2.024e-03 1.131e-01
## pDem_Rep:trustGovt.c:Pos_Emo.c 7.145e-02 6.746e-02
## pDem_Rep:trustGovt.c:Neg_Emo.c 1.056e-01 8.001e-02
## wave2:trustGovt.c:Pos_Emo.c 9.325e-02 4.787e-02
## wave3:trustGovt.c:Pos_Emo.c 1.240e-01 4.617e-02
## wave4:trustGovt.c:Pos_Emo.c 8.237e-02 4.617e-02
## wave2:trustGovt.c:Neg_Emo.c 5.027e-02 5.106e-02
## wave3:trustGovt.c:Neg_Emo.c 3.436e-02 5.219e-02
## wave4:trustGovt.c:Neg_Emo.c 2.130e-02 5.329e-02
## pDem_Rep:wave2:FT_Diff.100 3.944e-01 2.318e-01
## pDem_Rep:wave3:FT_Diff.100 6.653e-01 2.238e-01
## pDem_Rep:wave4:FT_Diff.100 6.602e-01 2.319e-01
## pDem_Rep:trustGovt.c:FT_Diff.100 1.712e-01 1.506e-01
## wave2:trustGovt.c:FT_Diff.100 8.928e-02 1.026e-01
## wave3:trustGovt.c:FT_Diff.100 -5.404e-02 1.124e-01
## wave4:trustGovt.c:FT_Diff.100 -7.614e-02 1.141e-01
## pDem_Rep:Pos_Emo.c:FT_Diff.100 8.913e-02 1.259e-01
## pDem_Rep:Neg_Emo.c:FT_Diff.100 -4.261e-02 1.386e-01
## wave2:Pos_Emo.c:FT_Diff.100 2.024e-01 8.829e-02
## wave3:Pos_Emo.c:FT_Diff.100 1.251e-01 8.652e-02
## wave4:Pos_Emo.c:FT_Diff.100 1.681e-01 8.763e-02
## wave2:Neg_Emo.c:FT_Diff.100 2.101e-02 9.054e-02
## wave3:Neg_Emo.c:FT_Diff.100 1.466e-01 9.014e-02
## wave4:Neg_Emo.c:FT_Diff.100 -6.985e-02 9.333e-02
## trustGovt.c:Pos_Emo.c:FT_Diff.100 6.630e-02 5.272e-02
## trustGovt.c:Neg_Emo.c:FT_Diff.100 -1.297e-02 5.960e-02
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c -1.144e-01 9.724e-02
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c -1.149e-01 9.273e-02
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c -2.021e-02 9.215e-02
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c -7.020e-02 1.029e-01
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c -6.499e-02 1.042e-01
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c -4.573e-02 1.071e-01
## pDem_Rep:wave2:trustGovt.c:FT_Diff.100 -1.294e-01 2.056e-01
## pDem_Rep:wave3:trustGovt.c:FT_Diff.100 -2.610e-01 2.250e-01
## pDem_Rep:wave4:trustGovt.c:FT_Diff.100 -1.606e-01 2.288e-01
## pDem_Rep:wave2:Pos_Emo.c:FT_Diff.100 -6.534e-02 1.781e-01
## pDem_Rep:wave3:Pos_Emo.c:FT_Diff.100 -9.340e-02 1.735e-01
## pDem_Rep:wave4:Pos_Emo.c:FT_Diff.100 1.382e-01 1.762e-01
## pDem_Rep:wave2:Neg_Emo.c:FT_Diff.100 -1.184e-01 1.815e-01
## pDem_Rep:wave3:Neg_Emo.c:FT_Diff.100 1.366e-01 1.801e-01
## pDem_Rep:wave4:Neg_Emo.c:FT_Diff.100 1.843e-01 1.881e-01
## pDem_Rep:trustGovt.c:Pos_Emo.c:FT_Diff.100 -8.941e-02 1.054e-01
## pDem_Rep:trustGovt.c:Neg_Emo.c:FT_Diff.100 -1.407e-01 1.191e-01
## wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100 -1.516e-01 7.488e-02
## wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100 -1.277e-01 7.296e-02
## wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100 -4.273e-02 7.117e-02
## wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100 -4.688e-02 7.956e-02
## wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100 -7.227e-02 7.911e-02
## wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100 2.180e-02 8.424e-02
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100 1.441e-01 1.513e-01
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100 1.750e-01 1.463e-01
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100 -2.013e-02 1.418e-01
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100 1.238e-01 1.596e-01
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100 -4.645e-02 1.588e-01
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100 -4.083e-02 1.689e-01
## df t value Pr(>|t|)
## (Intercept) 1.279e+03 14.984 < 2e-16
## pDem_Rep 4.300e+03 -6.728 1.95e-11
## wave2 3.368e+03 3.181 0.001482
## wave3 3.416e+03 3.077 0.002111
## wave4 3.404e+03 5.142 2.87e-07
## trustGovt.c 3.839e+03 5.245 1.65e-07
## Pos_Emo.c 3.693e+03 4.898 1.01e-06
## Neg_Emo.c 3.710e+03 -0.572 0.567629
## FT_Diff.100 4.049e+03 -2.024 0.043051
## govt_process 1.123e+03 1.915 0.055788
## representation_Dem 1.243e+03 2.455 0.014226
## representation_Rep 1.218e+03 2.070 0.038703
## GCB.c 1.166e+03 -8.688 < 2e-16
## pollAcc 4.307e+03 3.495 0.000479
## pollFreq.c 1.132e+03 -0.310 0.756346
## vs_age 1.161e+03 3.003 0.002731
## vs_raceBlack 1.164e+03 -4.148 3.60e-05
## vs_raceHispanic 1.136e+03 0.409 0.682765
## vs_raceOther 1.111e+03 0.000 0.999884
## male_female 1.118e+03 -5.283 1.53e-07
## nonbinary_mf 1.122e+03 -0.077 0.938540
## pDem_Rep:wave2 3.410e+03 3.910 9.39e-05
## pDem_Rep:wave3 3.437e+03 4.783 1.80e-06
## pDem_Rep:wave4 3.434e+03 4.914 9.34e-07
## pDem_Rep:trustGovt.c 3.810e+03 2.401 0.016412
## wave2:trustGovt.c 3.358e+03 -2.083 0.037347
## wave3:trustGovt.c 3.483e+03 -1.325 0.185351
## wave4:trustGovt.c 3.560e+03 -0.271 0.786598
## pDem_Rep:Pos_Emo.c 3.672e+03 -0.943 0.345646
## pDem_Rep:Neg_Emo.c 3.647e+03 -0.717 0.473468
## wave2:Pos_Emo.c 3.462e+03 -1.079 0.280800
## wave3:Pos_Emo.c 3.478e+03 -2.645 0.008205
## wave4:Pos_Emo.c 3.539e+03 -3.405 0.000670
## wave2:Neg_Emo.c 3.484e+03 -1.217 0.223514
## wave3:Neg_Emo.c 3.487e+03 -1.432 0.152333
## wave4:Neg_Emo.c 3.478e+03 0.610 0.541834
## trustGovt.c:Pos_Emo.c 3.673e+03 -2.152 0.031491
## trustGovt.c:Neg_Emo.c 3.659e+03 0.059 0.952920
## pDem_Rep:FT_Diff.100 4.047e+03 -4.239 2.29e-05
## wave2:FT_Diff.100 3.441e+03 0.779 0.435977
## wave3:FT_Diff.100 3.476e+03 1.597 0.110330
## wave4:FT_Diff.100 3.487e+03 -0.579 0.562471
## trustGovt.c:FT_Diff.100 3.737e+03 -0.780 0.435596
## Pos_Emo.c:FT_Diff.100 3.631e+03 -0.395 0.693113
## Neg_Emo.c:FT_Diff.100 3.672e+03 -0.510 0.610171
## pDem_Rep:wave2:trustGovt.c 3.386e+03 -1.159 0.246660
## pDem_Rep:wave3:trustGovt.c 3.486e+03 -1.647 0.099617
## pDem_Rep:wave4:trustGovt.c 3.566e+03 -1.950 0.051213
## pDem_Rep:wave2:Pos_Emo.c 3.513e+03 1.244 0.213711
## pDem_Rep:wave3:Pos_Emo.c 3.494e+03 2.165 0.030454
## pDem_Rep:wave4:Pos_Emo.c 3.562e+03 0.224 0.822968
## pDem_Rep:wave2:Neg_Emo.c 3.508e+03 0.156 0.876242
## pDem_Rep:wave3:Neg_Emo.c 3.499e+03 0.664 0.506874
## pDem_Rep:wave4:Neg_Emo.c 3.506e+03 0.018 0.985724
## pDem_Rep:trustGovt.c:Pos_Emo.c 3.649e+03 1.059 0.289617
## pDem_Rep:trustGovt.c:Neg_Emo.c 3.640e+03 1.319 0.187098
## wave2:trustGovt.c:Pos_Emo.c 3.487e+03 1.948 0.051465
## wave3:trustGovt.c:Pos_Emo.c 3.520e+03 2.686 0.007273
## wave4:trustGovt.c:Pos_Emo.c 3.525e+03 1.784 0.074514
## wave2:trustGovt.c:Neg_Emo.c 3.430e+03 0.985 0.324913
## wave3:trustGovt.c:Neg_Emo.c 3.523e+03 0.658 0.510422
## wave4:trustGovt.c:Neg_Emo.c 3.533e+03 0.400 0.689371
## pDem_Rep:wave2:FT_Diff.100 3.446e+03 1.702 0.088885
## pDem_Rep:wave3:FT_Diff.100 3.476e+03 2.973 0.002971
## pDem_Rep:wave4:FT_Diff.100 3.503e+03 2.847 0.004445
## pDem_Rep:trustGovt.c:FT_Diff.100 3.740e+03 1.137 0.255618
## wave2:trustGovt.c:FT_Diff.100 3.448e+03 0.870 0.384417
## wave3:trustGovt.c:FT_Diff.100 3.527e+03 -0.481 0.630686
## wave4:trustGovt.c:FT_Diff.100 3.624e+03 -0.667 0.504578
## pDem_Rep:Pos_Emo.c:FT_Diff.100 3.632e+03 0.708 0.479148
## pDem_Rep:Neg_Emo.c:FT_Diff.100 3.661e+03 -0.307 0.758597
## wave2:Pos_Emo.c:FT_Diff.100 3.509e+03 2.293 0.021927
## wave3:Pos_Emo.c:FT_Diff.100 3.466e+03 1.446 0.148395
## wave4:Pos_Emo.c:FT_Diff.100 3.551e+03 1.918 0.055171
## wave2:Neg_Emo.c:FT_Diff.100 3.468e+03 0.232 0.816537
## wave3:Neg_Emo.c:FT_Diff.100 3.527e+03 1.626 0.103970
## wave4:Neg_Emo.c:FT_Diff.100 3.498e+03 -0.748 0.454264
## trustGovt.c:Pos_Emo.c:FT_Diff.100 3.628e+03 1.257 0.208660
## trustGovt.c:Neg_Emo.c:FT_Diff.100 3.655e+03 -0.218 0.827737
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c 3.546e+03 -1.176 0.239510
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c 3.534e+03 -1.239 0.215301
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c 3.518e+03 -0.219 0.826400
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c 3.457e+03 -0.682 0.495357
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c 3.519e+03 -0.624 0.532987
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c 3.554e+03 -0.427 0.669402
## pDem_Rep:wave2:trustGovt.c:FT_Diff.100 3.452e+03 -0.629 0.529246
## pDem_Rep:wave3:trustGovt.c:FT_Diff.100 3.532e+03 -1.160 0.246049
## pDem_Rep:wave4:trustGovt.c:FT_Diff.100 3.625e+03 -0.702 0.482817
## pDem_Rep:wave2:Pos_Emo.c:FT_Diff.100 3.545e+03 -0.367 0.713765
## pDem_Rep:wave3:Pos_Emo.c:FT_Diff.100 3.486e+03 -0.538 0.590457
## pDem_Rep:wave4:Pos_Emo.c:FT_Diff.100 3.582e+03 0.784 0.432976
## pDem_Rep:wave2:Neg_Emo.c:FT_Diff.100 3.473e+03 -0.652 0.514282
## pDem_Rep:wave3:Neg_Emo.c:FT_Diff.100 3.517e+03 0.758 0.448207
## pDem_Rep:wave4:Neg_Emo.c:FT_Diff.100 3.527e+03 0.980 0.327292
## pDem_Rep:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.622e+03 -0.848 0.396465
## pDem_Rep:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.637e+03 -1.181 0.237675
## wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.529e+03 -2.024 0.043030
## wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.551e+03 -1.750 0.080175
## wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.536e+03 -0.600 0.548325
## wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.444e+03 -0.589 0.555727
## wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.610e+03 -0.913 0.361070
## wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.590e+03 0.259 0.795807
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.570e+03 0.952 0.340971
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.564e+03 1.197 0.231576
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100 3.522e+03 -0.142 0.887064
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.453e+03 0.776 0.437913
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.620e+03 -0.293 0.769904
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100 3.597e+03 -0.242 0.808973
##
## (Intercept) ***
## pDem_Rep ***
## wave2 **
## wave3 **
## wave4 ***
## trustGovt.c ***
## Pos_Emo.c ***
## Neg_Emo.c
## FT_Diff.100 *
## govt_process .
## representation_Dem *
## representation_Rep *
## GCB.c ***
## pollAcc ***
## pollFreq.c
## vs_age **
## vs_raceBlack ***
## vs_raceHispanic
## vs_raceOther
## male_female ***
## nonbinary_mf
## pDem_Rep:wave2 ***
## pDem_Rep:wave3 ***
## pDem_Rep:wave4 ***
## pDem_Rep:trustGovt.c *
## wave2:trustGovt.c *
## wave3:trustGovt.c
## wave4:trustGovt.c
## pDem_Rep:Pos_Emo.c
## pDem_Rep:Neg_Emo.c
## wave2:Pos_Emo.c
## wave3:Pos_Emo.c **
## wave4:Pos_Emo.c ***
## wave2:Neg_Emo.c
## wave3:Neg_Emo.c
## wave4:Neg_Emo.c
## trustGovt.c:Pos_Emo.c *
## trustGovt.c:Neg_Emo.c
## pDem_Rep:FT_Diff.100 ***
## wave2:FT_Diff.100
## wave3:FT_Diff.100
## wave4:FT_Diff.100
## trustGovt.c:FT_Diff.100
## Pos_Emo.c:FT_Diff.100
## Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave2:trustGovt.c
## pDem_Rep:wave3:trustGovt.c .
## pDem_Rep:wave4:trustGovt.c .
## pDem_Rep:wave2:Pos_Emo.c
## pDem_Rep:wave3:Pos_Emo.c *
## pDem_Rep:wave4:Pos_Emo.c
## pDem_Rep:wave2:Neg_Emo.c
## pDem_Rep:wave3:Neg_Emo.c
## pDem_Rep:wave4:Neg_Emo.c
## pDem_Rep:trustGovt.c:Pos_Emo.c
## pDem_Rep:trustGovt.c:Neg_Emo.c
## wave2:trustGovt.c:Pos_Emo.c .
## wave3:trustGovt.c:Pos_Emo.c **
## wave4:trustGovt.c:Pos_Emo.c .
## wave2:trustGovt.c:Neg_Emo.c
## wave3:trustGovt.c:Neg_Emo.c
## wave4:trustGovt.c:Neg_Emo.c
## pDem_Rep:wave2:FT_Diff.100 .
## pDem_Rep:wave3:FT_Diff.100 **
## pDem_Rep:wave4:FT_Diff.100 **
## pDem_Rep:trustGovt.c:FT_Diff.100
## wave2:trustGovt.c:FT_Diff.100
## wave3:trustGovt.c:FT_Diff.100
## wave4:trustGovt.c:FT_Diff.100
## pDem_Rep:Pos_Emo.c:FT_Diff.100
## pDem_Rep:Neg_Emo.c:FT_Diff.100
## wave2:Pos_Emo.c:FT_Diff.100 *
## wave3:Pos_Emo.c:FT_Diff.100
## wave4:Pos_Emo.c:FT_Diff.100 .
## wave2:Neg_Emo.c:FT_Diff.100
## wave3:Neg_Emo.c:FT_Diff.100
## wave4:Neg_Emo.c:FT_Diff.100
## trustGovt.c:Pos_Emo.c:FT_Diff.100
## trustGovt.c:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c
## pDem_Rep:wave2:trustGovt.c:FT_Diff.100
## pDem_Rep:wave3:trustGovt.c:FT_Diff.100
## pDem_Rep:wave4:trustGovt.c:FT_Diff.100
## pDem_Rep:wave2:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave3:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave4:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave2:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave3:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave4:Neg_Emo.c:FT_Diff.100
## pDem_Rep:trustGovt.c:Pos_Emo.c:FT_Diff.100
## pDem_Rep:trustGovt.c:Neg_Emo.c:FT_Diff.100
## wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100 *
## wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100 .
## wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100
## wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100
## wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100
## wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave2:trustGovt.c:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave3:trustGovt.c:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave4:trustGovt.c:Pos_Emo.c:FT_Diff.100
## pDem_Rep:wave2:trustGovt.c:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave3:trustGovt.c:Neg_Emo.c:FT_Diff.100
## pDem_Rep:wave4:trustGovt.c:Neg_Emo.c:FT_Diff.100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 48 columns / coefficientsPEL Path Models
LGM
library(lavaan)
#d1 <- read.csv("Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_w1.csv", na = "NA")
#d2 <- read.csv("Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_w2.csv", fileEncoding="latin1")
#d3 <- read.csv("Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_w3.csv", na = "NA")
#d4 <- read.csv("Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_w4.csv", na = "NA")
#d.wide <- d1 %>% left_join(d2, by = "pid")
#d.wide <- d.wide %>% left_join(d3, by = "pid")
#d.wide <- d.wide %>% left_join(d4, by = "pid")
#write.csv(d.wide, "Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_wide.csv")
d.wide <- read.csv("Documents/Research/24_ElectionStudy/data + analyses/2024ElectionStudy_cleancases_wide.csv")
PEL.lgm_model <- '
# Define latent intercept and slope factors
i =~ 1*voteconfidence_w1 + 1*voteconfidence_w2 + 1*voteconfidence_w3 + 1*voteconfidence_w4
s =~ 0*voteconfidence_w1 + 1*voteconfidence_w2 + 2*voteconfidence_w3 + 3*voteconfidence_w4
# Estimate means of latent factors
i ~ 1
s ~ 1
# Estimate variances and covariance
i ~~ i
s ~~ s
i ~~ s
# Estimate residual variances of observed variables
voteconfidence_w1 ~~ voteconfidence_w1
voteconfidence_w2 ~~ voteconfidence_w2
voteconfidence_w3 ~~ voteconfidence_w3
voteconfidence_w4 ~~ voteconfidence_w4
'
fit <- lavaan::growth(PEL.lgm_model, data = d.wide)
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-19 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 1270 2615
##
## Model Test User Model:
##
## Test statistic 134.438
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2114.906
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.939
## Tucker-Lewis Index (TLI) 0.926
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7018.605
## Loglikelihood unrestricted model (H1) -6951.386
##
## Akaike (AIC) 14055.210
## Bayesian (BIC) 14101.531
## Sample-size adjusted Bayesian (SABIC) 14072.943
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.143
## 90 Percent confidence interval - lower 0.123
## 90 Percent confidence interval - upper 0.164
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.075
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i =~
## voteconfdnc_w1 1.000 0.789 0.581
## voteconfdnc_w2 1.000 0.789 0.706
## voteconfdnc_w3 1.000 0.789 0.736
## voteconfdnc_w4 1.000 0.789 0.660
## s =~
## voteconfdnc_w1 0.000 0.000 0.000
## voteconfdnc_w2 1.000 0.152 0.136
## voteconfdnc_w3 2.000 0.304 0.284
## voteconfdnc_w4 3.000 0.456 0.381
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i ~~
## s 0.031 0.017 1.843 0.065 0.262 0.262
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 3.502 0.031 113.159 0.000 4.436 4.436
## s 0.001 0.011 0.070 0.944 0.005 0.005
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## i 0.623 0.053 11.844 0.000 1.000 1.000
## s 0.023 0.009 2.670 0.008 1.000 1.000
## .voteconfdnc_w1 1.223 0.062 19.678 0.000 1.223 0.662
## .voteconfdnc_w2 0.541 0.028 19.388 0.000 0.541 0.433
## .voteconfdnc_w3 0.309 0.020 15.521 0.000 0.309 0.268
## .voteconfdnc_w4 0.410 0.032 12.675 0.000 0.410 0.287LGM with moderators
d.wide$dem_rep_w1 <- ifelse(d.wide$party_factor_w1 == "Republican", 1, 0)
d.wide$dem_ind_w1 <- ifelse(d.wide$party_factor_w1 == "Independent", 1, 0)
d.wide$dem_rep_w2 <- ifelse(d.wide$party_factor_w2 == "Republican", 1, 0)
d.wide$dem_ind_w2 <- ifelse(d.wide$party_factor_w2 == "Independent", 1, 0)
d.wide$dem_rep_w3 <- ifelse(d.wide$party_factor_w3 == "Republican", 1, 0)
d.wide$dem_ind_w3 <- ifelse(d.wide$party_factor_w3 == "Independent", 1, 0)
d.wide$dem_rep_w4 <- ifelse(d.wide$party_factor_w4 == "Republican", 1, 0)
d.wide$dem_ind_w4 <- ifelse(d.wide$party_factor_w4 == "Independent", 1, 0)
d.wide$FT_DemminusRep_w1.100 <- d.wide$FT_DemminusRep_w1/100
d.wide$FT_DemminusRep_w2.100 <- d.wide$FT_DemminusRep_w2/100
d.wide$FT_DemminusRep_w3.100 <- d.wide$FT_DemminusRep_w3/100
d.wide$FT_DemminusRep_w4.100 <- d.wide$FT_DemminusRep_w4/100
PEL.lgm_exp <- '
### 1. Latent growth factors ###
i =~ 1*voteconfidence_w1 + 1*voteconfidence_w2 + 1*voteconfidence_w3 + 1*voteconfidence_w4
s =~ 0*voteconfidence_w1 + 1*voteconfidence_w2 + 2*voteconfidence_w3 + 3*voteconfidence_w4
### 2. Time-invariant predictors (trait-level moderators) ###
i ~ pollAcc
s ~ pollAcc
### 3. Time-varying predictors (state-level moderators)
voteconfidence_w1 ~ trustGovt_w1
voteconfidence_w3 ~ trustGovt_w3
voteconfidence_w4 ~ trustGovt_w4
voteconfidence_w1 ~ dem_rep_w1
voteconfidence_w2 ~ dem_rep_w2
voteconfidence_w3 ~ dem_rep_w3
voteconfidence_w4 ~ dem_rep_w4
voteconfidence_w1 ~ dem_ind_w1
voteconfidence_w2 ~ dem_ind_w2
voteconfidence_w3 ~ dem_ind_w3
voteconfidence_w4 ~ dem_ind_w4
voteconfidence_w1 ~ Pos_Emo_w1
voteconfidence_w2 ~ Pos_Emo_w2
voteconfidence_w3 ~ Pos_Emo_w3
voteconfidence_w4 ~ Pos_Emo_w4
voteconfidence_w1 ~ Neg_Emo_w1
voteconfidence_w2 ~ Neg_Emo_w2
voteconfidence_w3 ~ Neg_Emo_w3
voteconfidence_w4 ~ Neg_Emo_w4
voteconfidence_w1 ~ FT_DemminusRep_w1.100
voteconfidence_w2 ~ FT_DemminusRep_w2.100
voteconfidence_w3 ~ FT_DemminusRep_w3.100
voteconfidence_w4 ~ FT_DemminusRep_w4.100
### 4. Optional: Variances and covariances ###
i ~~ s
i ~~ i
s ~~ s
voteconfidence_w1 ~~ voteconfidence_w1
voteconfidence_w2 ~~ voteconfidence_w2
voteconfidence_w3 ~~ voteconfidence_w3
voteconfidence_w4 ~~ voteconfidence_w4
'
#fit.lgm_exp <- sem(PEL.lgm_exp, data = d.wide, missing = "ML", fixed.x = F)
#summary(fit.lgm_exp, standardized = TRUE, fit.measures = TRUE)
PEL.lgm_dexp <- '
### 1. Latent growth factors ###
i =~ 1*voteconfidence_w1 + 1*voteconfidence_w2 + 1*voteconfidence_w3 + 1*voteconfidence_w4
s =~ 0*voteconfidence_w1 + 1*voteconfidence_w2 + 2*voteconfidence_w3 + 3*voteconfidence_w4
### 2. Time-invariant predictors (trait-level moderators) ###
i ~ pollAcc + dem_rep_w1 + dem_ind_w1
s ~ pollAcc + dem_rep_w1 + dem_ind_w1
### 3. Time-varying predictors (state-level moderators)
voteconfidence_w1 ~ trustGovt_w1
voteconfidence_w3 ~ trustGovt_w3
voteconfidence_w4 ~ trustGovt_w4
voteconfidence_w1 ~ Pos_Emo_w1
voteconfidence_w2 ~ Pos_Emo_w2
voteconfidence_w3 ~ Pos_Emo_w3
voteconfidence_w4 ~ Pos_Emo_w4
voteconfidence_w1 ~ Neg_Emo_w1
voteconfidence_w2 ~ Neg_Emo_w2
voteconfidence_w3 ~ Neg_Emo_w3
voteconfidence_w4 ~ Neg_Emo_w4
### 4. Optional: Variances and covariances ###
i ~~ s
i ~~ i
s ~~ s
voteconfidence_w1 ~~ voteconfidence_w1
voteconfidence_w2 ~~ voteconfidence_w2
voteconfidence_w3 ~~ voteconfidence_w3
voteconfidence_w4 ~~ voteconfidence_w4
'
#fit.lgm_dexp <- sem(PEL.lgm_dexp, data = d.wide, missing = "ML", fixed.x = F)
#summary(fit.lgm_dexp, standardized = TRUE, fit.measures = TRUE)
d.wide$trustDR_int1 <- d.wide$trustGovt_w1 * d.wide$dem_rep_w1
# d.wide$trustDR_int2 <- d.wide$trustGovt_w2 * d.wide$dem_rep_w2
d.wide$trustDR_int3 <- d.wide$trustGovt_w3 * d.wide$dem_rep_w3
d.wide$trustDR_int4 <- d.wide$trustGovt_w4 * d.wide$dem_rep_w4
d.wide$trustDI_int1 <- d.wide$trustGovt_w1 * d.wide$dem_ind_w1
# d.wide$trustDI_int2 <- d.wide$trustGovt_w2 * d.wide$dem_ind_w2
d.wide$trustDI_int3 <- d.wide$trustGovt_w3 * d.wide$dem_ind_w3
d.wide$trustDI_int4 <- d.wide$trustGovt_w4 * d.wide$dem_ind_w4
d.wide$posemoDR_int1 <- d.wide$Pos_Emo_w1 * d.wide$dem_rep_w1
d.wide$posemoDR_int2 <- d.wide$Pos_Emo_w2 * d.wide$dem_rep_w2
d.wide$posemoDR_int3 <- d.wide$Pos_Emo_w3 * d.wide$dem_rep_w3
d.wide$posemoDR_int4 <- d.wide$Pos_Emo_w4 * d.wide$dem_rep_w4
d.wide$negemoDR_int1 <- d.wide$Neg_Emo_w1 * d.wide$dem_rep_w1
d.wide$negemoDR_int2 <- d.wide$Neg_Emo_w2 * d.wide$dem_rep_w2
d.wide$negemoDR_int3 <- d.wide$Neg_Emo_w3 * d.wide$dem_rep_w3
d.wide$negemoDR_int4 <- d.wide$Neg_Emo_w4 * d.wide$dem_rep_w4
d.wide$posemoDI_int1 <- d.wide$Pos_Emo_w1 * d.wide$dem_ind_w1
d.wide$posemoDI_int2 <- d.wide$Pos_Emo_w2 * d.wide$dem_ind_w2
d.wide$posemoDI_int3 <- d.wide$Pos_Emo_w3 * d.wide$dem_ind_w3
d.wide$posemoDI_int4 <- d.wide$Pos_Emo_w4 * d.wide$dem_ind_w4
d.wide$negemoDI_int1 <- d.wide$Neg_Emo_w1 * d.wide$dem_ind_w1
d.wide$negemoDI_int2 <- d.wide$Neg_Emo_w2 * d.wide$dem_ind_w2
d.wide$negemoDI_int3 <- d.wide$Neg_Emo_w3 * d.wide$dem_ind_w3
d.wide$negemoDI_int4 <- d.wide$Neg_Emo_w4 * d.wide$dem_ind_w4
PEL.lgm_int <- '
### 1. Latent growth factors ###
i =~ 1*voteconfidence_w1 + 1*voteconfidence_w2 + 1*voteconfidence_w3 + 1*voteconfidence_w4
s =~ 0*voteconfidence_w1 + 1*voteconfidence_w2 + 2*voteconfidence_w3 + 5*voteconfidence_w4
### 2. Time-invariant predictors (trait-level moderators) ###
i ~ pollAcc + dem_rep_w1 + dem_ind_w1 + GCB
s ~ pollAcc + dem_rep_w1 + dem_ind_w1 + GCB
### 3. Time-varying predictors (state-level moderators)
voteconfidence_w1 ~ trustGovt_w1 + trustDR_int1 + trustDI_int1
voteconfidence_w3 ~ trustGovt_w3 + trustDR_int3 + trustDI_int3
voteconfidence_w4 ~ trustGovt_w4 + trustDR_int4 + trustDI_int4
voteconfidence_w1 ~ Pos_Emo_w1 + posemoDR_int1 + posemoDI_int1
voteconfidence_w2 ~ Pos_Emo_w2 + posemoDR_int2 + posemoDI_int2
voteconfidence_w3 ~ Pos_Emo_w3 + posemoDR_int3 + posemoDI_int3
voteconfidence_w4 ~ Pos_Emo_w4 + posemoDR_int4 + posemoDI_int4
voteconfidence_w1 ~ Neg_Emo_w1 + negemoDR_int1 + negemoDI_int1
voteconfidence_w2 ~ Neg_Emo_w2 + negemoDR_int2 + negemoDI_int2
voteconfidence_w3 ~ Neg_Emo_w3 + negemoDR_int3 + negemoDI_int3
voteconfidence_w4 ~ Neg_Emo_w4 + negemoDR_int4 + negemoDI_int4
### 4. Optional: Variances and covariances ###
i ~~ s
i ~~ i
s ~~ s
voteconfidence_w1 ~~ voteconfidence_w1
voteconfidence_w2 ~~ voteconfidence_w2
voteconfidence_w3 ~~ voteconfidence_w3
voteconfidence_w4 ~~ voteconfidence_w4
'
fit.lgm_int <- sem(PEL.lgm_int, data = d.wide, missing = "ML", fixed.x = F)
kable(summary(fit.lgm_int, standardized = TRUE, fit.measures = TRUE))
|
|
|
|
|
|
DV: Support for Democratic Norms
Support for Democratic Norms was measured only among partisans (Democrats or Republicans), with four questions (-3 = strongly disagree, +3 = strongly agree):
[Republicans/Democrats] should reduce the number of polling stations in areas that typically support [Democrats/Republicans].
[Republicans/Democrats] should not accept the results of elections if they lose.
[Republican/Democratic]-elected officials should consider ignoring court decisions made by [Democratic/Republican]-appointed judges.
The government should be able to censor media sources that spend more time attacking [Republicans/Democrats] than [Democrats/Republicans].
The scale reached acceptable reliability for both Republicans (\(\alpha\) = .81) and Democrats (\(\alpha\) = .86). The scale was reversed for analysis, such that higher scores indicate lower agreement with these items (and thus higher support for democratic norms).
ggplot(d, aes(x=demNorms)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Support for Democratic Norms") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor),], aes(x=demNorms, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
facet_grid(wave~., labeller = as_labeller(wave_label)) +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Support for Democratic Norms") +
scale_x_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = party_factor,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .3, width =.3, alpha=0.4, size = .5, show.legend = F ) +
stat_summary(geom = "errorbar", color = "black", width = .025, show.legend = F) +
stat_summary( geom = "point", fun = "mean", color = "black", size = 1 , show.legend = F) +
theme_bw() +
scale_fill_manual(values = c("#1696d2","#db2b27")) +
scale_color_manual(values = c("#1696d2","#db2b27")) +
labs(x = "Participant Party ID",
y = "Support for Democratic Norms") +
scale_y_continuous(breaks = seq(-3,3,1))
1. Norms over time: Outcome bias
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = wave,
y = demNorms,
color = party_factor,
group = party_factor)) +
stat_summary(geom = "path", fun = "mean", linetype = "dashed") +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary(geom = "point", fun = "mean") +
# facet_wrap(~party_factor) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Wave",
y = "Support for Democratic Norms") +
coord_cartesian(ylim = c(0,3)) +
scale_y_continuous(breaks = seq(-3,3,.5)) +
scale_x_discrete(labels = c("Wave 1","Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
Summary
Effect of time appears to be positive, but only for Republicans; Democrats’ support for democratic norms are not affected by election outcome.
Models
DN.m1 <- lmer(demNorms ~ (pDem_Rep) * ( wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d[d$party_factor != "Independent",])
summary(DN.m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) + (1 |
## pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22340.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3956 -0.4457 0.1165 0.4819 4.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3847 1.1767
## Residual 0.7083 0.8416
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.37215 0.02695 2430.12021 50.921 < 2e-16 ***
## pDem_Rep -0.15084 0.04994 3431.24505 -3.020 0.002545 **
## wave.lin 0.10375 0.02884 5116.03786 3.598 0.000324 ***
## wave.quad 0.24901 0.04281 4976.07564 5.816 6.39e-09 ***
## wave.cub -0.04354 0.02579 4908.08917 -1.688 0.091385 .
## pDem_Rep:wave.lin 0.26467 0.05789 5127.84809 4.572 4.95e-06 ***
## pDem_Rep:wave.quad 0.25141 0.08592 4988.72133 2.926 0.003446 **
## pDem_Rep:wave.cub -0.17967 0.05181 4923.81176 -3.468 0.000529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp wav.ln wav.qd wav.cb pDm_Rp:wv.l pDm_Rp:wv.q
## pDem_Rep 0.103
## wave.lin 0.143 0.001
## wave.quad -0.037 -0.016 -0.215
## wave.cub -0.047 0.007 -0.114 0.168
## pDm_Rp:wv.l 0.006 0.148 0.108 -0.014 -0.016
## pDm_Rp:wv.q -0.009 -0.035 -0.013 0.103 0.014 -0.212
## pDm_Rp:wv.c 0.002 -0.049 -0.016 0.014 0.099 -0.117 0.166Simple-effects models
DN.m1.d <- lmer(demNorms ~ (pDem_R) * ( wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d[d$party_factor != "Independent",])
summary(DN.m1.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_R) * (wave.lin + wave.quad + wave.cub) + (1 |
## pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22340.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3956 -0.4457 0.1165 0.4819 4.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3847 1.1767
## Residual 0.7083 0.8416
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.44757 0.03479 2808.99466 41.604 < 2e-16 ***
## pDem_R -0.15084 0.04994 3431.24505 -3.020 0.002545 **
## wave.lin -0.02858 0.03859 5117.85367 -0.741 0.458872
## wave.quad 0.12330 0.05743 4984.83490 2.147 0.031856 *
## wave.cub 0.04629 0.03470 4917.15046 1.334 0.182228
## pDem_R:wave.lin 0.26467 0.05789 5127.84809 4.572 4.95e-06 ***
## pDem_R:wave.quad 0.25141 0.08592 4988.72133 2.926 0.003446 **
## pDem_R:wave.cub -0.17967 0.05181 4923.81176 -3.468 0.000529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDem_R wav.ln wav.qd wav.cb pDm_R:wv.l pDm_R:wv.q
## pDem_R -0.638
## wave.lin 0.159 -0.111
## wave.quad -0.027 0.014 -0.223
## wave.cub -0.058 0.042 -0.111 0.170
## pDm_R:wv.ln -0.102 0.148 -0.669 0.148 0.075
## pDm_R:wv.qd 0.019 -0.035 0.149 -0.671 -0.113 -0.212
## pDm_R:wv.cb 0.037 -0.049 0.076 -0.114 -0.673 -0.117 0.166DN.m1.r <- lmer(demNorms ~ (pRep_D) * ( wave.lin + wave.quad + wave.cub) + (1 | pid),
data = d[d$party_factor != "Independent",])
summary(DN.m1.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pRep_D) * (wave.lin + wave.quad + wave.cub) + (1 |
## pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22340.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3956 -0.4457 0.1165 0.4819 4.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3847 1.1767
## Residual 0.7083 0.8416
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.29673 0.03859 2871.40388 33.607 < 2e-16 ***
## pRep_D 0.15084 0.04994 3431.24505 3.020 0.002545 **
## wave.lin 0.23608 0.04301 5125.27378 5.489 4.23e-08 ***
## wave.quad 0.37471 0.06370 4980.45472 5.882 4.31e-09 ***
## wave.cub -0.13338 0.03832 4915.02633 -3.481 0.000504 ***
## pRep_D:wave.lin -0.26467 0.05789 5127.84809 -4.572 4.95e-06 ***
## pRep_D:wave.quad -0.25141 0.08592 4988.72133 -2.926 0.003446 **
## pRep_D:wave.cub 0.17967 0.05181 4923.81176 3.468 0.000529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pRep_D wav.ln wav.qd wav.cb pRp_D:wv.l pRp_D:wv.q
## pRep_D -0.719
## wave.lin 0.135 -0.100
## wave.quad -0.044 0.035 -0.206
## wave.cub -0.039 0.028 -0.119 0.164
## pRp_D:wv.ln -0.100 0.148 -0.745 0.152 0.090
## pRp_D:wv.qd 0.029 -0.035 0.152 -0.744 -0.122 -0.212
## pRp_D:wv.cb 0.030 -0.049 0.089 -0.121 -0.743 -0.117 0.166DN.m1.r.w <- lmer(demNorms ~ (pRep_D) * wave + (1 | pid),
data = d[d$party_factor != "Independent",])
summary(DN.m1.r.w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pRep_D) * wave + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22342.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3956 -0.4457 0.1165 0.4819 4.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3847 1.1767
## Residual 0.7083 0.8416
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.05167 0.04423 4492.55545 23.777 < 2e-16 ***
## pRep_D 0.39094 0.05725 5142.82000 6.829 9.54e-12 ***
## wave2 0.34641 0.03985 5017.19448 8.694 < 2e-16 ***
## wave3 0.33107 0.04185 5056.89171 7.911 3.10e-15 ***
## wave4 0.30277 0.04912 5125.26659 6.164 7.62e-10 ***
## pRep_D:wave2 -0.32663 0.05366 5045.56354 -6.087 1.23e-09 ***
## pRep_D:wave3 -0.27929 0.05622 5081.88610 -4.968 7.00e-07 ***
## pRep_D:wave4 -0.35450 0.06612 5132.52893 -5.361 8.63e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pRep_D wave2 wave3 wave4 pR_D:2 pR_D:3
## pRep_D -0.735
## wave2 -0.427 0.335
## wave3 -0.406 0.318 0.481
## wave4 -0.351 0.277 0.410 0.412
## pRep_D:wav2 0.316 -0.427 -0.747 -0.360 -0.307
## pRep_D:wav3 0.302 -0.408 -0.360 -0.748 -0.309 0.475
## pRep_D:wav4 0.263 -0.354 -0.307 -0.308 -0.746 0.406 0.406DN.m1.r.w2 <- lmer(demNorms ~ (pRep_D) * (wave2_1 + wave2_3 + wave2_4) + (1 | pid),
data = d[d$party_factor != "Independent",])
summary(DN.m1.r.w2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pRep_D) * (wave2_1 + wave2_3 + wave2_4) + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22342.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3956 -0.4457 0.1165 0.4819 4.3734
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3847 1.1767
## Residual 0.7083 0.8416
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.39808 0.04515 4760.10095 30.966 < 2e-16 ***
## pRep_D 0.06432 0.05943 5508.28674 1.082 0.279
## wave2_1 -0.34641 0.03985 5017.19447 -8.694 < 2e-16 ***
## wave2_3 -0.01534 0.04165 4902.96969 -0.368 0.713
## wave2_4 -0.04364 0.04894 5012.02041 -0.892 0.373
## pRep_D:wave2_1 0.32663 0.05366 5045.56353 6.087 1.23e-09 ***
## pRep_D:wave2_3 0.04733 0.05632 4905.75561 0.840 0.401
## pRep_D:wave2_4 -0.02788 0.06613 5000.79093 -0.422 0.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pRep_D wav2_1 wav2_3 wav2_4 pR_D:2_1 pR_D:2_3
## pRep_D -0.724
## wave2_1 -0.464 0.351
## wave2_3 -0.417 0.317 0.473
## wave2_4 -0.360 0.276 0.403 0.407
## pRp_D:wv2_1 0.349 -0.491 -0.747 -0.353 -0.300
## pRp_D:wv2_3 0.310 -0.432 -0.352 -0.742 -0.302 0.478
## pRp_D:wv2_4 0.269 -0.373 -0.299 -0.301 -0.741 0.406 0.4072. Moderator: Gov’t Trust
ggplot(d) +
geom_smooth(aes(x = trustGovt,
y = demNorms),
color = "#69b3a2",
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Support for Democratic Norms") +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = trustGovt,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width = .4, size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,.5))
ggplot(d[!is.na(d$party_factor) & !is.na(d$trustGovt_bins),]) +
geom_smooth(aes(x = trustGovt,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "loess") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Trust in Government",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() 
ggplot(d[!is.na(d$party_factor) & !is.na(d$trustGovt_bins) & d$party_factor != "Independent",],
aes(x = wave,
y = demNorms,
color = party_factor,
group = party_factor)) +
stat_summary(geom = "path", fun = "mean", linetype = "dashed", show.legend = F) +
stat_summary(geom = "errorbar", color = "black", width = .1, show.legend = F) +
stat_summary(geom = "point", fun = "mean") +
facet_wrap(~trustGovt_bins) +
labs(x = "Wave",
y = "Support for Democratic Norms") +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
coord_cartesian(ylim = c(0,3)) +
scale_y_continuous(breaks = seq(-3,3,.5)) +
scale_x_discrete(labels = c("Wave 1","Wave 2", "Wave 3", "Wave 4")) +
theme_bw()
Summary
Trust in the federal government negatively predicts support for democratic norms. True for both Dems and Reps, but stronger for Reps than for Dems.
Gov’t trust also moderates the party x linear time interaction effect on support for democratic norms. Low trust, party x time interaction strong; attenuated (but still significant) for those high in gov’t trust.
Models
DN.m2 <- lmer(demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(DN.m2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.c +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.379e+00 2.741e-02 2.548e+03 50.323
## pDem_Rep -1.827e-01 5.079e-02 3.601e+03 -3.597
## wave.lin 7.949e-02 3.310e-02 5.450e+03 2.401
## wave.quad 2.009e-01 4.658e-02 4.922e+03 4.314
## wave.cub -4.363e-02 2.775e-02 4.852e+03 -1.572
## trustGovt.c -6.462e-02 1.624e-02 7.020e+03 -3.978
## pDem_Rep:wave.lin 3.659e-01 6.661e-02 5.498e+03 5.493
## pDem_Rep:wave.quad 2.857e-01 9.351e-02 4.936e+03 3.055
## pDem_Rep:wave.cub -2.039e-01 5.576e-02 4.868e+03 -3.656
## pDem_Rep:trustGovt.c -3.276e-02 3.177e-02 6.956e+03 -1.031
## wave.lin:trustGovt.c 4.796e-02 3.086e-02 5.536e+03 1.554
## wave.quad:trustGovt.c 1.348e-03 4.315e-02 4.984e+03 0.031
## wave.cub:trustGovt.c -4.283e-02 2.623e-02 4.924e+03 -1.633
## pDem_Rep:wave.lin:trustGovt.c -1.363e-01 6.169e-02 5.516e+03 -2.210
## pDem_Rep:wave.quad:trustGovt.c -1.199e-01 8.666e-02 4.999e+03 -1.383
## pDem_Rep:wave.cub:trustGovt.c 4.132e-03 5.267e-02 4.939e+03 0.078
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.000326 ***
## wave.lin 0.016362 *
## wave.quad 1.64e-05 ***
## wave.cub 0.115999
## trustGovt.c 7.02e-05 ***
## pDem_Rep:wave.lin 4.13e-08 ***
## pDem_Rep:wave.quad 0.002261 **
## pDem_Rep:wave.cub 0.000259 ***
## pDem_Rep:trustGovt.c 0.302548
## wave.lin:trustGovt.c 0.120173
## wave.quad:trustGovt.c 0.975080
## wave.cub:trustGovt.c 0.102522
## pDem_Rep:wave.lin:trustGovt.c 0.027170 *
## pDem_Rep:wave.quad:trustGovt.c 0.166704
## pDem_Rep:wave.cub:trustGovt.c 0.937479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
DN.m2.d <- lmer(demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(DN.m2.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.c +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.471e+00 3.590e-02 3.082e+03 40.963 < 2e-16
## pDem_R -1.827e-01 5.079e-02 3.601e+03 -3.597 0.000326
## wave.lin -1.035e-01 4.886e-02 5.670e+03 -2.118 0.034245
## wave.quad 5.810e-02 6.619e-02 4.935e+03 0.878 0.380143
## wave.cub 5.829e-02 3.902e-02 4.883e+03 1.494 0.135278
## trustGovt.c -4.825e-02 2.078e-02 6.850e+03 -2.322 0.020251
## pDem_R:wave.lin 3.659e-01 6.661e-02 5.498e+03 5.493 4.13e-08
## pDem_R:wave.quad 2.857e-01 9.351e-02 4.936e+03 3.055 0.002261
## pDem_R:wave.cub -2.039e-01 5.576e-02 4.868e+03 -3.656 0.000259
## pDem_R:trustGovt.c -3.276e-02 3.177e-02 6.956e+03 -1.031 0.302548
## wave.lin:trustGovt.c 1.161e-01 4.191e-02 5.536e+03 2.771 0.005611
## wave.quad:trustGovt.c 6.128e-02 5.837e-02 4.985e+03 1.050 0.293822
## wave.cub:trustGovt.c -4.490e-02 3.561e-02 4.952e+03 -1.261 0.207350
## pDem_R:wave.lin:trustGovt.c -1.363e-01 6.169e-02 5.516e+03 -2.210 0.027170
## pDem_R:wave.quad:trustGovt.c -1.199e-01 8.666e-02 4.999e+03 -1.383 0.166704
## pDem_R:wave.cub:trustGovt.c 4.132e-03 5.267e-02 4.939e+03 0.078 0.937479
##
## (Intercept) ***
## pDem_R ***
## wave.lin *
## wave.quad
## wave.cub
## trustGovt.c *
## pDem_R:wave.lin ***
## pDem_R:wave.quad **
## pDem_R:wave.cub ***
## pDem_R:trustGovt.c
## wave.lin:trustGovt.c **
## wave.quad:trustGovt.c
## wave.cub:trustGovt.c
## pDem_R:wave.lin:trustGovt.c *
## pDem_R:wave.quad:trustGovt.c
## pDem_R:wave.cub:trustGovt.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.r <- lmer(demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.c + (1 | pid),
data = d)
summary(DN.m2.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.c +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.288e+00 3.877e-02 2.903e+03 33.219 < 2e-16
## pRep_D 1.827e-01 5.079e-02 3.601e+03 3.597 0.000326
## wave.lin 2.624e-01 4.498e-02 5.240e+03 5.835 5.69e-09
## wave.quad 3.438e-01 6.580e-02 4.923e+03 5.225 1.82e-07
## wave.cub -1.456e-01 3.966e-02 4.838e+03 -3.670 0.000245
## trustGovt.c -8.100e-02 2.451e-02 7.049e+03 -3.305 0.000955
## pRep_D:wave.lin -3.659e-01 6.661e-02 5.498e+03 -5.493 4.13e-08
## pRep_D:wave.quad -2.857e-01 9.351e-02 4.936e+03 -3.055 0.002261
## pRep_D:wave.cub 2.039e-01 5.576e-02 4.868e+03 3.656 0.000259
## pRep_D:trustGovt.c 3.276e-02 3.177e-02 6.956e+03 1.031 0.302548
## wave.lin:trustGovt.c -2.020e-02 4.529e-02 5.517e+03 -0.446 0.655610
## wave.quad:trustGovt.c -5.858e-02 6.382e-02 4.997e+03 -0.918 0.358681
## wave.cub:trustGovt.c -4.077e-02 3.867e-02 4.914e+03 -1.054 0.291838
## pRep_D:wave.lin:trustGovt.c 1.363e-01 6.169e-02 5.516e+03 2.210 0.027170
## pRep_D:wave.quad:trustGovt.c 1.199e-01 8.666e-02 4.999e+03 1.383 0.166704
## pRep_D:wave.cub:trustGovt.c -4.132e-03 5.267e-02 4.939e+03 -0.078 0.937479
##
## (Intercept) ***
## pRep_D ***
## wave.lin ***
## wave.quad ***
## wave.cub ***
## trustGovt.c ***
## pRep_D:wave.lin ***
## pRep_D:wave.quad **
## pRep_D:wave.cub ***
## pRep_D:trustGovt.c
## wave.lin:trustGovt.c
## wave.quad:trustGovt.c
## wave.cub:trustGovt.c
## pRep_D:wave.lin:trustGovt.c *
## pRep_D:wave.quad:trustGovt.c
## pRep_D:wave.cub:trustGovt.c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.lo <- lmer(demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.lo + (1 | pid),
data = d)
summary(DN.m2.lo)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.lo +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.451e+00 3.224e-02 3.690e+03 44.991
## pDem_Rep -1.465e-01 6.142e-02 4.741e+03 -2.385
## wave.lin 2.651e-02 4.547e-02 5.647e+03 0.583
## wave.quad 1.994e-01 6.350e-02 4.946e+03 3.141
## wave.cub 3.686e-03 3.892e-02 4.888e+03 0.095
## trustGovt.lo -6.462e-02 1.624e-02 7.020e+03 -3.978
## pDem_Rep:wave.lin 5.165e-01 9.090e-02 5.635e+03 5.682
## pDem_Rep:wave.quad 4.181e-01 1.274e-01 4.957e+03 3.282
## pDem_Rep:wave.cub -2.084e-01 7.813e-02 4.902e+03 -2.668
## pDem_Rep:trustGovt.lo -3.276e-02 3.177e-02 6.956e+03 -1.031
## wave.lin:trustGovt.lo 4.796e-02 3.086e-02 5.536e+03 1.554
## wave.quad:trustGovt.lo 1.348e-03 4.315e-02 4.984e+03 0.031
## wave.cub:trustGovt.lo -4.283e-02 2.623e-02 4.924e+03 -1.633
## pDem_Rep:wave.lin:trustGovt.lo -1.363e-01 6.169e-02 5.516e+03 -2.210
## pDem_Rep:wave.quad:trustGovt.lo -1.199e-01 8.666e-02 4.999e+03 -1.383
## pDem_Rep:wave.cub:trustGovt.lo 4.132e-03 5.267e-02 4.939e+03 0.078
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.01712 *
## wave.lin 0.55993
## wave.quad 0.00169 **
## wave.cub 0.92455
## trustGovt.lo 7.02e-05 ***
## pDem_Rep:wave.lin 1.40e-08 ***
## pDem_Rep:wave.quad 0.00104 **
## pDem_Rep:wave.cub 0.00767 **
## pDem_Rep:trustGovt.lo 0.30255
## wave.lin:trustGovt.lo 0.12017
## wave.quad:trustGovt.lo 0.97508
## wave.cub:trustGovt.lo 0.10252
## pDem_Rep:wave.lin:trustGovt.lo 0.02717 *
## pDem_Rep:wave.quad:trustGovt.lo 0.16670
## pDem_Rep:wave.cub:trustGovt.lo 0.93748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.lo.d <- lmer(demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.lo + (1 | pid),
data = d)
summary(DN.m2.lo.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.lo +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.524e+00 4.356e-02 4.500e+03 34.980 < 2e-16
## pDem_R -1.465e-01 6.142e-02 4.741e+03 -2.385 0.017121
## wave.lin -2.317e-01 6.557e-02 6.064e+03 -3.535 0.000412
## wave.quad -9.594e-03 9.045e-02 4.965e+03 -0.106 0.915527
## wave.cub 1.079e-01 5.788e-02 4.914e+03 1.864 0.062383
## trustGovt.lo -4.825e-02 2.078e-02 6.850e+03 -2.322 0.020251
## pDem_R:wave.lin 5.165e-01 9.090e-02 5.635e+03 5.682 1.4e-08
## pDem_R:wave.quad 4.181e-01 1.274e-01 4.957e+03 3.282 0.001037
## pDem_R:wave.cub -2.084e-01 7.813e-02 4.902e+03 -2.668 0.007666
## pDem_R:trustGovt.lo -3.276e-02 3.177e-02 6.956e+03 -1.031 0.302548
## wave.lin:trustGovt.lo 1.161e-01 4.191e-02 5.536e+03 2.771 0.005611
## wave.quad:trustGovt.lo 6.128e-02 5.837e-02 4.985e+03 1.050 0.293822
## wave.cub:trustGovt.lo -4.490e-02 3.561e-02 4.952e+03 -1.261 0.207350
## pDem_R:wave.lin:trustGovt.lo -1.363e-01 6.169e-02 5.516e+03 -2.210 0.027170
## pDem_R:wave.quad:trustGovt.lo -1.199e-01 8.666e-02 4.999e+03 -1.383 0.166704
## pDem_R:wave.cub:trustGovt.lo 4.132e-03 5.267e-02 4.939e+03 0.078 0.937479
##
## (Intercept) ***
## pDem_R *
## wave.lin ***
## wave.quad
## wave.cub .
## trustGovt.lo *
## pDem_R:wave.lin ***
## pDem_R:wave.quad **
## pDem_R:wave.cub **
## pDem_R:trustGovt.lo
## wave.lin:trustGovt.lo **
## wave.quad:trustGovt.lo
## wave.cub:trustGovt.lo
## pDem_R:wave.lin:trustGovt.lo *
## pDem_R:wave.quad:trustGovt.lo
## pDem_R:wave.cub:trustGovt.lo
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.lo.r <- lmer(demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.lo + (1 | pid),
data = d)
summary(DN.m2.lo.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.lo +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.377e+00 4.547e-02 3.874e+03 30.293 < 2e-16
## pRep_D 1.465e-01 6.142e-02 4.741e+03 2.385 0.017121
## wave.lin 2.848e-01 6.299e-02 5.164e+03 4.521 6.30e-06
## wave.quad 4.085e-01 8.942e-02 4.938e+03 4.568 5.04e-06
## wave.cub -1.005e-01 5.226e-02 4.871e+03 -1.924 0.054474
## trustGovt.lo -8.100e-02 2.451e-02 7.049e+03 -3.305 0.000955
## pRep_D:wave.lin -5.165e-01 9.090e-02 5.635e+03 -5.682 1.40e-08
## pRep_D:wave.quad -4.181e-01 1.274e-01 4.957e+03 -3.282 0.001037
## pRep_D:wave.cub 2.084e-01 7.813e-02 4.902e+03 2.668 0.007666
## pRep_D:trustGovt.lo 3.276e-02 3.177e-02 6.956e+03 1.031 0.302548
## wave.lin:trustGovt.lo -2.020e-02 4.529e-02 5.517e+03 -0.446 0.655610
## wave.quad:trustGovt.lo -5.858e-02 6.382e-02 4.997e+03 -0.918 0.358681
## wave.cub:trustGovt.lo -4.077e-02 3.867e-02 4.914e+03 -1.054 0.291838
## pRep_D:wave.lin:trustGovt.lo 1.363e-01 6.169e-02 5.516e+03 2.210 0.027170
## pRep_D:wave.quad:trustGovt.lo 1.199e-01 8.666e-02 4.999e+03 1.383 0.166704
## pRep_D:wave.cub:trustGovt.lo -4.132e-03 5.267e-02 4.939e+03 -0.078 0.937479
##
## (Intercept) ***
## pRep_D *
## wave.lin ***
## wave.quad ***
## wave.cub .
## trustGovt.lo ***
## pRep_D:wave.lin ***
## pRep_D:wave.quad **
## pRep_D:wave.cub **
## pRep_D:trustGovt.lo
## wave.lin:trustGovt.lo
## wave.quad:trustGovt.lo
## wave.cub:trustGovt.lo
## pRep_D:wave.lin:trustGovt.lo *
## pRep_D:wave.quad:trustGovt.lo
## pRep_D:wave.cub:trustGovt.lo
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.hi <- lmer(demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.hi + (1 | pid),
data = d)
summary(DN.m2.hi)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * trustGovt.hi +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.308e+00 3.327e-02 4.054e+03 39.311
## pDem_Rep -2.189e-01 6.204e-02 5.436e+03 -3.527
## wave.lin 1.325e-01 4.947e-02 5.365e+03 2.678
## wave.quad 2.024e-01 6.966e-02 4.960e+03 2.906
## wave.cub -9.095e-02 4.129e-02 4.891e+03 -2.203
## trustGovt.hi -6.462e-02 1.624e-02 7.020e+03 -3.978
## pDem_Rep:wave.lin 2.153e-01 9.950e-02 5.400e+03 2.164
## pDem_Rep:wave.quad 1.533e-01 1.400e-01 4.978e+03 1.095
## pDem_Rep:wave.cub -1.993e-01 8.298e-02 4.908e+03 -2.402
## pDem_Rep:trustGovt.hi -3.276e-02 3.177e-02 6.956e+03 -1.031
## wave.lin:trustGovt.hi 4.796e-02 3.086e-02 5.536e+03 1.554
## wave.quad:trustGovt.hi 1.348e-03 4.315e-02 4.984e+03 0.031
## wave.cub:trustGovt.hi -4.283e-02 2.623e-02 4.924e+03 -1.633
## pDem_Rep:wave.lin:trustGovt.hi -1.363e-01 6.169e-02 5.516e+03 -2.210
## pDem_Rep:wave.quad:trustGovt.hi -1.199e-01 8.666e-02 4.999e+03 -1.383
## pDem_Rep:wave.cub:trustGovt.hi 4.132e-03 5.267e-02 4.939e+03 0.078
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.000423 ***
## wave.lin 0.007437 **
## wave.quad 0.003678 **
## wave.cub 0.027674 *
## trustGovt.hi 7.02e-05 ***
## pDem_Rep:wave.lin 0.030501 *
## pDem_Rep:wave.quad 0.273536
## pDem_Rep:wave.cub 0.016363 *
## pDem_Rep:trustGovt.hi 0.302548
## wave.lin:trustGovt.hi 0.120173
## wave.quad:trustGovt.hi 0.975080
## wave.cub:trustGovt.hi 0.102522
## pDem_Rep:wave.lin:trustGovt.hi 0.027170 *
## pDem_Rep:wave.quad:trustGovt.hi 0.166704
## pDem_Rep:wave.cub:trustGovt.hi 0.937479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.hi.d <- lmer(demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.hi + (1 | pid),
data = d)
summary(DN.m2.hi.d)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pDem_R * (wave.lin + wave.quad + wave.cub) * trustGovt.hi +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.417e+00 4.163e-02 4.496e+03 34.042 < 2e-16
## pDem_R -2.189e-01 6.204e-02 5.436e+03 -3.527 0.000423
## wave.lin 2.481e-02 6.901e-02 5.179e+03 0.359 0.719262
## wave.quad 1.258e-01 9.432e-02 4.954e+03 1.334 0.182386
## wave.cub 8.691e-03 5.281e-02 4.922e+03 0.165 0.869298
## trustGovt.hi -4.825e-02 2.078e-02 6.850e+03 -2.322 0.020251
## pDem_R:wave.lin 2.153e-01 9.950e-02 5.400e+03 2.164 0.030501
## pDem_R:wave.quad 1.533e-01 1.400e-01 4.978e+03 1.095 0.273536
## pDem_R:wave.cub -1.993e-01 8.298e-02 4.908e+03 -2.402 0.016363
## pDem_R:trustGovt.hi -3.276e-02 3.177e-02 6.956e+03 -1.031 0.302548
## wave.lin:trustGovt.hi 1.161e-01 4.191e-02 5.536e+03 2.771 0.005611
## wave.quad:trustGovt.hi 6.128e-02 5.837e-02 4.985e+03 1.050 0.293822
## wave.cub:trustGovt.hi -4.490e-02 3.561e-02 4.952e+03 -1.261 0.207350
## pDem_R:wave.lin:trustGovt.hi -1.363e-01 6.169e-02 5.516e+03 -2.210 0.027170
## pDem_R:wave.quad:trustGovt.hi -1.199e-01 8.666e-02 4.999e+03 -1.383 0.166704
## pDem_R:wave.cub:trustGovt.hi 4.132e-03 5.267e-02 4.939e+03 0.078 0.937479
##
## (Intercept) ***
## pDem_R ***
## wave.lin
## wave.quad
## wave.cub
## trustGovt.hi *
## pDem_R:wave.lin *
## pDem_R:wave.quad
## pDem_R:wave.cub *
## pDem_R:trustGovt.hi
## wave.lin:trustGovt.hi **
## wave.quad:trustGovt.hi
## wave.cub:trustGovt.hi
## pDem_R:wave.lin:trustGovt.hi *
## pDem_R:wave.quad:trustGovt.hi
## pDem_R:wave.cub:trustGovt.hi
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m2.hi.r <- lmer(demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.hi + (1 | pid),
data = d)
summary(DN.m2.hi.r)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ pRep_D * (wave.lin + wave.quad + wave.cub) * trustGovt.hi +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3339 -0.4451 0.1075 0.4807 4.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3696 1.1703
## Residual 0.7065 0.8406
## Number of obs: 7082, groups: pid, 2352
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.198e+00 4.904e-02 4.802e+03 24.437 < 2e-16
## pRep_D 2.189e-01 6.204e-02 5.436e+03 3.527 0.000423
## wave.lin 2.401e-01 7.130e-02 5.572e+03 3.368 0.000762
## wave.quad 2.791e-01 1.030e-01 4.981e+03 2.710 0.006750
## wave.cub -1.906e-01 6.375e-02 4.884e+03 -2.990 0.002807
## trustGovt.hi -8.100e-02 2.451e-02 7.049e+03 -3.305 0.000955
## pRep_D:wave.lin -2.153e-01 9.950e-02 5.400e+03 -2.164 0.030501
## pRep_D:wave.quad -1.533e-01 1.400e-01 4.978e+03 -1.095 0.273536
## pRep_D:wave.cub 1.993e-01 8.298e-02 4.908e+03 2.402 0.016363
## pRep_D:trustGovt.hi 3.276e-02 3.177e-02 6.956e+03 1.031 0.302548
## wave.lin:trustGovt.hi -2.020e-02 4.529e-02 5.517e+03 -0.446 0.655610
## wave.quad:trustGovt.hi -5.858e-02 6.382e-02 4.997e+03 -0.918 0.358681
## wave.cub:trustGovt.hi -4.077e-02 3.867e-02 4.914e+03 -1.054 0.291838
## pRep_D:wave.lin:trustGovt.hi 1.363e-01 6.169e-02 5.516e+03 2.210 0.027170
## pRep_D:wave.quad:trustGovt.hi 1.199e-01 8.666e-02 4.999e+03 1.383 0.166704
## pRep_D:wave.cub:trustGovt.hi -4.132e-03 5.267e-02 4.939e+03 -0.078 0.937479
##
## (Intercept) ***
## pRep_D ***
## wave.lin ***
## wave.quad **
## wave.cub **
## trustGovt.hi ***
## pRep_D:wave.lin *
## pRep_D:wave.quad
## pRep_D:wave.cub *
## pRep_D:trustGovt.hi
## wave.lin:trustGovt.hi
## wave.quad:trustGovt.hi
## wave.cub:trustGovt.hi
## pRep_D:wave.lin:trustGovt.hi *
## pRep_D:wave.quad:trustGovt.hi
## pRep_D:wave.cub:trustGovt.hi
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 13. Moderator: GCB-5
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_smooth(aes(x = GCB,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Generic Conspiracist Beliefs",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_smooth(aes(x = GCB,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Generic Conspiracist Beliefs",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(-2,2,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = GCB,
y = demNorms,
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 = "Generic Conspiracist Beliefs",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) +
scale_x_continuous(breaks = seq(-2,2,1)) +
scale_y_continuous(breaks = seq(-3,3,1))
Mean and SD of GCB by party ID
| x | |
|---|---|
| Democrat | -0.12 |
| Independent | 0.14 |
| Republican | 0.34 |
## Democrat Independent Republican
## 0.99 0.88 0.89Summary
GCB negatively predicts support for democratic norms; this relationship does not change over linear time (Wave 1 to Wave 4).
GCB has a stronger negative effect for Democrats than for Republicans, a relationship that is not sensitive to time/election outcome.
Models
DN.m3 <- lmer(demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * GCB.c + (1|pid),
data = d)
summary(DN.m3) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ pDem_Rep * (wave.lin + wave.quad + wave.cub) * GCB.c +
## (1 | pid)
## Data: d
##
## REML criterion at convergence: 22064.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3234 -0.4507 0.0840 0.4845 4.3357
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2011 1.0959
## Residual 0.7057 0.8401
## Number of obs: 7170, groups: pid, 2365
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.359e+00 2.619e-02 2.463e+03 51.896 < 2e-16 ***
## pDem_Rep -9.385e-03 4.921e-02 3.279e+03 -0.191 0.84875
## wave.lin 8.593e-02 2.965e-02 5.151e+03 2.898 0.00377 **
## wave.quad 2.445e-01 4.407e-02 4.998e+03 5.548 3.03e-08 ***
## wave.cub -4.189e-02 2.656e-02 4.923e+03 -1.577 0.11481
## GCB.c -4.203e-01 2.736e-02 2.468e+03 -15.360 < 2e-16 ***
## pDem_Rep:wave.lin 2.468e-01 5.951e-02 5.162e+03 4.147 3.42e-05 ***
## pDem_Rep:wave.quad 2.151e-01 8.843e-02 5.012e+03 2.432 0.01504 *
## pDem_Rep:wave.cub -1.526e-01 5.334e-02 4.940e+03 -2.861 0.00424 **
## pDem_Rep:GCB.c 2.143e-01 5.142e-02 3.283e+03 4.168 3.16e-05 ***
## wave.lin:GCB.c 4.376e-02 3.104e-02 5.148e+03 1.410 0.15861
## wave.quad:GCB.c 7.724e-02 4.608e-02 4.995e+03 1.676 0.09373 .
## wave.cub:GCB.c -5.941e-02 2.771e-02 4.916e+03 -2.144 0.03206 *
## pDem_Rep:wave.lin:GCB.c 1.018e-01 6.230e-02 5.162e+03 1.634 0.10223
## pDem_Rep:wave.quad:GCB.c 1.905e-03 9.242e-02 5.008e+03 0.021 0.98356
## pDem_Rep:wave.cub:GCB.c 9.164e-03 5.566e-02 4.934e+03 0.165 0.86924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple effect models
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ pDem_Rep * wave * GCB.c + (1 | pid)
## Data: d
##
## REML criterion at convergence: 22068.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3234 -0.4507 0.0840 0.4845 4.3357
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2011 1.0959
## Residual 0.7057 0.8401
## Number of obs: 7170, groups: pid, 2365
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.24478 0.02975 3987.93319 41.840 < 2e-16 ***
## pDem_Rep -0.22472 0.05693 5138.23804 -3.947 8.01e-05 ***
## wave2 0.17516 0.02743 5042.60970 6.387 1.84e-10 ***
## wave3 0.17624 0.02877 5089.96643 6.125 9.73e-10 ***
## wave4 0.10687 0.03382 5149.48089 3.160 0.001586 **
## GCB.c -0.47631 0.03105 3987.46557 -15.341 < 2e-16 ***
## pDem_Rep:wave2 0.28371 0.05517 5066.94484 5.142 2.81e-07 ***
## pDem_Rep:wave3 0.25451 0.05780 5107.15131 4.403 1.09e-05 ***
## pDem_Rep:wave4 0.32312 0.06794 5165.45966 4.756 2.03e-06 ***
## pDem_Rep:GCB.c 0.16521 0.05937 5146.09177 2.783 0.005407 **
## wave2:GCB.c 0.09412 0.02858 5029.34251 3.293 0.000996 ***
## wave3:GCB.c 0.05659 0.02998 5082.92372 1.888 0.059099 .
## wave4:GCB.c 0.07347 0.03543 5147.43975 2.074 0.038165 *
## pDem_Rep:wave2:GCB.c 0.01953 0.05756 5059.72341 0.339 0.734360
## pDem_Rep:wave3:GCB.c 0.07961 0.06019 5100.84089 1.323 0.186038
## pDem_Rep:wave4:GCB.c 0.09724 0.07121 5166.92263 1.366 0.172154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14. Expanded model: trust, GCB, emotions
ggplot(d[d$party_factor != "Independent" & !is.na(d$party_factor),],
aes( x = trustGovt,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(negemo_bins~wave) +
labs( x = "Trust in Government",
y = "Support for Democratic Norms" ) +
scale_fill_manual("Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1)) 
ggplot(d[d$party_factor != "Independent" & !is.na(d$party_factor),],
aes( x = trustGovt,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width = .2, size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F, fullrange = T) +
facet_grid(posemo_bins~wave) +
labs( x = "Trust in Government",
y = "Support for Democratic Norms" ) +
scale_fill_manual("Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1)) 
ggplot(d[!is.na(d$party_factor),],
aes( x = trustGovt,
y = voteconfidence,
fill = party_factor,
color = party_factor)) +
geom_jitter(height = .2, width = .2, size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F, fullrange = T) +
facet_grid(posemo_bins~wave) +
labs( x = "Trust in Government",
y = "Perceived Election Legitimacy" ) +
scale_fill_manual("Party ID",
values = c("#1696d2", "black","#db2b27")) +
scale_color_manual("Party ID",
values = c("#1696d2", "black","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(1,5)) +
scale_y_continuous(breaks = seq(1,5,1)) 
Models
DN.m4 <- lmer(demNorms ~ pDem_Rep * wave * GCB.c * (Pos_Emo.c + Neg_Emo.c) * trustGovt.c + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ pDem_Rep * wave * GCB.c * (Pos_Emo.c + Neg_Emo.c) *
## trustGovt.c + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 21848
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3569 -0.4548 0.0813 0.4928 4.4156
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.069 1.0341
## Residual 0.697 0.8349
## Number of obs: 7080, groups: pid, 2350
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.267e+00 3.264e-02 5.118e+03
## pDem_Rep -3.641e-01 6.332e-02 6.021e+03
## wave2 1.520e-01 4.136e-02 5.180e+03
## wave3 1.156e-01 4.219e-02 5.344e+03
## wave4 1.178e-01 4.702e-02 5.317e+03
## GCB.c -4.614e-01 3.576e-02 5.499e+03
## Pos_Emo.c -4.774e-02 2.325e-02 6.107e+03
## Neg_Emo.c -9.579e-02 2.348e-02 6.165e+03
## trustGovt.c -1.373e-01 2.744e-02 6.655e+03
## pDem_Rep:wave2 1.829e-01 8.326e-02 5.221e+03
## pDem_Rep:wave3 2.120e-01 8.470e-02 5.364e+03
## pDem_Rep:wave4 4.153e-01 9.450e-02 5.342e+03
## pDem_Rep:GCB.c 3.465e-02 6.930e-02 6.362e+03
## wave2:GCB.c 1.614e-01 4.582e-02 5.170e+03
## wave3:GCB.c 6.416e-02 4.699e-02 5.361e+03
## wave4:GCB.c 8.699e-02 5.299e-02 5.345e+03
## pDem_Rep:Pos_Emo.c -1.509e-02 4.635e-02 6.065e+03
## pDem_Rep:Neg_Emo.c -9.823e-02 4.680e-02 6.120e+03
## wave2:Pos_Emo.c -7.418e-02 3.142e-02 5.313e+03
## wave3:Pos_Emo.c 1.012e-02 3.130e-02 5.370e+03
## wave4:Pos_Emo.c -9.471e-03 3.674e-02 5.350e+03
## wave2:Neg_Emo.c -1.294e-01 3.287e-02 5.319e+03
## wave3:Neg_Emo.c -5.553e-02 3.441e-02 5.337e+03
## wave4:Neg_Emo.c -3.642e-02 3.908e-02 5.277e+03
## GCB.c:Pos_Emo.c -4.810e-03 2.371e-02 6.191e+03
## GCB.c:Neg_Emo.c -5.229e-02 2.351e-02 6.322e+03
## pDem_Rep:trustGovt.c 7.780e-02 5.455e-02 6.586e+03
## wave2:trustGovt.c 7.212e-02 3.874e-02 5.095e+03
## wave3:trustGovt.c -1.092e-04 4.280e-02 5.336e+03
## wave4:trustGovt.c 5.994e-02 4.706e-02 5.348e+03
## GCB.c:trustGovt.c -1.290e-01 2.703e-02 6.663e+03
## Pos_Emo.c:trustGovt.c -1.815e-02 1.989e-02 5.933e+03
## Neg_Emo.c:trustGovt.c -1.772e-02 2.070e-02 5.936e+03
## pDem_Rep:wave2:GCB.c 9.547e-02 9.238e-02 5.228e+03
## pDem_Rep:wave3:GCB.c 2.035e-01 9.450e-02 5.417e+03
## pDem_Rep:wave4:GCB.c 2.032e-01 1.071e-01 5.429e+03
## pDem_Rep:wave2:Pos_Emo.c 9.438e-02 6.309e-02 5.334e+03
## pDem_Rep:wave3:Pos_Emo.c 2.926e-02 6.272e-02 5.379e+03
## pDem_Rep:wave4:Pos_Emo.c -7.556e-03 7.371e-02 5.364e+03
## pDem_Rep:wave2:Neg_Emo.c 9.621e-02 6.597e-02 5.340e+03
## pDem_Rep:wave3:Neg_Emo.c 3.923e-02 6.903e-02 5.350e+03
## pDem_Rep:wave4:Neg_Emo.c 1.983e-01 7.840e-02 5.291e+03
## pDem_Rep:GCB.c:Pos_Emo.c 4.523e-02 4.719e-02 6.125e+03
## pDem_Rep:GCB.c:Neg_Emo.c -5.954e-02 4.697e-02 6.303e+03
## wave2:GCB.c:Pos_Emo.c -2.414e-02 3.225e-02 5.281e+03
## wave3:GCB.c:Pos_Emo.c -1.579e-02 3.095e-02 5.305e+03
## wave4:GCB.c:Pos_Emo.c 8.123e-02 3.670e-02 5.348e+03
## wave2:GCB.c:Neg_Emo.c 1.619e-02 3.404e-02 5.427e+03
## wave3:GCB.c:Neg_Emo.c 2.047e-03 3.496e-02 5.395e+03
## wave4:GCB.c:Neg_Emo.c 4.455e-02 4.307e-02 5.357e+03
## pDem_Rep:wave2:trustGovt.c 2.201e-02 7.786e-02 5.123e+03
## pDem_Rep:wave3:trustGovt.c -1.566e-01 8.552e-02 5.323e+03
## pDem_Rep:wave4:trustGovt.c -2.086e-01 9.399e-02 5.331e+03
## pDem_Rep:GCB.c:trustGovt.c -3.569e-03 5.357e-02 6.546e+03
## wave2:GCB.c:trustGovt.c -5.324e-03 3.879e-02 5.096e+03
## wave3:GCB.c:trustGovt.c 4.916e-02 4.137e-02 5.268e+03
## wave4:GCB.c:trustGovt.c 5.567e-02 4.796e-02 5.263e+03
## pDem_Rep:Pos_Emo.c:trustGovt.c 3.942e-03 3.975e-02 5.923e+03
## pDem_Rep:Neg_Emo.c:trustGovt.c 3.138e-03 4.138e-02 5.929e+03
## wave2:Pos_Emo.c:trustGovt.c 6.576e-03 2.691e-02 5.266e+03
## wave3:Pos_Emo.c:trustGovt.c 1.855e-02 2.807e-02 5.356e+03
## wave4:Pos_Emo.c:trustGovt.c 8.590e-03 3.041e-02 5.330e+03
## wave2:Neg_Emo.c:trustGovt.c 5.222e-02 3.089e-02 5.207e+03
## wave3:Neg_Emo.c:trustGovt.c 1.373e-03 3.193e-02 5.298e+03
## wave4:Neg_Emo.c:trustGovt.c -4.603e-02 3.591e-02 5.336e+03
## GCB.c:Pos_Emo.c:trustGovt.c -1.341e-02 1.799e-02 6.095e+03
## GCB.c:Neg_Emo.c:trustGovt.c -5.369e-02 1.792e-02 6.359e+03
## pDem_Rep:wave2:GCB.c:Pos_Emo.c -5.921e-03 6.499e-02 5.316e+03
## pDem_Rep:wave3:GCB.c:Pos_Emo.c -4.860e-02 6.204e-02 5.321e+03
## pDem_Rep:wave4:GCB.c:Pos_Emo.c -7.433e-02 7.392e-02 5.384e+03
## pDem_Rep:wave2:GCB.c:Neg_Emo.c 1.044e-01 6.817e-02 5.434e+03
## pDem_Rep:wave3:GCB.c:Neg_Emo.c 7.369e-02 6.987e-02 5.385e+03
## pDem_Rep:wave4:GCB.c:Neg_Emo.c 2.482e-01 8.605e-02 5.348e+03
## pDem_Rep:wave2:GCB.c:trustGovt.c -4.359e-02 7.820e-02 5.139e+03
## pDem_Rep:wave3:GCB.c:trustGovt.c 4.311e-02 8.289e-02 5.276e+03
## pDem_Rep:wave4:GCB.c:trustGovt.c 4.167e-02 9.638e-02 5.293e+03
## pDem_Rep:wave2:Pos_Emo.c:trustGovt.c -1.416e-02 5.401e-02 5.288e+03
## pDem_Rep:wave3:Pos_Emo.c:trustGovt.c 4.551e-02 5.622e-02 5.365e+03
## pDem_Rep:wave4:Pos_Emo.c:trustGovt.c -1.995e-02 6.064e-02 5.310e+03
## pDem_Rep:wave2:Neg_Emo.c:trustGovt.c -1.837e-02 6.189e-02 5.217e+03
## pDem_Rep:wave3:Neg_Emo.c:trustGovt.c -3.120e-02 6.383e-02 5.296e+03
## pDem_Rep:wave4:Neg_Emo.c:trustGovt.c -3.100e-03 7.151e-02 5.314e+03
## pDem_Rep:GCB.c:Pos_Emo.c:trustGovt.c 1.027e-02 3.590e-02 6.064e+03
## pDem_Rep:GCB.c:Neg_Emo.c:trustGovt.c -5.586e-02 3.578e-02 6.342e+03
## wave2:GCB.c:Pos_Emo.c:trustGovt.c 4.681e-03 2.386e-02 5.255e+03
## wave3:GCB.c:Pos_Emo.c:trustGovt.c -7.010e-03 2.495e-02 5.352e+03
## wave4:GCB.c:Pos_Emo.c:trustGovt.c 2.228e-02 2.796e-02 5.372e+03
## wave2:GCB.c:Neg_Emo.c:trustGovt.c -3.567e-02 2.601e-02 5.243e+03
## wave3:GCB.c:Neg_Emo.c:trustGovt.c -2.569e-02 2.704e-02 5.286e+03
## wave4:GCB.c:Neg_Emo.c:trustGovt.c -8.920e-03 3.682e-02 5.360e+03
## pDem_Rep:wave2:GCB.c:Pos_Emo.c:trustGovt.c -2.167e-02 4.810e-02 5.297e+03
## pDem_Rep:wave3:GCB.c:Pos_Emo.c:trustGovt.c -7.119e-02 4.998e-02 5.362e+03
## pDem_Rep:wave4:GCB.c:Pos_Emo.c:trustGovt.c 4.572e-02 5.597e-02 5.374e+03
## pDem_Rep:wave2:GCB.c:Neg_Emo.c:trustGovt.c -1.293e-01 5.214e-02 5.256e+03
## pDem_Rep:wave3:GCB.c:Neg_Emo.c:trustGovt.c -7.861e-02 5.404e-02 5.281e+03
## pDem_Rep:wave4:GCB.c:Neg_Emo.c:trustGovt.c 8.586e-02 7.340e-02 5.339e+03
## t value Pr(>|t|)
## (Intercept) 38.821 < 2e-16 ***
## pDem_Rep -5.750 9.35e-09 ***
## wave2 3.674 0.000241 ***
## wave3 2.739 0.006182 **
## wave4 2.505 0.012272 *
## GCB.c -12.902 < 2e-16 ***
## Pos_Emo.c -2.054 0.040042 *
## Neg_Emo.c -4.080 4.56e-05 ***
## trustGovt.c -5.005 5.74e-07 ***
## pDem_Rep:wave2 2.196 0.028115 *
## pDem_Rep:wave3 2.503 0.012328 *
## pDem_Rep:wave4 4.394 1.13e-05 ***
## pDem_Rep:GCB.c 0.500 0.617047
## wave2:GCB.c 3.523 0.000431 ***
## wave3:GCB.c 1.365 0.172168
## wave4:GCB.c 1.642 0.100711
## pDem_Rep:Pos_Emo.c -0.326 0.744807
## pDem_Rep:Neg_Emo.c -2.099 0.035844 *
## wave2:Pos_Emo.c -2.361 0.018255 *
## wave3:Pos_Emo.c 0.323 0.746503
## wave4:Pos_Emo.c -0.258 0.796595
## wave2:Neg_Emo.c -3.935 8.43e-05 ***
## wave3:Neg_Emo.c -1.614 0.106607
## wave4:Neg_Emo.c -0.932 0.351405
## GCB.c:Pos_Emo.c -0.203 0.839255
## GCB.c:Neg_Emo.c -2.224 0.026210 *
## pDem_Rep:trustGovt.c 1.426 0.153850
## wave2:trustGovt.c 1.862 0.062707 .
## wave3:trustGovt.c -0.003 0.997964
## wave4:trustGovt.c 1.274 0.202887
## GCB.c:trustGovt.c -4.771 1.87e-06 ***
## Pos_Emo.c:trustGovt.c -0.913 0.361502
## Neg_Emo.c:trustGovt.c -0.856 0.391831
## pDem_Rep:wave2:GCB.c 1.033 0.301469
## pDem_Rep:wave3:GCB.c 2.154 0.031312 *
## pDem_Rep:wave4:GCB.c 1.898 0.057773 .
## pDem_Rep:wave2:Pos_Emo.c 1.496 0.134734
## pDem_Rep:wave3:Pos_Emo.c 0.467 0.640870
## pDem_Rep:wave4:Pos_Emo.c -0.103 0.918359
## pDem_Rep:wave2:Neg_Emo.c 1.458 0.144817
## pDem_Rep:wave3:Neg_Emo.c 0.568 0.569908
## pDem_Rep:wave4:Neg_Emo.c 2.529 0.011467 *
## pDem_Rep:GCB.c:Pos_Emo.c 0.958 0.337897
## pDem_Rep:GCB.c:Neg_Emo.c -1.268 0.204947
## wave2:GCB.c:Pos_Emo.c -0.749 0.454118
## wave3:GCB.c:Pos_Emo.c -0.510 0.609862
## wave4:GCB.c:Pos_Emo.c 2.213 0.026912 *
## wave2:GCB.c:Neg_Emo.c 0.476 0.634268
## wave3:GCB.c:Neg_Emo.c 0.059 0.953311
## wave4:GCB.c:Neg_Emo.c 1.034 0.300996
## pDem_Rep:wave2:trustGovt.c 0.283 0.777395
## pDem_Rep:wave3:trustGovt.c -1.831 0.067151 .
## pDem_Rep:wave4:trustGovt.c -2.220 0.026490 *
## pDem_Rep:GCB.c:trustGovt.c -0.067 0.946885
## wave2:GCB.c:trustGovt.c -0.137 0.890842
## wave3:GCB.c:trustGovt.c 1.188 0.234777
## wave4:GCB.c:trustGovt.c 1.161 0.245780
## pDem_Rep:Pos_Emo.c:trustGovt.c 0.099 0.921016
## pDem_Rep:Neg_Emo.c:trustGovt.c 0.076 0.939539
## wave2:Pos_Emo.c:trustGovt.c 0.244 0.806938
## wave3:Pos_Emo.c:trustGovt.c 0.661 0.508742
## wave4:Pos_Emo.c:trustGovt.c 0.282 0.777613
## wave2:Neg_Emo.c:trustGovt.c 1.691 0.090981 .
## wave3:Neg_Emo.c:trustGovt.c 0.043 0.965692
## wave4:Neg_Emo.c:trustGovt.c -1.282 0.199995
## GCB.c:Pos_Emo.c:trustGovt.c -0.745 0.456117
## GCB.c:Neg_Emo.c:trustGovt.c -2.997 0.002741 **
## pDem_Rep:wave2:GCB.c:Pos_Emo.c -0.091 0.927415
## pDem_Rep:wave3:GCB.c:Pos_Emo.c -0.783 0.433500
## pDem_Rep:wave4:GCB.c:Pos_Emo.c -1.005 0.314713
## pDem_Rep:wave2:GCB.c:Neg_Emo.c 1.531 0.125838
## pDem_Rep:wave3:GCB.c:Neg_Emo.c 1.055 0.291637
## pDem_Rep:wave4:GCB.c:Neg_Emo.c 2.885 0.003935 **
## pDem_Rep:wave2:GCB.c:trustGovt.c -0.557 0.577265
## pDem_Rep:wave3:GCB.c:trustGovt.c 0.520 0.603033
## pDem_Rep:wave4:GCB.c:trustGovt.c 0.432 0.665507
## pDem_Rep:wave2:Pos_Emo.c:trustGovt.c -0.262 0.793159
## pDem_Rep:wave3:Pos_Emo.c:trustGovt.c 0.809 0.418301
## pDem_Rep:wave4:Pos_Emo.c:trustGovt.c -0.329 0.742142
## pDem_Rep:wave2:Neg_Emo.c:trustGovt.c -0.297 0.766579
## pDem_Rep:wave3:Neg_Emo.c:trustGovt.c -0.489 0.624966
## pDem_Rep:wave4:Neg_Emo.c:trustGovt.c -0.043 0.965427
## pDem_Rep:GCB.c:Pos_Emo.c:trustGovt.c 0.286 0.774763
## pDem_Rep:GCB.c:Neg_Emo.c:trustGovt.c -1.561 0.118536
## wave2:GCB.c:Pos_Emo.c:trustGovt.c 0.196 0.844432
## wave3:GCB.c:Pos_Emo.c:trustGovt.c -0.281 0.778743
## wave4:GCB.c:Pos_Emo.c:trustGovt.c 0.797 0.425513
## wave2:GCB.c:Neg_Emo.c:trustGovt.c -1.371 0.170419
## wave3:GCB.c:Neg_Emo.c:trustGovt.c -0.950 0.342231
## wave4:GCB.c:Neg_Emo.c:trustGovt.c -0.242 0.808604
## pDem_Rep:wave2:GCB.c:Pos_Emo.c:trustGovt.c -0.451 0.652344
## pDem_Rep:wave3:GCB.c:Pos_Emo.c:trustGovt.c -1.424 0.154372
## pDem_Rep:wave4:GCB.c:Pos_Emo.c:trustGovt.c 0.817 0.414021
## pDem_Rep:wave2:GCB.c:Neg_Emo.c:trustGovt.c -2.480 0.013182 *
## pDem_Rep:wave3:GCB.c:Neg_Emo.c:trustGovt.c -1.455 0.145844
## pDem_Rep:wave4:GCB.c:Neg_Emo.c:trustGovt.c 1.170 0.242142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15. Moderator: Affective Polarization
Outgroup Rating Version
ggplot(d, aes(x=FT_Outgroup)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9) +
theme_bw() +
labs(x = "Perception of Outparty")
ggplot(d[!is.na(d$party_factor),], aes(x=FT_Outgroup, fill = party_factor)) +
geom_density(color="black", alpha=0.5, position = 'identity') +
theme_bw() +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
labs(x = "Perception of Outparty")
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = FT_Outgroup,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Perception of Outparty",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = FT_Outgroup,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Perception of Outparty",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = FT_Outgroup,
y = demNorms,
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 = "Perception of Outparty",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3))
#### Ingroup - Outgroup Version
ggplot(d, aes(x=affPol, y = demNorms)) +
geom_jitter(height = .1, width = .2, alpha = .4, size = .2) +
geom_smooth(method = "lm", color="#69b3a2", se = F) +
theme_bw() +
labs(x = "Affective Polarization",
y = "Support for Democratic Norms")
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = affPol,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Affective Polarization",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3), xlim = c(0,100)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = affPol,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(alpha = .4, size = .2) +
geom_smooth(method = "lm", se = F, fullrange = T) +
labs(x = "Affective Polarization",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw()# +
# coord_cartesian(ylim = c(1,5), xlim = c(0,100))
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = affPol,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Affective Polarization",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3), xlim = c(0,100))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = affPol,
y = demNorms,
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 = "Affective Polarization",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(-3,3), xlim = c(0,100)) 
Summary
Models
DN.m5 <- lmer(demNorms ~ (pDem_Rep) * affPol * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * affPol * (wave.lin + wave.quad + wave.cub) +
## (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22352.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3827 -0.4546 0.1012 0.4781 4.2776
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3619 1.1670
## Residual 0.7062 0.8404
## Number of obs: 7162, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.475e+00 3.642e-02 4.631e+03 40.483 < 2e-16 ***
## pDem_Rep -2.065e-02 6.614e-02 6.339e+03 -0.312 0.754920
## affPol -2.157e-03 4.814e-04 7.146e+03 -4.480 7.58e-06 ***
## wave.lin 1.693e-01 5.233e-02 5.231e+03 3.236 0.001219 **
## wave.quad 2.259e-01 7.759e-02 5.092e+03 2.911 0.003616 **
## wave.cub -1.770e-01 4.666e-02 5.017e+03 -3.794 0.000150 ***
## pDem_Rep:affPol -3.511e-03 9.472e-04 7.145e+03 -3.707 0.000211 ***
## pDem_Rep:wave.lin 7.313e-02 1.055e-01 5.265e+03 0.693 0.488138
## pDem_Rep:wave.quad -5.099e-02 1.564e-01 5.126e+03 -0.326 0.744482
## pDem_Rep:wave.cub -9.821e-02 9.397e-02 5.046e+03 -1.045 0.295994
## affPol:wave.lin -9.996e-04 8.206e-04 5.236e+03 -1.218 0.223226
## affPol:wave.quad 6.409e-04 1.220e-03 5.100e+03 0.525 0.599304
## affPol:wave.cub 2.404e-03 7.365e-04 5.037e+03 3.265 0.001103 **
## pDem_Rep:affPol:wave.lin 3.740e-03 1.648e-03 5.255e+03 2.270 0.023249 *
## pDem_Rep:affPol:wave.quad 6.006e-03 2.450e-03 5.120e+03 2.451 0.014278 *
## pDem_Rep:affPol:wave.cub -1.218e-03 1.475e-03 5.048e+03 -0.825 0.409148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16. Moderator: Emotions
Positive Emotions
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",], aes(x=Pos_Emo, y = demNorms)) +
geom_smooth(method="lm", color="#69b3a2") +
theme_bw() +
labs(x = "Positive Emotions",
y = "Support for Democratic Norms") +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = Pos_Emo,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Positive Emotions",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1)) +
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[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x=Neg_Emo, y = demNorms)) +
geom_smooth(method="lm", color="#69b3a2") +
theme_bw() +
labs(x = "Negative Emotions",
y = "Support for Democratic Norms") +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = Neg_Emo,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_smooth(method = "lm") +
facet_grid(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Negative Emotions",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2" ,"#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2" ,"#db2b27")) +
coord_cartesian(ylim = c(-3,3)) +
scale_y_continuous(breaks = seq(-3,3,1)) +
theme_bw()
Summary
Models
DN.m6 <- lmer(demNorms ~ (pDem_Rep) * (Neg_Emo) * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * (Neg_Emo) * (wave.lin + wave.quad + wave.cub) +
## (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22297.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4121 -0.4472 0.0945 0.4794 4.2038
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3723 1.171
## Residual 0.7006 0.837
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.60768 0.04206 6085.55729 38.225 < 2e-16
## pDem_Rep -0.22362 0.08165 6743.04541 -2.739 0.00618
## Neg_Emo -0.09846 0.01452 6556.86977 -6.779 1.31e-11
## wave.lin 0.09981 0.07049 5253.03495 1.416 0.15685
## wave.quad 0.47005 0.10401 5123.01605 4.519 6.35e-06
## wave.cub -0.12257 0.06248 5068.20190 -1.962 0.04985
## pDem_Rep:Neg_Emo -0.02293 0.02863 6467.47082 -0.801 0.42323
## pDem_Rep:wave.lin -0.16640 0.14144 5259.49875 -1.176 0.23946
## pDem_Rep:wave.quad 0.21645 0.20821 5127.56079 1.040 0.29859
## pDem_Rep:wave.cub 0.17773 0.12508 5072.11361 1.421 0.15540
## Neg_Emo:wave.lin 0.02043 0.02923 5312.12214 0.699 0.48448
## Neg_Emo:wave.quad -0.13471 0.04407 5186.26961 -3.056 0.00225
## Neg_Emo:wave.cub 0.01698 0.02696 5108.80402 0.630 0.52885
## pDem_Rep:Neg_Emo:wave.lin 0.15500 0.05874 5327.08427 2.639 0.00834
## pDem_Rep:Neg_Emo:wave.quad -0.11438 0.08835 5199.12025 -1.295 0.19551
## pDem_Rep:Neg_Emo:wave.cub -0.11035 0.05397 5115.45378 -2.045 0.04094
##
## (Intercept) ***
## pDem_Rep **
## Neg_Emo ***
## wave.lin
## wave.quad ***
## wave.cub *
## pDem_Rep:Neg_Emo
## pDem_Rep:wave.lin
## pDem_Rep:wave.quad
## pDem_Rep:wave.cub
## Neg_Emo:wave.lin
## Neg_Emo:wave.quad **
## Neg_Emo:wave.cub
## pDem_Rep:Neg_Emo:wave.lin **
## pDem_Rep:Neg_Emo:wave.quad
## pDem_Rep:Neg_Emo:wave.cub *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m7 <- lmer(demNorms ~ (pDem_Rep) * (Pos_Emo) * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * (Pos_Emo) * (wave.lin + wave.quad + wave.cub) +
## (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22355.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3505 -0.4470 0.1088 0.4818 4.3112
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3508 1.1622
## Residual 0.7126 0.8441
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.51831 0.04561 6553.53646 33.288 < 2e-16
## pDem_Rep -0.14707 0.08852 7010.07629 -1.662 0.09665
## Pos_Emo -0.05312 0.01298 6678.81135 -4.092 4.32e-05
## wave.lin 0.03473 0.07924 5294.45785 0.438 0.66122
## wave.quad 0.17701 0.11890 5181.02286 1.489 0.13663
## wave.cub -0.08305 0.07178 5078.97571 -1.157 0.24732
## pDem_Rep:Pos_Emo 0.01413 0.02558 6592.66389 0.553 0.58060
## pDem_Rep:wave.lin 0.50691 0.15879 5300.25539 3.192 0.00142
## pDem_Rep:wave.quad 0.12733 0.23841 5194.47675 0.534 0.59331
## pDem_Rep:wave.cub -0.27676 0.14406 5093.56919 -1.921 0.05477
## Pos_Emo:wave.lin 0.02745 0.02600 5302.26399 1.056 0.29107
## Pos_Emo:wave.quad 0.01073 0.03854 5183.09405 0.279 0.78064
## Pos_Emo:wave.cub 0.01460 0.02301 5086.11798 0.634 0.52590
## pDem_Rep:Pos_Emo:wave.lin -0.07274 0.05215 5312.72087 -1.395 0.16310
## pDem_Rep:Pos_Emo:wave.quad 0.08366 0.07724 5193.68824 1.083 0.27880
## pDem_Rep:Pos_Emo:wave.cub 0.01050 0.04620 5100.96100 0.227 0.82026
##
## (Intercept) ***
## pDem_Rep .
## Pos_Emo ***
## wave.lin
## wave.quad
## wave.cub
## pDem_Rep:Pos_Emo
## pDem_Rep:wave.lin **
## pDem_Rep:wave.quad
## pDem_Rep:wave.cub .
## Pos_Emo:wave.lin
## Pos_Emo:wave.quad
## Pos_Emo:wave.cub
## pDem_Rep:Pos_Emo:wave.lin
## pDem_Rep:Pos_Emo:wave.quad
## pDem_Rep:Pos_Emo:wave.cub
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
DN.m6.full <- lmer(demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * (Neg_Emo * Pos_Emo) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m6.full)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) * (Neg_Emo *
## Pos_Emo) + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22274.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2500 -0.4560 0.0901 0.4874 4.0801
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2877 1.135
## Residual 0.7056 0.840
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.644e+00 8.766e-02 6.955e+03 18.752
## pDem_Rep -3.752e-01 1.738e-01 6.862e+03 -2.159
## wave.lin 1.478e-01 1.793e-01 5.304e+03 0.825
## wave.quad 6.753e-01 2.673e-01 5.162e+03 2.527
## wave.cub -3.586e-01 1.619e-01 5.092e+03 -2.215
## Neg_Emo 2.444e-02 3.311e-02 6.153e+03 0.738
## Pos_Emo 7.746e-03 2.597e-02 6.358e+03 0.298
## pDem_Rep:wave.lin -3.745e-01 3.586e-01 5.305e+03 -1.044
## pDem_Rep:wave.quad -9.135e-01 5.363e-01 5.177e+03 -1.703
## pDem_Rep:wave.cub 6.341e-01 3.241e-01 5.096e+03 1.957
## Neg_Emo:Pos_Emo -5.527e-02 1.085e-02 6.109e+03 -5.096
## pDem_Rep:Neg_Emo 9.274e-02 6.556e-02 6.095e+03 1.415
## pDem_Rep:Pos_Emo 5.472e-02 5.149e-02 6.310e+03 1.063
## wave.lin:Neg_Emo -2.568e-02 7.288e-02 5.350e+03 -0.352
## wave.lin:Pos_Emo -5.652e-03 5.641e-02 5.319e+03 -0.100
## wave.quad:Neg_Emo -1.477e-01 1.115e-01 5.233e+03 -1.324
## wave.quad:Pos_Emo -7.104e-02 8.332e-02 5.176e+03 -0.853
## wave.cub:Neg_Emo 5.014e-02 6.892e-02 5.131e+03 0.728
## wave.cub:Pos_Emo 7.770e-02 4.989e-02 5.097e+03 1.557
## pDem_Rep:Neg_Emo:Pos_Emo -3.390e-02 2.135e-02 6.019e+03 -1.588
## wave.lin:Neg_Emo:Pos_Emo 8.740e-03 2.450e-02 5.322e+03 0.357
## wave.quad:Neg_Emo:Pos_Emo 2.146e-03 3.666e-02 5.212e+03 0.059
## wave.cub:Neg_Emo:Pos_Emo -8.519e-03 2.201e-02 5.112e+03 -0.387
## pDem_Rep:wave.lin:Neg_Emo 3.723e-01 1.460e-01 5.361e+03 2.551
## pDem_Rep:wave.lin:Pos_Emo 7.545e-02 1.133e-01 5.338e+03 0.666
## pDem_Rep:wave.quad:Neg_Emo 4.234e-01 2.242e-01 5.257e+03 1.888
## pDem_Rep:wave.quad:Pos_Emo 3.474e-01 1.670e-01 5.187e+03 2.080
## pDem_Rep:wave.cub:Neg_Emo -3.501e-01 1.381e-01 5.141e+03 -2.535
## pDem_Rep:wave.cub:Pos_Emo -1.765e-01 9.985e-02 5.100e+03 -1.768
## pDem_Rep:wave.lin:Neg_Emo:Pos_Emo -7.492e-02 4.922e-02 5.348e+03 -1.522
## pDem_Rep:wave.quad:Neg_Emo:Pos_Emo -1.417e-01 7.368e-02 5.234e+03 -1.923
## pDem_Rep:wave.cub:Neg_Emo:Pos_Emo 8.214e-02 4.416e-02 5.126e+03 1.860
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.0309 *
## wave.lin 0.4096
## wave.quad 0.0115 *
## wave.cub 0.0268 *
## Neg_Emo 0.4605
## Pos_Emo 0.7655
## pDem_Rep:wave.lin 0.2964
## pDem_Rep:wave.quad 0.0886 .
## pDem_Rep:wave.cub 0.0504 .
## Neg_Emo:Pos_Emo 3.58e-07 ***
## pDem_Rep:Neg_Emo 0.1572
## pDem_Rep:Pos_Emo 0.2880
## wave.lin:Neg_Emo 0.7246
## wave.lin:Pos_Emo 0.9202
## wave.quad:Neg_Emo 0.1854
## wave.quad:Pos_Emo 0.3939
## wave.cub:Neg_Emo 0.4669
## wave.cub:Pos_Emo 0.1194
## pDem_Rep:Neg_Emo:Pos_Emo 0.1124
## wave.lin:Neg_Emo:Pos_Emo 0.7213
## wave.quad:Neg_Emo:Pos_Emo 0.9533
## wave.cub:Neg_Emo:Pos_Emo 0.6988
## pDem_Rep:wave.lin:Neg_Emo 0.0108 *
## pDem_Rep:wave.lin:Pos_Emo 0.5054
## pDem_Rep:wave.quad:Neg_Emo 0.0591 .
## pDem_Rep:wave.quad:Pos_Emo 0.0376 *
## pDem_Rep:wave.cub:Neg_Emo 0.0113 *
## pDem_Rep:wave.cub:Pos_Emo 0.0771 .
## pDem_Rep:wave.lin:Neg_Emo:Pos_Emo 0.1281
## pDem_Rep:wave.quad:Neg_Emo:Pos_Emo 0.0545 .
## pDem_Rep:wave.cub:Neg_Emo:Pos_Emo 0.0630 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m6.part <- lmer(demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) + (Neg_Emo * Pos_Emo) + (1|pid),
data = d[d$party_factor != "Independent",])
summary(DN.m6.part)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * (wave.lin + wave.quad + wave.cub) + (Neg_Emo *
## Pos_Emo) + (1 | pid)
## Data: d[d$party_factor != "Independent", ]
##
## REML criterion at convergence: 22237.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2904 -0.4559 0.1001 0.4872 4.2598
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2954 1.1382
## Residual 0.7095 0.8423
## Number of obs: 7172, groups: pid, 2367
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.757e+00 7.916e-02 7.106e+03 22.196 < 2e-16 ***
## pDem_Rep -2.107e-01 5.181e-02 3.954e+03 -4.067 4.86e-05 ***
## wave.lin 8.915e-02 2.951e-02 5.117e+03 3.021 0.002530 **
## wave.quad 2.114e-01 4.343e-02 4.953e+03 4.869 1.16e-06 ***
## wave.cub -4.774e-02 2.619e-02 4.886e+03 -1.823 0.068430 .
## Neg_Emo -5.443e-02 2.405e-02 6.479e+03 -2.263 0.023643 *
## Pos_Emo -2.596e-02 2.225e-02 6.497e+03 -1.167 0.243351
## pDem_Rep:wave.lin 2.054e-01 6.149e-02 5.283e+03 3.341 0.000841 ***
## pDem_Rep:wave.quad 2.249e-01 8.998e-02 5.107e+03 2.500 0.012459 *
## pDem_Rep:wave.cub -1.393e-01 5.348e-02 4.988e+03 -2.604 0.009230 **
## Neg_Emo:Pos_Emo -3.160e-02 8.142e-03 6.282e+03 -3.882 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pDm_Rp wav.ln wav.qd wav.cb Neg_Em Pos_Em pDm_Rp:wv.l
## pDem_Rep 0.001
## wave.lin 0.169 -0.032
## wave.quad 0.079 -0.007 -0.179
## wave.cub -0.160 0.021 -0.142 0.140
## Neg_Emo -0.828 0.144 -0.191 -0.116 0.168
## Pos_Emo -0.852 -0.083 -0.125 -0.133 0.147 0.761
## pDm_Rp:wv.l -0.035 0.247 0.063 -0.007 0.002 0.156 -0.074
## pDm_Rp:wv.q 0.049 0.062 -0.034 0.110 0.015 0.060 -0.142 -0.095
## pDm_Rp:wv.c 0.002 -0.127 0.005 0.004 0.088 -0.084 0.085 -0.188
## Ng_Em:Ps_Em 0.628 -0.014 0.192 0.162 -0.155 -0.822 -0.806 -0.030
## pDm_Rp:wv.q pDm_Rp:wv.c
## pDem_Rep
## wave.lin
## wave.quad
## wave.cub
## Neg_Emo
## Pos_Emo
## pDm_Rp:wv.l
## pDm_Rp:wv.q
## pDm_Rp:wv.c 0.082
## Ng_Em:Ps_Em 0.027 -0.011interact_plot(DN.m6.part, pred = Neg_Emo, modx = Pos_Emo) +
coord_cartesian(ylim = c(0,3)) +
labs(x = "Negative Emotions",
y = "Support for Democratic Norms") 
7. Moderator: Representation
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = representation_Dem,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F) +
labs(x = "Representation (Democratic elites)",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3), xlim = c(1,5)) +
scale_x_continuous(breaks = seq(1,5,1)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_smooth(aes(x = representation_Dem,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Democratic elites)",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = representation_Dem,
y = demNorms,
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 = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = representation_Rep,
y = demNorms,
fill = party_factor,
color = party_factor)) +
geom_jitter(size = .2, alpha = .4) +
geom_smooth(method = "lm", se = F) +
labs(x = "Representation (Republican elites)",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3), xlim = c(1,5)) +
scale_x_continuous(breaks = seq(1,5,1)) 
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",]) +
geom_smooth(aes(x = representation_Rep,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Representation (Republican elites)",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor) & d$party_factor != "Independent",],
aes(x = representation_Rep,
y = demNorms,
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 = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1))
Summary
Models
DN.m8 <- lmer(demNorms ~ (pDem_Rep) * (representation_Dem + representation_Rep) * (wave.lin + wave.quad + wave.cub) + (wave.lin + pDem_Rep|pid),
data = d)
summary(DN.m8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * (representation_Dem + representation_Rep) *
## (wave.lin + wave.quad + wave.cub) + (wave.lin + pDem_Rep | pid)
## Data: d
##
## REML criterion at convergence: 13726.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3938 -0.4634 0.0978 0.4856 4.1713
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pid (Intercept) 1.17841 1.0855
## wave.lin 0.04855 0.2204 -0.09
## pDem_Rep 0.56172 0.7495 -0.12 -0.98
## Residual 0.70281 0.8383
## Number of obs: 4485, groups: pid, 1163
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.92027 0.11579 1157.68784 16.584
## pDem_Rep 0.07449 0.21851 936.63572 0.341
## representation_Dem -0.07155 0.03099 1178.50331 -2.309
## representation_Rep -0.10963 0.02981 1135.77811 -3.677
## wave.lin 0.01696 0.10763 1939.43972 0.158
## wave.quad 0.51842 0.16573 2908.06783 3.128
## wave.cub -0.13737 0.10429 2875.52650 -1.317
## pDem_Rep:representation_Dem -0.01569 0.05960 1031.81050 -0.263
## pDem_Rep:representation_Rep -0.07213 0.05668 919.13968 -1.273
## pDem_Rep:wave.lin 0.66468 0.21590 1920.86353 3.079
## pDem_Rep:wave.quad 0.03299 0.33218 2915.48427 0.099
## pDem_Rep:wave.cub -0.24828 0.20909 2902.33181 -1.187
## representation_Dem:wave.lin 0.01048 0.02837 1904.53099 0.369
## representation_Dem:wave.quad -0.09059 0.04385 2880.32047 -2.066
## representation_Dem:wave.cub -0.02271 0.02773 2870.39551 -0.819
## representation_Rep:wave.lin 0.01527 0.02755 1912.82142 0.554
## representation_Rep:wave.quad -0.02386 0.04253 2883.50216 -0.561
## representation_Rep:wave.cub 0.05119 0.02684 2868.81628 1.907
## pDem_Rep:representation_Dem:wave.lin -0.09318 0.05683 1903.30841 -1.640
## pDem_Rep:representation_Dem:wave.quad -0.11622 0.08785 2894.08126 -1.323
## pDem_Rep:representation_Dem:wave.cub -0.01102 0.05554 2880.44648 -0.198
## pDem_Rep:representation_Rep:wave.lin -0.05515 0.05524 1905.72181 -0.998
## pDem_Rep:representation_Rep:wave.quad 0.14353 0.08521 2901.49847 1.684
## pDem_Rep:representation_Rep:wave.cub 0.03211 0.05376 2881.66063 0.597
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.733267
## representation_Dem 0.021127 *
## representation_Rep 0.000247 ***
## wave.lin 0.874834
## wave.quad 0.001777 **
## wave.cub 0.187889
## pDem_Rep:representation_Dem 0.792440
## pDem_Rep:representation_Rep 0.203472
## pDem_Rep:wave.lin 0.002109 **
## pDem_Rep:wave.quad 0.920895
## pDem_Rep:wave.cub 0.235150
## representation_Dem:wave.lin 0.711877
## representation_Dem:wave.quad 0.038945 *
## representation_Dem:wave.cub 0.412904
## representation_Rep:wave.lin 0.579559
## representation_Rep:wave.quad 0.574842
## representation_Rep:wave.cub 0.056570 .
## pDem_Rep:representation_Dem:wave.lin 0.101261
## pDem_Rep:representation_Dem:wave.quad 0.185952
## pDem_Rep:representation_Dem:wave.cub 0.842778
## pDem_Rep:representation_Rep:wave.lin 0.318207
## pDem_Rep:representation_Rep:wave.quad 0.092207 .
## pDem_Rep:representation_Rep:wave.cub 0.550413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
DN.m8.w <- lmer(demNorms ~ (pDem_Rep) * (representation_Dem + representation_Rep) * wave + (pDem_Rep|pid),
data = d)
summary(DN.m8.w)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * (representation_Dem + representation_Rep) *
## wave + (pDem_Rep | pid)
## Data: d
##
## REML criterion at convergence: 13738.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3984 -0.4678 0.0937 0.4904 4.1808
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pid (Intercept) 1.1682 1.0808
## pDem_Rep 0.5859 0.7655 -0.11
## Residual 0.7128 0.8443
## Number of obs: 4485, groups: pid, 1163
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.752e+00 1.372e-01 2.144e+03 12.773
## pDem_Rep -3.181e-01 2.642e-01 1.785e+03 -1.204
## representation_Dem -5.983e-02 3.645e-02 2.136e+03 -1.641
## representation_Rep -9.960e-02 3.513e-02 2.073e+03 -2.835
## wave2 3.630e-01 1.182e-01 3.316e+03 3.071
## wave3 2.348e-01 1.185e-01 3.329e+03 1.982
## wave4 8.402e-02 1.192e-01 3.352e+03 0.705
## pDem_Rep:representation_Dem 5.905e-02 7.091e-02 1.832e+03 0.833
## pDem_Rep:representation_Rep -7.665e-02 6.784e-02 1.650e+03 -1.130
## pDem_Rep:wave2 3.560e-01 2.369e-01 3.299e+03 1.503
## pDem_Rep:wave3 4.471e-01 2.375e-01 3.311e+03 1.882
## pDem_Rep:wave4 7.793e-01 2.392e-01 3.333e+03 3.257
## representation_Dem:wave2 -2.621e-02 3.126e-02 3.316e+03 -0.838
## representation_Dem:wave3 -4.348e-02 3.134e-02 3.312e+03 -1.387
## representation_Dem:wave4 2.097e-02 3.136e-02 3.323e+03 0.669
## representation_Rep:wave2 -4.542e-02 3.031e-02 3.315e+03 -1.499
## representation_Rep:wave3 1.327e-02 3.033e-02 3.320e+03 0.437
## representation_Rep:wave4 -9.323e-03 3.045e-02 3.341e+03 -0.306
## pDem_Rep:representation_Dem:wave2 -7.401e-02 6.267e-02 3.300e+03 -1.181
## pDem_Rep:representation_Dem:wave3 -1.318e-01 6.279e-02 3.298e+03 -2.100
## pDem_Rep:representation_Dem:wave4 -8.856e-02 6.284e-02 3.311e+03 -1.409
## pDem_Rep:representation_Rep:wave2 3.791e-02 6.074e-02 3.298e+03 0.624
## pDem_Rep:representation_Rep:wave3 4.066e-02 6.080e-02 3.304e+03 0.669
## pDem_Rep:representation_Rep:wave4 -6.833e-02 6.110e-02 3.323e+03 -1.118
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.22867
## representation_Dem 0.10087
## representation_Rep 0.00462 **
## wave2 0.00215 **
## wave3 0.04761 *
## wave4 0.48097
## pDem_Rep:representation_Dem 0.40517
## pDem_Rep:representation_Rep 0.25868
## pDem_Rep:wave2 0.13296
## pDem_Rep:wave3 0.05987 .
## pDem_Rep:wave4 0.00114 **
## representation_Dem:wave2 0.40186
## representation_Dem:wave3 0.16541
## representation_Dem:wave4 0.50381
## representation_Rep:wave2 0.13409
## representation_Rep:wave3 0.66191
## representation_Rep:wave4 0.75951
## pDem_Rep:representation_Dem:wave2 0.23771
## pDem_Rep:representation_Dem:wave3 0.03581 *
## pDem_Rep:representation_Dem:wave4 0.15888
## pDem_Rep:representation_Rep:wave2 0.53260
## pDem_Rep:representation_Rep:wave3 0.50364
## pDem_Rep:representation_Rep:wave4 0.26353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18. Moderator: Process variable
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = govt_process,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
labs(x = "Governmental Process Preference",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1)) 
ggplot(d[!is.na(d$party_factor),]) +
geom_smooth(aes(x = govt_process,
y = demNorms,
fill = party_factor,
color = party_factor),
method = "lm") +
facet_wrap(~wave, labeller = as_labeller(wave_label)) +
labs(x = "Governmental Process Preference",
y = "Support for Democratic Norms") +
scale_fill_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
scale_color_manual("Participant Party ID",
values = c("#1696d2","#db2b27")) +
theme_bw() +
coord_cartesian(ylim = c(0,3)) +
scale_x_continuous(breaks = seq(1,7,1))
ggplot(d[!is.na(d$party_factor),],
aes(x = govt_process,
y = demNorms,
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 = "Governmental Process Preference",
y = "Support for Democratic Norms") +
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(-3,3)) +
scale_x_continuous(breaks = seq(1,7,1)) +
scale_y_continuous(breaks = seq(-3,3,1)) 
Summary
Models
DN.m9 <- lmer(demNorms ~ (pDem_Rep) * gov_proc.4 * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(DN.m9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * gov_proc.4 * (wave.lin + wave.quad +
## wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13750.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3564 -0.4540 0.1136 0.4884 4.4219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3397 1.1574
## Residual 0.7178 0.8472
## Number of obs: 4493, groups: pid, 1165
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.41444 0.03646 1163.27947 38.794 < 2e-16
## pDem_Rep -0.16075 0.06618 1786.43614 -2.429 0.015242
## gov_proc.4 -0.10954 0.03142 1153.07045 -3.486 0.000508
## wave.lin 0.10021 0.03244 3338.23528 3.089 0.002025
## wave.quad 0.25796 0.05115 3333.62409 5.043 4.82e-07
## wave.cub -0.04663 0.03230 3330.40771 -1.444 0.148852
## pDem_Rep:gov_proc.4 -0.02411 0.05684 1793.10418 -0.424 0.671446
## pDem_Rep:wave.lin 0.26045 0.06522 3348.10021 3.993 6.66e-05
## pDem_Rep:wave.quad 0.19012 0.10267 3339.28444 1.852 0.064152
## pDem_Rep:wave.cub -0.11522 0.06486 3338.65957 -1.776 0.075757
## gov_proc.4:wave.lin -0.05563 0.02784 3337.43537 -1.998 0.045794
## gov_proc.4:wave.quad -0.08562 0.04387 3326.23508 -1.952 0.051071
## gov_proc.4:wave.cub 0.02493 0.02774 3329.17301 0.899 0.368810
## pDem_Rep:gov_proc.4:wave.lin -0.03524 0.05599 3341.02987 -0.629 0.529097
## pDem_Rep:gov_proc.4:wave.quad -0.18919 0.08808 3331.64400 -2.148 0.031780
## pDem_Rep:gov_proc.4:wave.cub -0.10003 0.05566 3334.38067 -1.797 0.072413
##
## (Intercept) ***
## pDem_Rep *
## gov_proc.4 ***
## wave.lin **
## wave.quad ***
## wave.cub
## pDem_Rep:gov_proc.4
## pDem_Rep:wave.lin ***
## pDem_Rep:wave.quad .
## pDem_Rep:wave.cub .
## gov_proc.4:wave.lin *
## gov_proc.4:wave.quad .
## gov_proc.4:wave.cub
## pDem_Rep:gov_proc.4:wave.lin
## pDem_Rep:gov_proc.4:wave.quad *
## pDem_Rep:gov_proc.4:wave.cub .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Simple-effects models
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: demNorms ~ (pDem_Rep) * gov_proc.4 * wave + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13754
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3564 -0.4540 0.1136 0.4884 4.4219
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.3397 1.1574
## Residual 0.7178 0.8472
## Number of obs: 4493, groups: pid, 1165
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.28819 0.04279 2091.89943 30.104 < 2e-16 ***
## pDem_Rep -0.36731 0.07961 3158.93816 -4.614 4.11e-06 ***
## gov_proc.4 -0.05409 0.03676 2061.50939 -1.471 0.141315
## wave2 0.18901 0.03619 3332.00365 5.222 1.88e-07 ***
## wave3 0.19248 0.03627 3335.05632 5.307 1.19e-07 ***
## wave4 0.12353 0.03632 3340.53500 3.401 0.000678 ***
## pDem_Rep:gov_proc.4 0.01580 0.06839 3139.60739 0.231 0.817343
## pDem_Rep:wave2 0.24659 0.07279 3341.68969 3.387 0.000714 ***
## pDem_Rep:wave3 0.26159 0.07289 3343.32279 3.589 0.000337 ***
## pDem_Rep:wave4 0.31806 0.07313 3353.18688 4.349 1.41e-05 ***
## gov_proc.4:wave2 -0.07542 0.03099 3326.11347 -2.434 0.015002 *
## gov_proc.4:wave3 -0.07830 0.03105 3325.69207 -2.522 0.011713 *
## gov_proc.4:wave4 -0.06809 0.03119 3342.30098 -2.183 0.029098 *
## pDem_Rep:gov_proc.4:wave2 -0.02838 0.06225 3332.76962 -0.456 0.648429
## pDem_Rep:gov_proc.4:wave3 -0.14604 0.06240 3333.71738 -2.340 0.019324 *
## pDem_Rep:gov_proc.4:wave4 0.01477 0.06270 3343.40527 0.236 0.813740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 19. Moderator: All process variables
Models
DN.m10 <- lmer(demNorms ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d)
summary(DN.m10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) *
## (wave.lin + wave.quad + wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13844.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3691 -0.4690 0.0933 0.4914 4.2235
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2646 1.1245
## Residual 0.7158 0.8461
## Number of obs: 4485, groups: pid, 1163
##
## Fixed effects:
## Estimate
## (Intercept) 1.831e+00
## pDem_Rep 2.170e-01
## gov_proc.4 -3.798e-01
## representation_Dem -7.919e-02
## representation_Rep -1.038e-01
## wave.lin 5.432e-02
## wave.quad 5.777e-01
## wave.cub -9.747e-02
## pDem_Rep:gov_proc.4 4.820e-01
## representation_Dem:representation_Rep 1.776e-02
## pDem_Rep:representation_Dem -1.037e-01
## pDem_Rep:representation_Rep -1.324e-01
## gov_proc.4:representation_Dem 1.322e-01
## gov_proc.4:representation_Rep 1.957e-01
## pDem_Rep:wave.lin 7.942e-01
## pDem_Rep:wave.quad -6.103e-02
## pDem_Rep:wave.cub -2.630e-01
## gov_proc.4:wave.lin -4.081e-01
## gov_proc.4:wave.quad -2.586e-01
## gov_proc.4:wave.cub 7.970e-03
## representation_Dem:wave.lin -3.663e-02
## representation_Dem:wave.quad -1.592e-01
## representation_Dem:wave.cub -4.034e-02
## representation_Rep:wave.lin -1.412e-02
## representation_Rep:wave.quad -7.006e-02
## representation_Rep:wave.cub 4.414e-02
## pDem_Rep:representation_Dem:representation_Rep 4.254e-02
## gov_proc.4:representation_Dem:representation_Rep -7.574e-02
## pDem_Rep:gov_proc.4:representation_Dem -1.224e-01
## pDem_Rep:gov_proc.4:representation_Rep -1.942e-01
## pDem_Rep:gov_proc.4:wave.lin 3.531e-01
## pDem_Rep:gov_proc.4:wave.quad -6.483e-01
## pDem_Rep:gov_proc.4:wave.cub -4.566e-01
## representation_Dem:representation_Rep:wave.lin 2.203e-02
## representation_Dem:representation_Rep:wave.quad 3.416e-02
## representation_Dem:representation_Rep:wave.cub 2.738e-03
## pDem_Rep:representation_Dem:wave.lin -1.940e-01
## pDem_Rep:representation_Dem:wave.quad -1.761e-01
## pDem_Rep:representation_Dem:wave.cub -1.817e-02
## pDem_Rep:representation_Rep:wave.lin -9.599e-02
## pDem_Rep:representation_Rep:wave.quad 1.556e-01
## pDem_Rep:representation_Rep:wave.cub 2.372e-02
## gov_proc.4:representation_Dem:wave.lin 1.407e-01
## gov_proc.4:representation_Dem:wave.quad 1.057e-01
## gov_proc.4:representation_Dem:wave.cub -3.329e-03
## gov_proc.4:representation_Rep:wave.lin 1.208e-01
## gov_proc.4:representation_Rep:wave.quad 8.427e-02
## gov_proc.4:representation_Rep:wave.cub -4.185e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 3.709e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 3.407e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 3.096e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 5.891e-03
## gov_proc.4:representation_Dem:representation_Rep:wave.lin -4.421e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.quad -4.187e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 1.251e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin -2.882e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.850e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 2.735e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin -1.476e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 1.467e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 2.023e-01
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 1.557e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad -5.813e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub -3.132e-02
## Std. Error
## (Intercept) 1.985e-01
## pDem_Rep 3.669e-01
## gov_proc.4 1.454e-01
## representation_Dem 7.454e-02
## representation_Rep 6.745e-02
## wave.lin 1.878e-01
## wave.quad 2.971e-01
## wave.cub 1.879e-01
## pDem_Rep:gov_proc.4 2.498e-01
## representation_Dem:representation_Rep 2.325e-02
## pDem_Rep:representation_Dem 1.383e-01
## pDem_Rep:representation_Rep 1.278e-01
## gov_proc.4:representation_Dem 4.887e-02
## gov_proc.4:representation_Rep 5.526e-02
## pDem_Rep:wave.lin 3.773e-01
## pDem_Rep:wave.quad 5.958e-01
## pDem_Rep:wave.cub 3.771e-01
## gov_proc.4:wave.lin 1.378e-01
## gov_proc.4:wave.quad 2.188e-01
## gov_proc.4:wave.cub 1.401e-01
## representation_Dem:wave.lin 7.180e-02
## representation_Dem:wave.quad 1.148e-01
## representation_Dem:wave.cub 7.290e-02
## representation_Rep:wave.lin 6.315e-02
## representation_Rep:wave.quad 9.986e-02
## representation_Rep:wave.cub 6.317e-02
## pDem_Rep:representation_Dem:representation_Rep 4.362e-02
## gov_proc.4:representation_Dem:representation_Rep 1.587e-02
## pDem_Rep:gov_proc.4:representation_Dem 8.435e-02
## pDem_Rep:gov_proc.4:representation_Rep 1.029e-01
## pDem_Rep:gov_proc.4:wave.lin 2.794e-01
## pDem_Rep:gov_proc.4:wave.quad 4.382e-01
## pDem_Rep:gov_proc.4:wave.cub 2.821e-01
## representation_Dem:representation_Rep:wave.lin 2.205e-02
## representation_Dem:representation_Rep:wave.quad 3.507e-02
## representation_Dem:representation_Rep:wave.cub 2.222e-02
## pDem_Rep:representation_Dem:wave.lin 1.442e-01
## pDem_Rep:representation_Dem:wave.quad 2.302e-01
## pDem_Rep:representation_Dem:wave.cub 1.462e-01
## pDem_Rep:representation_Rep:wave.lin 1.267e-01
## pDem_Rep:representation_Rep:wave.quad 2.001e-01
## pDem_Rep:representation_Rep:wave.cub 1.266e-01
## gov_proc.4:representation_Dem:wave.lin 4.705e-02
## gov_proc.4:representation_Dem:wave.quad 7.644e-02
## gov_proc.4:representation_Dem:wave.cub 5.029e-02
## gov_proc.4:representation_Rep:wave.lin 5.206e-02
## gov_proc.4:representation_Rep:wave.quad 8.245e-02
## gov_proc.4:representation_Rep:wave.cub 5.241e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 2.882e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 4.427e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 7.033e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 4.454e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 1.507e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 2.414e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 1.559e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 9.491e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.532e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 1.014e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 1.048e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 1.650e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 1.051e-01
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 3.032e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 4.837e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 3.132e-02
## df
## (Intercept) 1.230e+03
## pDem_Rep 1.695e+03
## gov_proc.4 1.227e+03
## representation_Dem 1.354e+03
## representation_Rep 1.242e+03
## wave.lin 3.330e+03
## wave.quad 3.327e+03
## wave.cub 3.315e+03
## pDem_Rep:gov_proc.4 2.252e+03
## representation_Dem:representation_Rep 1.304e+03
## pDem_Rep:representation_Dem 1.858e+03
## pDem_Rep:representation_Rep 1.556e+03
## gov_proc.4:representation_Dem 1.352e+03
## gov_proc.4:representation_Rep 1.294e+03
## pDem_Rep:wave.lin 3.341e+03
## pDem_Rep:wave.quad 3.324e+03
## pDem_Rep:wave.cub 3.314e+03
## gov_proc.4:wave.lin 3.321e+03
## gov_proc.4:wave.quad 3.332e+03
## gov_proc.4:wave.cub 3.349e+03
## representation_Dem:wave.lin 3.321e+03
## representation_Dem:wave.quad 3.330e+03
## representation_Dem:wave.cub 3.312e+03
## representation_Rep:wave.lin 3.315e+03
## representation_Rep:wave.quad 3.309e+03
## representation_Rep:wave.cub 3.300e+03
## pDem_Rep:representation_Dem:representation_Rep 1.711e+03
## gov_proc.4:representation_Dem:representation_Rep 1.327e+03
## pDem_Rep:gov_proc.4:representation_Dem 2.470e+03
## pDem_Rep:gov_proc.4:representation_Rep 1.723e+03
## pDem_Rep:gov_proc.4:wave.lin 3.351e+03
## pDem_Rep:gov_proc.4:wave.quad 3.308e+03
## pDem_Rep:gov_proc.4:wave.cub 3.338e+03
## representation_Dem:representation_Rep:wave.lin 3.315e+03
## representation_Dem:representation_Rep:wave.quad 3.317e+03
## representation_Dem:representation_Rep:wave.cub 3.301e+03
## pDem_Rep:representation_Dem:wave.lin 3.330e+03
## pDem_Rep:representation_Dem:wave.quad 3.328e+03
## pDem_Rep:representation_Dem:wave.cub 3.311e+03
## pDem_Rep:representation_Rep:wave.lin 3.321e+03
## pDem_Rep:representation_Rep:wave.quad 3.307e+03
## pDem_Rep:representation_Rep:wave.cub 3.300e+03
## gov_proc.4:representation_Dem:wave.lin 3.324e+03
## gov_proc.4:representation_Dem:wave.quad 3.328e+03
## gov_proc.4:representation_Dem:wave.cub 3.371e+03
## gov_proc.4:representation_Rep:wave.lin 3.305e+03
## gov_proc.4:representation_Rep:wave.quad 3.307e+03
## gov_proc.4:representation_Rep:wave.cub 3.309e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 1.957e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 3.321e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 3.316e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 3.301e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 3.317e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 3.311e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 3.331e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 3.335e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 3.311e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 3.362e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 3.319e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 3.296e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 3.305e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 3.321e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 3.303e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 3.326e+03
## t value
## (Intercept) 9.222
## pDem_Rep 0.591
## gov_proc.4 -2.612
## representation_Dem -1.062
## representation_Rep -1.540
## wave.lin 0.289
## wave.quad 1.945
## wave.cub -0.519
## pDem_Rep:gov_proc.4 1.929
## representation_Dem:representation_Rep 0.764
## pDem_Rep:representation_Dem -0.750
## pDem_Rep:representation_Rep -1.035
## gov_proc.4:representation_Dem 2.706
## gov_proc.4:representation_Rep 3.541
## pDem_Rep:wave.lin 2.105
## pDem_Rep:wave.quad -0.102
## pDem_Rep:wave.cub -0.697
## gov_proc.4:wave.lin -2.961
## gov_proc.4:wave.quad -1.182
## gov_proc.4:wave.cub 0.057
## representation_Dem:wave.lin -0.510
## representation_Dem:wave.quad -1.387
## representation_Dem:wave.cub -0.553
## representation_Rep:wave.lin -0.224
## representation_Rep:wave.quad -0.702
## representation_Rep:wave.cub 0.699
## pDem_Rep:representation_Dem:representation_Rep 0.975
## gov_proc.4:representation_Dem:representation_Rep -4.773
## pDem_Rep:gov_proc.4:representation_Dem -1.451
## pDem_Rep:gov_proc.4:representation_Rep -1.887
## pDem_Rep:gov_proc.4:wave.lin 1.264
## pDem_Rep:gov_proc.4:wave.quad -1.479
## pDem_Rep:gov_proc.4:wave.cub -1.618
## representation_Dem:representation_Rep:wave.lin 0.999
## representation_Dem:representation_Rep:wave.quad 0.974
## representation_Dem:representation_Rep:wave.cub 0.123
## pDem_Rep:representation_Dem:wave.lin -1.345
## pDem_Rep:representation_Dem:wave.quad -0.765
## pDem_Rep:representation_Dem:wave.cub -0.124
## pDem_Rep:representation_Rep:wave.lin -0.758
## pDem_Rep:representation_Rep:wave.quad 0.777
## pDem_Rep:representation_Rep:wave.cub 0.187
## gov_proc.4:representation_Dem:wave.lin 2.991
## gov_proc.4:representation_Dem:wave.quad 1.383
## gov_proc.4:representation_Dem:wave.cub -0.066
## gov_proc.4:representation_Rep:wave.lin 2.320
## gov_proc.4:representation_Rep:wave.quad 1.022
## gov_proc.4:representation_Rep:wave.cub -0.799
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 1.287
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 0.770
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 0.440
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 0.132
## gov_proc.4:representation_Dem:representation_Rep:wave.lin -2.934
## gov_proc.4:representation_Dem:representation_Rep:wave.quad -1.735
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.803
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin -0.304
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.208
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 0.270
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin -1.408
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 0.889
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 1.924
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 0.513
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad -1.202
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub -1.000
## Pr(>|t|)
## (Intercept) < 2e-16
## pDem_Rep 0.554346
## gov_proc.4 0.009113
## representation_Dem 0.288254
## representation_Rep 0.123904
## wave.lin 0.772381
## wave.quad 0.051878
## wave.cub 0.603977
## pDem_Rep:gov_proc.4 0.053832
## representation_Dem:representation_Rep 0.444975
## pDem_Rep:representation_Dem 0.453638
## pDem_Rep:representation_Rep 0.300639
## gov_proc.4:representation_Dem 0.006897
## gov_proc.4:representation_Rep 0.000414
## pDem_Rep:wave.lin 0.035354
## pDem_Rep:wave.quad 0.918430
## pDem_Rep:wave.cub 0.485631
## gov_proc.4:wave.lin 0.003087
## gov_proc.4:wave.quad 0.237353
## gov_proc.4:wave.cub 0.954649
## representation_Dem:wave.lin 0.609974
## representation_Dem:wave.quad 0.165481
## representation_Dem:wave.cub 0.580089
## representation_Rep:wave.lin 0.823062
## representation_Rep:wave.quad 0.482992
## representation_Rep:wave.cub 0.484729
## pDem_Rep:representation_Dem:representation_Rep 0.329618
## gov_proc.4:representation_Dem:representation_Rep 2.02e-06
## pDem_Rep:gov_proc.4:representation_Dem 0.146948
## pDem_Rep:gov_proc.4:representation_Rep 0.059386
## pDem_Rep:gov_proc.4:wave.lin 0.206408
## pDem_Rep:gov_proc.4:wave.quad 0.139184
## pDem_Rep:gov_proc.4:wave.cub 0.105662
## representation_Dem:representation_Rep:wave.lin 0.317761
## representation_Dem:representation_Rep:wave.quad 0.330155
## representation_Dem:representation_Rep:wave.cub 0.901951
## pDem_Rep:representation_Dem:wave.lin 0.178595
## pDem_Rep:representation_Dem:wave.quad 0.444285
## pDem_Rep:representation_Dem:wave.cub 0.901111
## pDem_Rep:representation_Rep:wave.lin 0.448720
## pDem_Rep:representation_Rep:wave.quad 0.436966
## pDem_Rep:representation_Rep:wave.cub 0.851415
## gov_proc.4:representation_Dem:wave.lin 0.002800
## gov_proc.4:representation_Dem:wave.quad 0.166806
## gov_proc.4:representation_Dem:wave.cub 0.947222
## gov_proc.4:representation_Rep:wave.lin 0.020407
## gov_proc.4:representation_Rep:wave.quad 0.306814
## gov_proc.4:representation_Rep:wave.cub 0.424600
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.198185
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 0.441618
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 0.659799
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 0.894778
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 0.003365
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 0.082893
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.422233
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 0.761413
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 0.227271
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 0.787474
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 0.159263
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 0.374264
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 0.054379
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 0.607649
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 0.229523
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.317442
##
## (Intercept) ***
## pDem_Rep
## gov_proc.4 **
## representation_Dem
## representation_Rep
## wave.lin
## wave.quad .
## wave.cub
## pDem_Rep:gov_proc.4 .
## representation_Dem:representation_Rep
## pDem_Rep:representation_Dem
## pDem_Rep:representation_Rep
## gov_proc.4:representation_Dem **
## gov_proc.4:representation_Rep ***
## pDem_Rep:wave.lin *
## pDem_Rep:wave.quad
## pDem_Rep:wave.cub
## gov_proc.4:wave.lin **
## gov_proc.4:wave.quad
## gov_proc.4:wave.cub
## representation_Dem:wave.lin
## representation_Dem:wave.quad
## representation_Dem:wave.cub
## representation_Rep:wave.lin
## representation_Rep:wave.quad
## representation_Rep:wave.cub
## pDem_Rep:representation_Dem:representation_Rep
## gov_proc.4:representation_Dem:representation_Rep ***
## pDem_Rep:gov_proc.4:representation_Dem
## pDem_Rep:gov_proc.4:representation_Rep .
## pDem_Rep:gov_proc.4:wave.lin
## pDem_Rep:gov_proc.4:wave.quad
## pDem_Rep:gov_proc.4:wave.cub
## representation_Dem:representation_Rep:wave.lin
## representation_Dem:representation_Rep:wave.quad
## representation_Dem:representation_Rep:wave.cub
## pDem_Rep:representation_Dem:wave.lin
## pDem_Rep:representation_Dem:wave.quad
## pDem_Rep:representation_Dem:wave.cub
## pDem_Rep:representation_Rep:wave.lin
## pDem_Rep:representation_Rep:wave.quad
## pDem_Rep:representation_Rep:wave.cub
## gov_proc.4:representation_Dem:wave.lin **
## gov_proc.4:representation_Dem:wave.quad
## gov_proc.4:representation_Dem:wave.cub
## gov_proc.4:representation_Rep:wave.lin *
## gov_proc.4:representation_Rep:wave.quad
## gov_proc.4:representation_Rep:wave.cub
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep
## pDem_Rep:representation_Dem:representation_Rep:wave.lin
## pDem_Rep:representation_Dem:representation_Rep:wave.quad
## pDem_Rep:representation_Dem:representation_Rep:wave.cub
## gov_proc.4:representation_Dem:representation_Rep:wave.lin **
## gov_proc.4:representation_Dem:representation_Rep:wave.quad .
## gov_proc.4:representation_Dem:representation_Rep:wave.cub
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub .
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1DN.m10.w4 <- lmer(demNorms ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) * (wave4_1 + wave4_2 + wave4_3) + (1|pid),
data = d)
summary(DN.m10.w4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## demNorms ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) *
## (wave4_1 + wave4_2 + wave4_3) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13859.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3691 -0.4690 0.0933 0.4914 4.2235
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 1.2646 1.1245
## Residual 0.7158 0.8461
## Number of obs: 4485, groups: pid, 1163
##
## Fixed effects:
## Estimate
## (Intercept) 1.738e+00
## pDem_Rep 6.951e-01
## gov_proc.4 -5.212e-01
## representation_Dem -4.762e-02
## representation_Rep -1.044e-01
## wave4_1 -1.031e-01
## wave4_2 2.725e-01
## wave4_3 2.022e-01
## pDem_Rep:gov_proc.4 9.347e-01
## representation_Dem:representation_Rep 1.956e-02
## pDem_Rep:representation_Dem -1.521e-01
## pDem_Rep:representation_Rep -2.252e-01
## gov_proc.4:representation_Dem 1.770e-01
## gov_proc.4:representation_Rep 2.455e-01
## pDem_Rep:wave4_1 -9.257e-01
## pDem_Rep:wave4_2 -5.604e-01
## pDem_Rep:wave4_3 -4.263e-01
## gov_proc.4:wave4_1 4.121e-01
## gov_proc.4:wave4_2 1.748e-01
## gov_proc.4:wave4_3 -2.131e-02
## representation_Dem:wave4_1 1.646e-02
## representation_Dem:wave4_2 -4.204e-02
## representation_Dem:wave4_3 -1.007e-01
## representation_Rep:wave4_1 3.619e-02
## representation_Rep:wave4_2 -3.547e-02
## representation_Rep:wave4_3 1.609e-03
## pDem_Rep:representation_Dem:representation_Rep 5.036e-02
## gov_proc.4:representation_Dem:representation_Rep -9.050e-02
## pDem_Rep:gov_proc.4:representation_Dem -1.899e-01
## pDem_Rep:gov_proc.4:representation_Rep -3.552e-01
## pDem_Rep:gov_proc.4:wave4_1 -5.814e-01
## pDem_Rep:gov_proc.4:wave4_2 -4.748e-01
## pDem_Rep:gov_proc.4:wave4_3 -7.548e-01
## representation_Dem:representation_Rep:wave4_1 -2.067e-02
## representation_Dem:representation_Rep:wave4_2 -1.312e-04
## representation_Dem:representation_Rep:wave4_3 1.362e-02
## pDem_Rep:representation_Dem:wave4_1 1.849e-01
## pDem_Rep:representation_Dem:wave4_2 6.200e-02
## pDem_Rep:representation_Dem:wave4_3 -5.318e-02
## pDem_Rep:representation_Rep:wave4_1 1.079e-01
## pDem_Rep:representation_Rep:wave4_2 1.439e-01
## pDem_Rep:representation_Rep:wave4_3 1.196e-01
## gov_proc.4:representation_Dem:wave4_1 -1.424e-01
## gov_proc.4:representation_Dem:wave4_2 -5.186e-02
## gov_proc.4:representation_Dem:wave4_3 1.517e-02
## gov_proc.4:representation_Rep:wave4_1 -1.417e-01
## gov_proc.4:representation_Rep:wave4_2 -3.799e-02
## gov_proc.4:representation_Rep:wave4_3 -1.945e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 6.724e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 -3.113e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 -1.154e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 1.138e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 5.046e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 9.091e-03
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 -5.019e-04
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 4.249e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 1.073e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 1.202e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 2.487e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 1.334e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 2.620e-01
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 -3.123e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 -3.291e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 -5.645e-02
## Std. Error
## (Intercept) 2.346e-01
## pDem_Rep 4.436e-01
## gov_proc.4 1.720e-01
## representation_Dem 8.887e-02
## representation_Rep 7.996e-02
## wave4_1 2.096e-01
## wave4_2 2.089e-01
## wave4_3 2.097e-01
## pDem_Rep:gov_proc.4 3.103e-01
## representation_Dem:representation_Rep 2.765e-02
## pDem_Rep:representation_Dem 1.691e-01
## pDem_Rep:representation_Rep 1.540e-01
## gov_proc.4:representation_Dem 5.730e-02
## gov_proc.4:representation_Rep 6.607e-02
## pDem_Rep:wave4_1 4.206e-01
## pDem_Rep:wave4_2 4.199e-01
## pDem_Rep:wave4_3 4.213e-01
## gov_proc.4:wave4_1 1.539e-01
## gov_proc.4:wave4_2 1.540e-01
## gov_proc.4:wave4_3 1.571e-01
## representation_Dem:wave4_1 7.982e-02
## representation_Dem:wave4_2 8.053e-02
## representation_Dem:wave4_3 8.126e-02
## representation_Rep:wave4_1 7.056e-02
## representation_Rep:wave4_2 7.040e-02
## representation_Rep:wave4_3 7.082e-02
## pDem_Rep:representation_Dem:representation_Rep 5.296e-02
## gov_proc.4:representation_Dem:representation_Rep 1.882e-02
## pDem_Rep:gov_proc.4:representation_Dem 1.039e-01
## pDem_Rep:gov_proc.4:representation_Rep 1.257e-01
## pDem_Rep:gov_proc.4:wave4_1 3.102e-01
## pDem_Rep:gov_proc.4:wave4_2 3.108e-01
## pDem_Rep:gov_proc.4:wave4_3 3.154e-01
## representation_Dem:representation_Rep:wave4_1 2.458e-02
## representation_Dem:representation_Rep:wave4_2 2.461e-02
## representation_Dem:representation_Rep:wave4_3 2.484e-02
## pDem_Rep:representation_Dem:wave4_1 1.602e-01
## pDem_Rep:representation_Dem:wave4_2 1.616e-01
## pDem_Rep:representation_Dem:wave4_3 1.629e-01
## pDem_Rep:representation_Rep:wave4_1 1.415e-01
## pDem_Rep:representation_Rep:wave4_2 1.412e-01
## pDem_Rep:representation_Rep:wave4_3 1.420e-01
## gov_proc.4:representation_Dem:wave4_1 5.140e-02
## gov_proc.4:representation_Dem:wave4_2 5.363e-02
## gov_proc.4:representation_Dem:wave4_3 5.477e-02
## gov_proc.4:representation_Rep:wave4_1 5.819e-02
## gov_proc.4:representation_Rep:wave4_2 5.834e-02
## gov_proc.4:representation_Rep:wave4_3 5.899e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 3.511e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 4.936e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 4.934e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 4.978e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 1.665e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 1.698e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 1.726e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 1.033e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 1.082e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 1.098e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 1.169e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 1.172e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 1.183e-01
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.346e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 3.418e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 3.466e-02
## df
## (Intercept) 2.241e+03
## pDem_Rep 3.046e+03
## gov_proc.4 2.256e+03
## representation_Dem 2.458e+03
## representation_Rep 2.268e+03
## wave4_1 3.337e+03
## wave4_2 3.324e+03
## wave4_3 3.338e+03
## pDem_Rep:gov_proc.4 3.778e+03
## representation_Dem:representation_Rep 2.372e+03
## pDem_Rep:representation_Dem 3.241e+03
## pDem_Rep:representation_Rep 2.817e+03
## gov_proc.4:representation_Dem 2.391e+03
## gov_proc.4:representation_Rep 2.373e+03
## pDem_Rep:wave4_1 3.342e+03
## pDem_Rep:wave4_2 3.331e+03
## pDem_Rep:wave4_3 3.335e+03
## gov_proc.4:wave4_1 3.339e+03
## gov_proc.4:wave4_2 3.339e+03
## gov_proc.4:wave4_3 3.378e+03
## representation_Dem:wave4_1 3.327e+03
## representation_Dem:wave4_2 3.314e+03
## representation_Dem:wave4_3 3.325e+03
## representation_Rep:wave4_1 3.321e+03
## representation_Rep:wave4_2 3.305e+03
## representation_Rep:wave4_3 3.315e+03
## pDem_Rep:representation_Dem:representation_Rep 3.038e+03
## gov_proc.4:representation_Dem:representation_Rep 2.380e+03
## pDem_Rep:gov_proc.4:representation_Dem 3.881e+03
## pDem_Rep:gov_proc.4:representation_Rep 3.100e+03
## pDem_Rep:gov_proc.4:wave4_1 3.347e+03
## pDem_Rep:gov_proc.4:wave4_2 3.340e+03
## pDem_Rep:gov_proc.4:wave4_3 3.344e+03
## representation_Dem:representation_Rep:wave4_1 3.322e+03
## representation_Dem:representation_Rep:wave4_2 3.300e+03
## representation_Dem:representation_Rep:wave4_3 3.309e+03
## pDem_Rep:representation_Dem:wave4_1 3.332e+03
## pDem_Rep:representation_Dem:wave4_2 3.316e+03
## pDem_Rep:representation_Dem:wave4_3 3.324e+03
## pDem_Rep:representation_Rep:wave4_1 3.324e+03
## pDem_Rep:representation_Rep:wave4_2 3.310e+03
## pDem_Rep:representation_Rep:wave4_3 3.313e+03
## gov_proc.4:representation_Dem:wave4_1 3.313e+03
## gov_proc.4:representation_Dem:wave4_2 3.347e+03
## gov_proc.4:representation_Dem:wave4_3 3.357e+03
## gov_proc.4:representation_Rep:wave4_1 3.315e+03
## gov_proc.4:representation_Rep:wave4_2 3.304e+03
## gov_proc.4:representation_Rep:wave4_3 3.326e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 3.390e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 3.326e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 3.302e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 3.309e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.315e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 3.318e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 3.330e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 3.318e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 3.335e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 3.341e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 3.320e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 3.305e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 3.310e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.314e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 3.312e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 3.319e+03
## t value
## (Intercept) 7.407
## pDem_Rep 1.567
## gov_proc.4 -3.030
## representation_Dem -0.536
## representation_Rep -1.306
## wave4_1 -0.492
## wave4_2 1.304
## wave4_3 0.964
## pDem_Rep:gov_proc.4 3.012
## representation_Dem:representation_Rep 0.707
## pDem_Rep:representation_Dem -0.900
## pDem_Rep:representation_Rep -1.462
## gov_proc.4:representation_Dem 3.089
## gov_proc.4:representation_Rep 3.715
## pDem_Rep:wave4_1 -2.201
## pDem_Rep:wave4_2 -1.335
## pDem_Rep:wave4_3 -1.012
## gov_proc.4:wave4_1 2.678
## gov_proc.4:wave4_2 1.135
## gov_proc.4:wave4_3 -0.136
## representation_Dem:wave4_1 0.206
## representation_Dem:wave4_2 -0.522
## representation_Dem:wave4_3 -1.239
## representation_Rep:wave4_1 0.513
## representation_Rep:wave4_2 -0.504
## representation_Rep:wave4_3 0.023
## pDem_Rep:representation_Dem:representation_Rep 0.951
## gov_proc.4:representation_Dem:representation_Rep -4.808
## pDem_Rep:gov_proc.4:representation_Dem -1.828
## pDem_Rep:gov_proc.4:representation_Rep -2.827
## pDem_Rep:gov_proc.4:wave4_1 -1.874
## pDem_Rep:gov_proc.4:wave4_2 -1.528
## pDem_Rep:gov_proc.4:wave4_3 -2.393
## representation_Dem:representation_Rep:wave4_1 -0.841
## representation_Dem:representation_Rep:wave4_2 -0.005
## representation_Dem:representation_Rep:wave4_3 0.548
## pDem_Rep:representation_Dem:wave4_1 1.154
## pDem_Rep:representation_Dem:wave4_2 0.384
## pDem_Rep:representation_Dem:wave4_3 -0.326
## pDem_Rep:representation_Rep:wave4_1 0.762
## pDem_Rep:representation_Rep:wave4_2 1.019
## pDem_Rep:representation_Rep:wave4_3 0.842
## gov_proc.4:representation_Dem:wave4_1 -2.770
## gov_proc.4:representation_Dem:wave4_2 -0.967
## gov_proc.4:representation_Dem:wave4_3 0.277
## gov_proc.4:representation_Rep:wave4_1 -2.435
## gov_proc.4:representation_Rep:wave4_2 -0.651
## gov_proc.4:representation_Rep:wave4_3 -0.330
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 1.915
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 -0.631
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 -0.234
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 0.229
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.030
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.535
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 -0.029
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 0.411
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 0.991
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 1.094
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 2.128
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 1.138
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 2.214
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 -0.933
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 -0.963
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 -1.629
## Pr(>|t|)
## (Intercept) 1.82e-13 ***
## pDem_Rep 0.117214
## gov_proc.4 0.002476 **
## representation_Dem 0.592116
## representation_Rep 0.191706
## wave4_1 0.622957
## wave4_2 0.192198
## wave4_3 0.335093
## pDem_Rep:gov_proc.4 0.002614 **
## representation_Dem:representation_Rep 0.479421
## pDem_Rep:representation_Dem 0.368340
## pDem_Rep:representation_Rep 0.143745
## gov_proc.4:representation_Dem 0.002031 **
## gov_proc.4:representation_Rep 0.000208 ***
## pDem_Rep:wave4_1 0.027820 *
## pDem_Rep:wave4_2 0.182079
## pDem_Rep:wave4_3 0.311625
## gov_proc.4:wave4_1 0.007448 **
## gov_proc.4:wave4_2 0.256482
## gov_proc.4:wave4_3 0.892109
## representation_Dem:wave4_1 0.836617
## representation_Dem:wave4_2 0.601657
## representation_Dem:wave4_3 0.215407
## representation_Rep:wave4_1 0.608032
## representation_Rep:wave4_2 0.614404
## representation_Rep:wave4_3 0.981879
## pDem_Rep:representation_Dem:representation_Rep 0.341775
## gov_proc.4:representation_Dem:representation_Rep 1.62e-06 ***
## pDem_Rep:gov_proc.4:representation_Dem 0.067609 .
## pDem_Rep:gov_proc.4:representation_Rep 0.004726 **
## pDem_Rep:gov_proc.4:wave4_1 0.061013 .
## pDem_Rep:gov_proc.4:wave4_2 0.126695
## pDem_Rep:gov_proc.4:wave4_3 0.016773 *
## representation_Dem:representation_Rep:wave4_1 0.400610
## representation_Dem:representation_Rep:wave4_2 0.995747
## representation_Dem:representation_Rep:wave4_3 0.583451
## pDem_Rep:representation_Dem:wave4_1 0.248513
## pDem_Rep:representation_Dem:wave4_2 0.701346
## pDem_Rep:representation_Dem:wave4_3 0.744106
## pDem_Rep:representation_Rep:wave4_1 0.445933
## pDem_Rep:representation_Rep:wave4_2 0.308451
## pDem_Rep:representation_Rep:wave4_3 0.399884
## gov_proc.4:representation_Dem:wave4_1 0.005633 **
## gov_proc.4:representation_Dem:wave4_2 0.333603
## gov_proc.4:representation_Dem:wave4_3 0.781745
## gov_proc.4:representation_Rep:wave4_1 0.014932 *
## gov_proc.4:representation_Rep:wave4_2 0.514956
## gov_proc.4:representation_Rep:wave4_3 0.741615
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.055557 .
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 0.528334
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 0.815019
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 0.819161
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 0.002463 **
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.592507
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 0.976808
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 0.680869
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 0.321635
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 0.273864
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 0.033410 *
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 0.255016
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 0.026878 *
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 0.350826
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.335658
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 0.103454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary( lmer(voteconfidence ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) * (wave.lin + wave.quad + wave.cub) + (1|pid),
data = d) )## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * gov_proc.4 * (representation_Dem *
## representation_Rep) * (wave.lin + wave.quad + wave.cub) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13704.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8632 -0.4915 0.0503 0.5441 3.8785
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7019 0.8378
## Residual 0.5223 0.7227
## Number of obs: 5068, groups: pid, 1267
##
## Fixed effects:
## Estimate
## (Intercept) 2.646e+00
## pDem_Rep -6.079e-01
## gov_proc.4 9.833e-02
## representation_Dem 2.621e-01
## representation_Rep 1.395e-01
## wave.lin 1.601e-01
## wave.quad 5.852e-01
## wave.cub -5.225e-02
## pDem_Rep:gov_proc.4 9.693e-02
## representation_Dem:representation_Rep -3.810e-02
## pDem_Rep:representation_Dem 1.529e-01
## pDem_Rep:representation_Rep 2.452e-01
## gov_proc.4:representation_Dem -2.892e-02
## gov_proc.4:representation_Rep -1.752e-02
## pDem_Rep:wave.lin 8.039e-01
## pDem_Rep:wave.quad 1.493e+00
## pDem_Rep:wave.cub -4.881e-01
## gov_proc.4:wave.lin -9.667e-02
## gov_proc.4:wave.quad 6.292e-02
## gov_proc.4:wave.cub 1.522e-01
## representation_Dem:wave.lin -1.497e-01
## representation_Dem:wave.quad -1.226e-01
## representation_Dem:wave.cub 1.681e-03
## representation_Rep:wave.lin 9.940e-02
## representation_Rep:wave.quad 8.048e-02
## representation_Rep:wave.cub -9.087e-02
## pDem_Rep:representation_Dem:representation_Rep -7.539e-02
## gov_proc.4:representation_Dem:representation_Rep 1.190e-02
## pDem_Rep:gov_proc.4:representation_Dem -5.639e-02
## pDem_Rep:gov_proc.4:representation_Rep -3.094e-02
## pDem_Rep:gov_proc.4:wave.lin 1.002e-02
## pDem_Rep:gov_proc.4:wave.quad -4.181e-01
## pDem_Rep:gov_proc.4:wave.cub 7.928e-02
## representation_Dem:representation_Rep:wave.lin 1.652e-02
## representation_Dem:representation_Rep:wave.quad -4.392e-03
## representation_Dem:representation_Rep:wave.cub 2.102e-02
## pDem_Rep:representation_Dem:wave.lin 6.396e-02
## pDem_Rep:representation_Dem:wave.quad -3.502e-01
## pDem_Rep:representation_Dem:wave.cub 1.966e-02
## pDem_Rep:representation_Rep:wave.lin 1.124e-01
## pDem_Rep:representation_Rep:wave.quad 3.613e-02
## pDem_Rep:representation_Rep:wave.cub -6.815e-02
## gov_proc.4:representation_Dem:wave.lin 3.790e-02
## gov_proc.4:representation_Dem:wave.quad 1.286e-02
## gov_proc.4:representation_Dem:wave.cub -6.257e-02
## gov_proc.4:representation_Rep:wave.lin 2.618e-02
## gov_proc.4:representation_Rep:wave.quad 5.580e-03
## gov_proc.4:representation_Rep:wave.cub -3.950e-03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 1.740e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.lin -2.746e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 4.304e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 7.831e-03
## gov_proc.4:representation_Dem:representation_Rep:wave.lin -1.421e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.quad -1.466e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 6.262e-03
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin -9.248e-03
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.860e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub -1.231e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 4.837e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 4.115e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub -1.927e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin -2.058e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad -2.845e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 2.458e-03
## Std. Error
## (Intercept) 1.378e-01
## pDem_Rep 2.703e-01
## gov_proc.4 9.890e-02
## representation_Dem 5.326e-02
## representation_Rep 4.756e-02
## wave.lin 1.404e-01
## wave.quad 2.233e-01
## wave.cub 1.419e-01
## pDem_Rep:gov_proc.4 1.835e-01
## representation_Dem:representation_Rep 1.673e-02
## pDem_Rep:representation_Dem 1.024e-01
## pDem_Rep:representation_Rep 9.370e-02
## gov_proc.4:representation_Dem 3.509e-02
## gov_proc.4:representation_Rep 3.784e-02
## pDem_Rep:wave.lin 3.168e-01
## pDem_Rep:wave.quad 5.010e-01
## pDem_Rep:wave.cub 3.174e-01
## gov_proc.4:wave.lin 1.008e-01
## gov_proc.4:wave.quad 1.607e-01
## gov_proc.4:wave.cub 1.024e-01
## representation_Dem:wave.lin 5.586e-02
## representation_Dem:wave.quad 8.914e-02
## representation_Dem:wave.cub 5.684e-02
## representation_Rep:wave.lin 4.883e-02
## representation_Rep:wave.quad 7.737e-02
## representation_Rep:wave.cub 4.908e-02
## pDem_Rep:representation_Dem:representation_Rep 3.229e-02
## gov_proc.4:representation_Dem:representation_Rep 1.129e-02
## pDem_Rep:gov_proc.4:representation_Dem 6.356e-02
## pDem_Rep:gov_proc.4:representation_Rep 7.324e-02
## pDem_Rep:gov_proc.4:wave.lin 2.341e-01
## pDem_Rep:gov_proc.4:wave.quad 3.686e-01
## pDem_Rep:gov_proc.4:wave.cub 2.358e-01
## representation_Dem:representation_Rep:wave.lin 1.741e-02
## representation_Dem:representation_Rep:wave.quad 2.765e-02
## representation_Dem:representation_Rep:wave.cub 1.757e-02
## pDem_Rep:representation_Dem:wave.lin 1.200e-01
## pDem_Rep:representation_Dem:wave.quad 1.910e-01
## pDem_Rep:representation_Dem:wave.cub 1.216e-01
## pDem_Rep:representation_Rep:wave.lin 1.061e-01
## pDem_Rep:representation_Rep:wave.quad 1.676e-01
## pDem_Rep:representation_Rep:wave.cub 1.061e-01
## gov_proc.4:representation_Dem:wave.lin 3.658e-02
## gov_proc.4:representation_Dem:wave.quad 5.930e-02
## gov_proc.4:representation_Dem:wave.cub 3.839e-02
## gov_proc.4:representation_Rep:wave.lin 3.957e-02
## gov_proc.4:representation_Rep:wave.quad 6.264e-02
## gov_proc.4:representation_Rep:wave.cub 3.968e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 2.110e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 3.695e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 5.858e-02
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 3.716e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 1.182e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 1.889e-02
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 1.208e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 7.945e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.281e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 8.374e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 8.577e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 1.352e-01
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 8.593e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 2.505e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 3.996e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 2.570e-02
## df
## (Intercept) 1.330e+03
## pDem_Rep 2.372e+03
## gov_proc.4 1.337e+03
## representation_Dem 1.488e+03
## representation_Rep 1.366e+03
## wave.lin 3.759e+03
## wave.quad 3.767e+03
## wave.cub 3.763e+03
## pDem_Rep:gov_proc.4 3.148e+03
## representation_Dem:representation_Rep 1.437e+03
## pDem_Rep:representation_Dem 2.421e+03
## pDem_Rep:representation_Rep 2.061e+03
## gov_proc.4:representation_Dem 1.496e+03
## gov_proc.4:representation_Rep 1.460e+03
## pDem_Rep:wave.lin 3.782e+03
## pDem_Rep:wave.quad 3.774e+03
## pDem_Rep:wave.cub 3.772e+03
## gov_proc.4:wave.lin 3.760e+03
## gov_proc.4:wave.quad 3.775e+03
## gov_proc.4:wave.cub 3.773e+03
## representation_Dem:wave.lin 3.767e+03
## representation_Dem:wave.quad 3.775e+03
## representation_Dem:wave.cub 3.777e+03
## representation_Rep:wave.lin 3.767e+03
## representation_Rep:wave.quad 3.759e+03
## representation_Rep:wave.cub 3.758e+03
## pDem_Rep:representation_Dem:representation_Rep 2.172e+03
## gov_proc.4:representation_Dem:representation_Rep 1.485e+03
## pDem_Rep:gov_proc.4:representation_Dem 3.167e+03
## pDem_Rep:gov_proc.4:representation_Rep 2.285e+03
## pDem_Rep:gov_proc.4:wave.lin 3.808e+03
## pDem_Rep:gov_proc.4:wave.quad 3.779e+03
## pDem_Rep:gov_proc.4:wave.cub 3.803e+03
## representation_Dem:representation_Rep:wave.lin 3.768e+03
## representation_Dem:representation_Rep:wave.quad 3.765e+03
## representation_Dem:representation_Rep:wave.cub 3.768e+03
## pDem_Rep:representation_Dem:wave.lin 3.782e+03
## pDem_Rep:representation_Dem:wave.quad 3.782e+03
## pDem_Rep:representation_Dem:wave.cub 3.777e+03
## pDem_Rep:representation_Rep:wave.lin 3.776e+03
## pDem_Rep:representation_Rep:wave.quad 3.767e+03
## pDem_Rep:representation_Rep:wave.cub 3.765e+03
## gov_proc.4:representation_Dem:wave.lin 3.770e+03
## gov_proc.4:representation_Dem:wave.quad 3.800e+03
## gov_proc.4:representation_Dem:wave.cub 3.796e+03
## gov_proc.4:representation_Rep:wave.lin 3.773e+03
## gov_proc.4:representation_Rep:wave.quad 3.764e+03
## gov_proc.4:representation_Rep:wave.cub 3.762e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 2.505e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 3.779e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 3.775e+03
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 3.769e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 3.779e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 3.781e+03
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 3.775e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 3.795e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 3.788e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 3.828e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 3.790e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 3.769e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 3.779e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 3.789e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 3.778e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 3.798e+03
## t value
## (Intercept) 19.196
## pDem_Rep -2.249
## gov_proc.4 0.994
## representation_Dem 4.922
## representation_Rep 2.934
## wave.lin 1.140
## wave.quad 2.621
## wave.cub -0.368
## pDem_Rep:gov_proc.4 0.528
## representation_Dem:representation_Rep -2.278
## pDem_Rep:representation_Dem 1.493
## pDem_Rep:representation_Rep 2.617
## gov_proc.4:representation_Dem -0.824
## gov_proc.4:representation_Rep -0.463
## pDem_Rep:wave.lin 2.537
## pDem_Rep:wave.quad 2.980
## pDem_Rep:wave.cub -1.538
## gov_proc.4:wave.lin -0.959
## gov_proc.4:wave.quad 0.391
## gov_proc.4:wave.cub 1.486
## representation_Dem:wave.lin -2.680
## representation_Dem:wave.quad -1.375
## representation_Dem:wave.cub 0.030
## representation_Rep:wave.lin 2.036
## representation_Rep:wave.quad 1.040
## representation_Rep:wave.cub -1.851
## pDem_Rep:representation_Dem:representation_Rep -2.335
## gov_proc.4:representation_Dem:representation_Rep 1.054
## pDem_Rep:gov_proc.4:representation_Dem -0.887
## pDem_Rep:gov_proc.4:representation_Rep -0.422
## pDem_Rep:gov_proc.4:wave.lin 0.043
## pDem_Rep:gov_proc.4:wave.quad -1.134
## pDem_Rep:gov_proc.4:wave.cub 0.336
## representation_Dem:representation_Rep:wave.lin 0.949
## representation_Dem:representation_Rep:wave.quad -0.159
## representation_Dem:representation_Rep:wave.cub 1.196
## pDem_Rep:representation_Dem:wave.lin 0.533
## pDem_Rep:representation_Dem:wave.quad -1.833
## pDem_Rep:representation_Dem:wave.cub 0.162
## pDem_Rep:representation_Rep:wave.lin 1.060
## pDem_Rep:representation_Rep:wave.quad 0.216
## pDem_Rep:representation_Rep:wave.cub -0.642
## gov_proc.4:representation_Dem:wave.lin 1.036
## gov_proc.4:representation_Dem:wave.quad 0.217
## gov_proc.4:representation_Dem:wave.cub -1.630
## gov_proc.4:representation_Rep:wave.lin 0.661
## gov_proc.4:representation_Rep:wave.quad 0.089
## gov_proc.4:representation_Rep:wave.cub -0.100
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.825
## pDem_Rep:representation_Dem:representation_Rep:wave.lin -0.743
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 0.735
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 0.211
## gov_proc.4:representation_Dem:representation_Rep:wave.lin -1.202
## gov_proc.4:representation_Dem:representation_Rep:wave.quad -0.776
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.518
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin -0.116
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 1.452
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub -0.147
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 0.564
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 0.304
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub -0.224
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin -0.822
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad -0.712
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.096
## Pr(>|t|)
## (Intercept) < 2e-16
## pDem_Rep 0.02461
## gov_proc.4 0.32026
## representation_Dem 9.52e-07
## representation_Rep 0.00340
## wave.lin 0.25416
## wave.quad 0.00881
## wave.cub 0.71282
## pDem_Rep:gov_proc.4 0.59732
## representation_Dem:representation_Rep 0.02290
## pDem_Rep:representation_Dem 0.13565
## pDem_Rep:representation_Rep 0.00893
## gov_proc.4:representation_Dem 0.41001
## gov_proc.4:representation_Rep 0.64337
## pDem_Rep:wave.lin 0.01120
## pDem_Rep:wave.quad 0.00290
## pDem_Rep:wave.cub 0.12411
## gov_proc.4:wave.lin 0.33770
## gov_proc.4:wave.quad 0.69551
## gov_proc.4:wave.cub 0.13735
## representation_Dem:wave.lin 0.00739
## representation_Dem:wave.quad 0.16919
## representation_Dem:wave.cub 0.97641
## representation_Rep:wave.lin 0.04185
## representation_Rep:wave.quad 0.29834
## representation_Rep:wave.cub 0.06418
## pDem_Rep:representation_Dem:representation_Rep 0.01965
## gov_proc.4:representation_Dem:representation_Rep 0.29227
## pDem_Rep:gov_proc.4:representation_Dem 0.37501
## pDem_Rep:gov_proc.4:representation_Rep 0.67272
## pDem_Rep:gov_proc.4:wave.lin 0.96586
## pDem_Rep:gov_proc.4:wave.quad 0.25684
## pDem_Rep:gov_proc.4:wave.cub 0.73676
## representation_Dem:representation_Rep:wave.lin 0.34268
## representation_Dem:representation_Rep:wave.quad 0.87382
## representation_Dem:representation_Rep:wave.cub 0.23167
## pDem_Rep:representation_Dem:wave.lin 0.59409
## pDem_Rep:representation_Dem:wave.quad 0.06682
## pDem_Rep:representation_Dem:wave.cub 0.87157
## pDem_Rep:representation_Rep:wave.lin 0.28927
## pDem_Rep:representation_Rep:wave.quad 0.82936
## pDem_Rep:representation_Rep:wave.cub 0.52077
## gov_proc.4:representation_Dem:wave.lin 0.30029
## gov_proc.4:representation_Dem:wave.quad 0.82839
## gov_proc.4:representation_Dem:wave.cub 0.10323
## gov_proc.4:representation_Rep:wave.lin 0.50838
## gov_proc.4:representation_Rep:wave.quad 0.92902
## gov_proc.4:representation_Rep:wave.cub 0.92071
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.40955
## pDem_Rep:representation_Dem:representation_Rep:wave.lin 0.45737
## pDem_Rep:representation_Dem:representation_Rep:wave.quad 0.46257
## pDem_Rep:representation_Dem:representation_Rep:wave.cub 0.83313
## gov_proc.4:representation_Dem:representation_Rep:wave.lin 0.22942
## gov_proc.4:representation_Dem:representation_Rep:wave.quad 0.43790
## gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.60418
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin 0.90734
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad 0.14666
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub 0.88316
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin 0.57284
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad 0.76085
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub 0.82257
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin 0.41133
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad 0.47658
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub 0.92381
##
## (Intercept) ***
## pDem_Rep *
## gov_proc.4
## representation_Dem ***
## representation_Rep **
## wave.lin
## wave.quad **
## wave.cub
## pDem_Rep:gov_proc.4
## representation_Dem:representation_Rep *
## pDem_Rep:representation_Dem
## pDem_Rep:representation_Rep **
## gov_proc.4:representation_Dem
## gov_proc.4:representation_Rep
## pDem_Rep:wave.lin *
## pDem_Rep:wave.quad **
## pDem_Rep:wave.cub
## gov_proc.4:wave.lin
## gov_proc.4:wave.quad
## gov_proc.4:wave.cub
## representation_Dem:wave.lin **
## representation_Dem:wave.quad
## representation_Dem:wave.cub
## representation_Rep:wave.lin *
## representation_Rep:wave.quad
## representation_Rep:wave.cub .
## pDem_Rep:representation_Dem:representation_Rep *
## gov_proc.4:representation_Dem:representation_Rep
## pDem_Rep:gov_proc.4:representation_Dem
## pDem_Rep:gov_proc.4:representation_Rep
## pDem_Rep:gov_proc.4:wave.lin
## pDem_Rep:gov_proc.4:wave.quad
## pDem_Rep:gov_proc.4:wave.cub
## representation_Dem:representation_Rep:wave.lin
## representation_Dem:representation_Rep:wave.quad
## representation_Dem:representation_Rep:wave.cub
## pDem_Rep:representation_Dem:wave.lin
## pDem_Rep:representation_Dem:wave.quad .
## pDem_Rep:representation_Dem:wave.cub
## pDem_Rep:representation_Rep:wave.lin
## pDem_Rep:representation_Rep:wave.quad
## pDem_Rep:representation_Rep:wave.cub
## gov_proc.4:representation_Dem:wave.lin
## gov_proc.4:representation_Dem:wave.quad
## gov_proc.4:representation_Dem:wave.cub
## gov_proc.4:representation_Rep:wave.lin
## gov_proc.4:representation_Rep:wave.quad
## gov_proc.4:representation_Rep:wave.cub
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep
## pDem_Rep:representation_Dem:representation_Rep:wave.lin
## pDem_Rep:representation_Dem:representation_Rep:wave.quad
## pDem_Rep:representation_Dem:representation_Rep:wave.cub
## gov_proc.4:representation_Dem:representation_Rep:wave.lin
## gov_proc.4:representation_Dem:representation_Rep:wave.quad
## gov_proc.4:representation_Dem:representation_Rep:wave.cub
## pDem_Rep:gov_proc.4:representation_Dem:wave.lin
## pDem_Rep:gov_proc.4:representation_Dem:wave.quad
## pDem_Rep:gov_proc.4:representation_Dem:wave.cub
## pDem_Rep:gov_proc.4:representation_Rep:wave.lin
## pDem_Rep:gov_proc.4:representation_Rep:wave.quad
## pDem_Rep:gov_proc.4:representation_Rep:wave.cub
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.lin
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.quad
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave.cub
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary( lmer(voteconfidence ~ (pDem_Rep) * gov_proc.4 * (representation_Dem * representation_Rep) * (wave4_1 + wave4_2 + wave4_3) + (1|pid),
data = d) )## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: voteconfidence ~ (pDem_Rep) * gov_proc.4 * (representation_Dem *
## representation_Rep) * (wave4_1 + wave4_2 + wave4_3) + (1 | pid)
## Data: d
##
## REML criterion at convergence: 13719.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8632 -0.4915 0.0503 0.5441 3.8785
##
## Random effects:
## Groups Name Variance Std.Dev.
## pid (Intercept) 0.7019 0.8378
## Residual 0.5223 0.7227
## Number of obs: 5068, groups: pid, 1267
##
## Fixed effects:
## Estimate
## (Intercept) 2.593e+00
## pDem_Rep -4.571e-01
## gov_proc.4 -3.776e-03
## representation_Dem 2.175e-01
## representation_Rep 1.918e-01
## wave4_1 -1.862e-01
## wave4_2 1.856e-01
## wave4_3 2.134e-01
## pDem_Rep:gov_proc.4 1.866e-01
## representation_Dem:representation_Rep -3.399e-02
## pDem_Rep:representation_Dem 2.675e-01
## pDem_Rep:representation_Rep 3.094e-01
## gov_proc.4:representation_Dem 2.453e-03
## gov_proc.4:representation_Rep -4.845e-03
## pDem_Rep:wave4_1 -1.048e+00
## pDem_Rep:wave4_2 2.656e-01
## pDem_Rep:wave4_3 1.794e-01
## gov_proc.4:wave4_1 1.728e-01
## gov_proc.4:wave4_2 6.591e-02
## gov_proc.4:wave4_3 1.698e-01
## representation_Dem:wave4_1 1.505e-01
## representation_Dem:wave4_2 5.057e-02
## representation_Dem:wave4_3 -2.260e-02
## representation_Rep:wave4_1 -1.448e-01
## representation_Rep:wave4_2 -1.159e-02
## representation_Rep:wave4_3 -5.276e-02
## pDem_Rep:representation_Dem:representation_Rep -1.018e-01
## gov_proc.4:representation_Dem:representation_Rep 6.889e-03
## pDem_Rep:gov_proc.4:representation_Dem -1.044e-01
## pDem_Rep:gov_proc.4:representation_Rep -1.223e-02
## pDem_Rep:gov_proc.4:wave4_1 2.962e-02
## pDem_Rep:gov_proc.4:wave4_2 -2.364e-01
## pDem_Rep:gov_proc.4:wave4_3 -1.521e-01
## representation_Dem:representation_Rep:wave4_1 -6.015e-03
## representation_Dem:representation_Rep:wave4_2 -1.984e-02
## representation_Dem:representation_Rep:wave4_3 9.437e-03
## pDem_Rep:representation_Dem:wave4_1 -5.413e-02
## pDem_Rep:representation_Dem:wave4_2 -2.280e-01
## pDem_Rep:representation_Dem:wave4_3 -1.764e-01
## pDem_Rep:representation_Rep:wave4_1 -1.465e-01
## pDem_Rep:representation_Rep:wave4_2 -4.922e-02
## pDem_Rep:representation_Rep:wave4_3 -6.115e-02
## gov_proc.4:representation_Dem:wave4_1 -6.918e-02
## gov_proc.4:representation_Dem:wave4_2 -6.352e-03
## gov_proc.4:representation_Dem:wave4_3 -4.997e-02
## gov_proc.4:representation_Rep:wave4_1 -2.815e-02
## gov_proc.4:representation_Rep:wave4_2 -1.585e-02
## gov_proc.4:representation_Rep:wave4_3 -6.716e-03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 1.361e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 3.138e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 4.016e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 3.426e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 1.734e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 1.765e-03
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 9.206e-04
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 3.094e-03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 1.030e-01
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 8.608e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 -5.800e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 -1.088e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 -5.966e-03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 2.181e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 6.008e-04
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 -7.234e-03
## Std. Error
## (Intercept) 1.674e-01
## pDem_Rep 3.416e-01
## gov_proc.4 1.199e-01
## representation_Dem 6.538e-02
## representation_Rep 5.843e-02
## wave4_1 1.564e-01
## wave4_2 1.582e-01
## wave4_3 1.577e-01
## pDem_Rep:gov_proc.4 2.400e-01
## representation_Dem:representation_Rep 2.057e-02
## pDem_Rep:representation_Dem 1.301e-01
## pDem_Rep:representation_Rep 1.180e-01
## gov_proc.4:representation_Dem 4.218e-02
## gov_proc.4:representation_Rep 4.692e-02
## pDem_Rep:wave4_1 3.537e-01
## pDem_Rep:wave4_2 3.532e-01
## pDem_Rep:wave4_3 3.538e-01
## gov_proc.4:wave4_1 1.121e-01
## gov_proc.4:wave4_2 1.136e-01
## gov_proc.4:wave4_3 1.136e-01
## representation_Dem:wave4_1 6.212e-02
## representation_Dem:wave4_2 6.340e-02
## representation_Dem:wave4_3 6.289e-02
## representation_Rep:wave4_1 5.449e-02
## representation_Rep:wave4_2 5.487e-02
## representation_Rep:wave4_3 5.501e-02
## pDem_Rep:representation_Dem:representation_Rep 4.080e-02
## gov_proc.4:representation_Dem:representation_Rep 1.382e-02
## pDem_Rep:gov_proc.4:representation_Dem 8.120e-02
## pDem_Rep:gov_proc.4:representation_Rep 9.405e-02
## pDem_Rep:gov_proc.4:wave4_1 2.603e-01
## pDem_Rep:gov_proc.4:wave4_2 2.611e-01
## pDem_Rep:gov_proc.4:wave4_3 2.638e-01
## representation_Dem:representation_Rep:wave4_1 1.941e-02
## representation_Dem:representation_Rep:wave4_2 1.962e-02
## representation_Dem:representation_Rep:wave4_3 1.958e-02
## pDem_Rep:representation_Dem:wave4_1 1.336e-01
## pDem_Rep:representation_Dem:wave4_2 1.350e-01
## pDem_Rep:representation_Dem:wave4_3 1.349e-01
## pDem_Rep:representation_Rep:wave4_1 1.185e-01
## pDem_Rep:representation_Rep:wave4_2 1.183e-01
## pDem_Rep:representation_Rep:wave4_3 1.189e-01
## gov_proc.4:representation_Dem:wave4_1 4.019e-02
## gov_proc.4:representation_Dem:wave4_2 4.221e-02
## gov_proc.4:representation_Dem:wave4_3 4.145e-02
## gov_proc.4:representation_Rep:wave4_1 4.417e-02
## gov_proc.4:representation_Rep:wave4_2 4.428e-02
## gov_proc.4:representation_Rep:wave4_3 4.470e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 2.683e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 4.124e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 4.131e-02
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 4.142e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 1.311e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 1.338e-02
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 1.336e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 8.705e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 9.103e-02
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 9.065e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 9.568e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 9.588e-02
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 9.677e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 2.774e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 2.832e-02
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 2.843e-02
## df
## (Intercept) 2.668e+03
## pDem_Rep 4.292e+03
## gov_proc.4 2.669e+03
## representation_Dem 2.985e+03
## representation_Rep 2.761e+03
## wave4_1 3.759e+03
## wave4_2 3.766e+03
## wave4_3 3.767e+03
## pDem_Rep:gov_proc.4 4.853e+03
## representation_Dem:representation_Rep 2.896e+03
## pDem_Rep:representation_Dem 4.307e+03
## pDem_Rep:representation_Rep 3.915e+03
## gov_proc.4:representation_Dem 2.909e+03
## gov_proc.4:representation_Rep 2.945e+03
## pDem_Rep:wave4_1 3.782e+03
## pDem_Rep:wave4_2 3.779e+03
## pDem_Rep:wave4_3 3.780e+03
## gov_proc.4:wave4_1 3.755e+03
## gov_proc.4:wave4_2 3.774e+03
## gov_proc.4:wave4_3 3.778e+03
## representation_Dem:wave4_1 3.767e+03
## representation_Dem:wave4_2 3.770e+03
## representation_Dem:wave4_3 3.769e+03
## representation_Rep:wave4_1 3.767e+03
## representation_Rep:wave4_2 3.760e+03
## representation_Rep:wave4_3 3.759e+03
## pDem_Rep:representation_Dem:representation_Rep 4.032e+03
## gov_proc.4:representation_Dem:representation_Rep 2.942e+03
## pDem_Rep:gov_proc.4:representation_Dem 4.815e+03
## pDem_Rep:gov_proc.4:representation_Rep 4.199e+03
## pDem_Rep:gov_proc.4:wave4_1 3.803e+03
## pDem_Rep:gov_proc.4:wave4_2 3.805e+03
## pDem_Rep:gov_proc.4:wave4_3 3.803e+03
## representation_Dem:representation_Rep:wave4_1 3.769e+03
## representation_Dem:representation_Rep:wave4_2 3.762e+03
## representation_Dem:representation_Rep:wave4_3 3.762e+03
## pDem_Rep:representation_Dem:wave4_1 3.784e+03
## pDem_Rep:representation_Dem:wave4_2 3.777e+03
## pDem_Rep:representation_Dem:wave4_3 3.777e+03
## pDem_Rep:representation_Rep:wave4_1 3.778e+03
## pDem_Rep:representation_Rep:wave4_2 3.769e+03
## pDem_Rep:representation_Rep:wave4_3 3.770e+03
## gov_proc.4:representation_Dem:wave4_1 3.756e+03
## gov_proc.4:representation_Dem:wave4_2 3.802e+03
## gov_proc.4:representation_Dem:wave4_3 3.791e+03
## gov_proc.4:representation_Rep:wave4_1 3.766e+03
## gov_proc.4:representation_Rep:wave4_2 3.769e+03
## gov_proc.4:representation_Rep:wave4_3 3.764e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 4.397e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 3.782e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 3.768e+03
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 3.770e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.771e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 3.787e+03
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 3.778e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 3.781e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 3.813e+03
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 3.801e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 3.787e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 3.783e+03
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 3.778e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 3.783e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 3.794e+03
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 3.788e+03
## t value
## (Intercept) 15.486
## pDem_Rep -1.338
## gov_proc.4 -0.031
## representation_Dem 3.327
## representation_Rep 3.283
## wave4_1 -1.191
## wave4_2 1.173
## wave4_3 1.353
## pDem_Rep:gov_proc.4 0.778
## representation_Dem:representation_Rep -1.653
## pDem_Rep:representation_Dem 2.057
## pDem_Rep:representation_Rep 2.622
## gov_proc.4:representation_Dem 0.058
## gov_proc.4:representation_Rep -0.103
## pDem_Rep:wave4_1 -2.963
## pDem_Rep:wave4_2 0.752
## pDem_Rep:wave4_3 0.507
## gov_proc.4:wave4_1 1.541
## gov_proc.4:wave4_2 0.580
## gov_proc.4:wave4_3 1.494
## representation_Dem:wave4_1 2.423
## representation_Dem:wave4_2 0.798
## representation_Dem:wave4_3 -0.359
## representation_Rep:wave4_1 -2.658
## representation_Rep:wave4_2 -0.211
## representation_Rep:wave4_3 -0.959
## pDem_Rep:representation_Dem:representation_Rep -2.496
## gov_proc.4:representation_Dem:representation_Rep 0.498
## pDem_Rep:gov_proc.4:representation_Dem -1.286
## pDem_Rep:gov_proc.4:representation_Rep -0.130
## pDem_Rep:gov_proc.4:wave4_1 0.114
## pDem_Rep:gov_proc.4:wave4_2 -0.905
## pDem_Rep:gov_proc.4:wave4_3 -0.576
## representation_Dem:representation_Rep:wave4_1 -0.310
## representation_Dem:representation_Rep:wave4_2 -1.012
## representation_Dem:representation_Rep:wave4_3 0.482
## pDem_Rep:representation_Dem:wave4_1 -0.405
## pDem_Rep:representation_Dem:wave4_2 -1.689
## pDem_Rep:representation_Dem:wave4_3 -1.308
## pDem_Rep:representation_Rep:wave4_1 -1.236
## pDem_Rep:representation_Rep:wave4_2 -0.416
## pDem_Rep:representation_Rep:wave4_3 -0.514
## gov_proc.4:representation_Dem:wave4_1 -1.721
## gov_proc.4:representation_Dem:wave4_2 -0.150
## gov_proc.4:representation_Dem:wave4_3 -1.205
## gov_proc.4:representation_Rep:wave4_1 -0.637
## gov_proc.4:representation_Rep:wave4_2 -0.358
## gov_proc.4:representation_Rep:wave4_3 -0.150
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.507
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 0.761
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 0.972
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 0.827
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 1.323
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.132
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 0.069
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 0.036
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 1.132
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 0.950
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 -0.606
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 -0.113
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 -0.062
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 0.786
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.021
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 -0.254
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## pDem_Rep 0.18095
## gov_proc.4 0.97489
## representation_Dem 0.00089 ***
## representation_Rep 0.00104 **
## wave4_1 0.23382
## wave4_2 0.24088
## wave4_3 0.17613
## pDem_Rep:gov_proc.4 0.43688
## representation_Dem:representation_Rep 0.09845 .
## pDem_Rep:representation_Dem 0.03976 *
## pDem_Rep:representation_Rep 0.00877 **
## gov_proc.4:representation_Dem 0.95361
## gov_proc.4:representation_Rep 0.91777
## pDem_Rep:wave4_1 0.00306 **
## pDem_Rep:wave4_2 0.45217
## pDem_Rep:wave4_3 0.61210
## gov_proc.4:wave4_1 0.12342
## gov_proc.4:wave4_2 0.56184
## gov_proc.4:wave4_3 0.13532
## representation_Dem:wave4_1 0.01543 *
## representation_Dem:wave4_2 0.42515
## representation_Dem:wave4_3 0.71932
## representation_Rep:wave4_1 0.00789 **
## representation_Rep:wave4_2 0.83267
## representation_Rep:wave4_3 0.33756
## pDem_Rep:representation_Dem:representation_Rep 0.01260 *
## gov_proc.4:representation_Dem:representation_Rep 0.61826
## pDem_Rep:gov_proc.4:representation_Dem 0.19844
## pDem_Rep:gov_proc.4:representation_Rep 0.89656
## pDem_Rep:gov_proc.4:wave4_1 0.90942
## pDem_Rep:gov_proc.4:wave4_2 0.36534
## pDem_Rep:gov_proc.4:wave4_3 0.56432
## representation_Dem:representation_Rep:wave4_1 0.75670
## representation_Dem:representation_Rep:wave4_2 0.31180
## representation_Dem:representation_Rep:wave4_3 0.62984
## pDem_Rep:representation_Dem:wave4_1 0.68545
## pDem_Rep:representation_Dem:wave4_2 0.09128 .
## pDem_Rep:representation_Dem:wave4_3 0.19101
## pDem_Rep:representation_Rep:wave4_1 0.21649
## pDem_Rep:representation_Rep:wave4_2 0.67745
## pDem_Rep:representation_Rep:wave4_3 0.60700
## gov_proc.4:representation_Dem:wave4_1 0.08529 .
## gov_proc.4:representation_Dem:wave4_2 0.88038
## gov_proc.4:representation_Dem:wave4_3 0.22810
## gov_proc.4:representation_Rep:wave4_1 0.52392
## gov_proc.4:representation_Rep:wave4_2 0.72034
## gov_proc.4:representation_Rep:wave4_3 0.88058
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep 0.61199
## pDem_Rep:representation_Dem:representation_Rep:wave4_1 0.44681
## pDem_Rep:representation_Dem:representation_Rep:wave4_2 0.33099
## pDem_Rep:representation_Dem:representation_Rep:wave4_3 0.40820
## gov_proc.4:representation_Dem:representation_Rep:wave4_1 0.18601
## gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.89508
## gov_proc.4:representation_Dem:representation_Rep:wave4_3 0.94508
## pDem_Rep:gov_proc.4:representation_Dem:wave4_1 0.97165
## pDem_Rep:gov_proc.4:representation_Dem:wave4_2 0.25788
## pDem_Rep:gov_proc.4:representation_Dem:wave4_3 0.34239
## pDem_Rep:gov_proc.4:representation_Rep:wave4_1 0.54441
## pDem_Rep:gov_proc.4:representation_Rep:wave4_2 0.90965
## pDem_Rep:gov_proc.4:representation_Rep:wave4_3 0.95084
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_1 0.43174
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_2 0.98308
## pDem_Rep:gov_proc.4:representation_Dem:representation_Rep:wave4_3 0.79917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 110. Full Model
DN.m11 <- lmer(demNorms ~ pDem_Rep
* wave
* trustGovt.c
* (Pos_Emo.c + Neg_Emo.c)
* FT_Diff.100
+ representation_Dem
+ representation_Rep
+ GCB.c
+ pollAcc
+ pollFreq.c
+ vs_age
+ vs_race
+ male_female
+ nonbinary_mf
+ (1 | pid),
data = d[d$party_factor != "Independent",])
sjPlot::tab_model(PEL.m11)| voteconfidence | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.73 | 2.37 – 3.09 | <0.001 |
| pDem Rep | -0.69 | -0.88 – -0.49 | <0.001 |
| wave [2] | 0.20 | 0.08 – 0.33 | 0.001 |
| wave [3] | 0.20 | 0.07 – 0.32 | 0.002 |
| wave [4] | 0.33 | 0.20 – 0.46 | <0.001 |
| trustGovt c | 0.24 | 0.15 – 0.33 | <0.001 |
| Pos Emo c | 0.20 | 0.12 – 0.28 | <0.001 |
| Neg Emo c | -0.02 | -0.11 – 0.06 | 0.568 |
| FT Diff 100 | -0.17 | -0.33 – -0.01 | 0.043 |
| govt process | 0.04 | -0.00 – 0.08 | 0.056 |
| representation Dem | 0.05 | 0.01 – 0.10 | 0.014 |
| representation Rep | 0.04 | 0.00 – 0.08 | 0.039 |
| GCB c | -0.23 | -0.28 – -0.18 | <0.001 |
| pollAcc | 0.04 | 0.02 – 0.06 | <0.001 |
| pollFreq c | -0.00 | -0.02 – 0.02 | 0.756 |
| vs age | 0.01 | 0.00 – 0.01 | 0.003 |
| vs race [Black] | -0.33 | -0.49 – -0.17 | <0.001 |
| vs race [Hispanic] | 0.03 | -0.13 – 0.20 | 0.683 |
| vs race [Other] | -0.00 | -0.18 – 0.18 | 1.000 |
| male female | -0.26 | -0.35 – -0.16 | <0.001 |
| nonbinary mf | -0.03 | -0.74 – 0.68 | 0.939 |
| pDem Rep × wave [2] | 0.51 | 0.25 – 0.76 | <0.001 |
| pDem Rep × wave [3] | 0.62 | 0.37 – 0.87 | <0.001 |
| pDem Rep × wave [4] | 0.64 | 0.38 – 0.89 | <0.001 |
| pDem Rep × trustGovt c | 0.22 | 0.04 – 0.40 | 0.016 |
| wave [2] × trustGovt c | -0.12 | -0.24 – -0.01 | 0.037 |
| wave [3] × trustGovt c | -0.09 | -0.21 – 0.04 | 0.185 |
| wave [4] × trustGovt c | -0.02 | -0.15 – 0.11 | 0.787 |
| pDem Rep × Pos Emo c | -0.08 | -0.24 – 0.08 | 0.346 |
| pDem Rep × Neg Emo c | -0.06 | -0.23 – 0.11 | 0.473 |
| wave [2] × Pos Emo c | -0.06 | -0.17 – 0.05 | 0.281 |
| wave [3] × Pos Emo c | -0.15 | -0.25 – -0.04 | 0.008 |
| wave [4] × Pos Emo c | -0.19 | -0.30 – -0.08 | 0.001 |
| wave [2] × Neg Emo c | -0.07 | -0.18 – 0.04 | 0.223 |
| wave [3] × Neg Emo c | -0.08 | -0.19 – 0.03 | 0.152 |
| wave [4] × Neg Emo c | 0.03 | -0.08 – 0.14 | 0.542 |
| trustGovt c × Pos Emo c | -0.07 | -0.14 – -0.01 | 0.031 |
| trustGovt c × Neg Emo c | 0.00 | -0.08 – 0.08 | 0.953 |
| pDem Rep × FT Diff 100 | -0.71 | -1.04 – -0.38 | <0.001 |
| wave [2] × FT Diff 100 | 0.09 | -0.14 – 0.32 | 0.436 |
| wave [3] × FT Diff 100 | 0.18 | -0.04 – 0.40 | 0.110 |
| wave [4] × FT Diff 100 | -0.07 | -0.29 – 0.16 | 0.562 |
| trustGovt c × FT Diff 100 | -0.06 | -0.21 – 0.09 | 0.436 |
| Pos Emo c × FT Diff 100 | -0.02 | -0.15 – 0.10 | 0.693 |
| Neg Emo c × FT Diff 100 | -0.04 | -0.17 – 0.10 | 0.610 |
|
(pDem Rep × wave [2]) × trustGovt c |
-0.14 | -0.37 – 0.09 | 0.247 |
|
(pDem Rep × wave [3]) × trustGovt c |
-0.21 | -0.47 – 0.04 | 0.100 |
|
(pDem Rep × wave [4]) × trustGovt c |
-0.26 | -0.51 – 0.00 | 0.051 |
|
(pDem Rep × wave [2]) × Pos Emo c |
0.14 | -0.08 – 0.36 | 0.214 |
|
(pDem Rep × wave [3]) × Pos Emo c |
0.24 | 0.02 – 0.45 | 0.030 |
|
(pDem Rep × wave [4]) × Pos Emo c |
0.03 | -0.20 – 0.25 | 0.823 |
|
(pDem Rep × wave [2]) × Neg Emo c |
0.02 | -0.21 – 0.24 | 0.876 |
|
(pDem Rep × wave [3]) × Neg Emo c |
0.08 | -0.15 – 0.30 | 0.507 |
|
(pDem Rep × wave [4]) × Neg Emo c |
0.00 | -0.22 – 0.22 | 0.986 |
|
(pDem Rep × trustGovt c) × Pos Emo c |
0.07 | -0.06 – 0.20 | 0.290 |
|
(pDem Rep × trustGovt c) × Neg Emo c |
0.11 | -0.05 – 0.26 | 0.187 |
|
(wave [2] × trustGovt c) × Pos Emo c |
0.09 | -0.00 – 0.19 | 0.051 |
|
(wave [3] × trustGovt c) × Pos Emo c |
0.12 | 0.03 – 0.21 | 0.007 |
|
(wave [4] × trustGovt c) × Pos Emo c |
0.08 | -0.01 – 0.17 | 0.074 |
|
(wave [2] × trustGovt c) × Neg Emo c |
0.05 | -0.05 – 0.15 | 0.325 |
|
(wave [3] × trustGovt c) × Neg Emo c |
0.03 | -0.07 – 0.14 | 0.510 |
|
(wave [4] × trustGovt c) × Neg Emo c |
0.02 | -0.08 – 0.13 | 0.689 |
|
(pDem Rep × wave [2]) × FT Diff 100 |
0.39 | -0.06 – 0.85 | 0.089 |
|
(pDem Rep × wave [3]) × FT Diff 100 |
0.67 | 0.23 – 1.10 | 0.003 |
|
(pDem Rep × wave [4]) × FT Diff 100 |
0.66 | 0.21 – 1.11 | 0.004 |
|
(pDem Rep × trustGovt c) × FT Diff 100 |
0.17 | -0.12 – 0.47 | 0.256 |
|
(wave [2] × trustGovt c) × FT Diff 100 |
0.09 | -0.11 – 0.29 | 0.384 |
|
(wave [3] × trustGovt c) × FT Diff 100 |
-0.05 | -0.27 – 0.17 | 0.631 |
|
(wave [4] × trustGovt c) × FT Diff 100 |
-0.08 | -0.30 – 0.15 | 0.505 |
|
(pDem Rep × Pos Emo c) × FT Diff 100 |
0.09 | -0.16 – 0.34 | 0.479 |
|
(pDem Rep × Neg Emo c) × FT Diff 100 |
-0.04 | -0.31 – 0.23 | 0.759 |
|
(wave [2] × Pos Emo c) × FT Diff 100 |
0.20 | 0.03 – 0.38 | 0.022 |
|
(wave [3] × Pos Emo c) × FT Diff 100 |
0.13 | -0.04 – 0.29 | 0.148 |
|
(wave [4] × Pos Emo c) × FT Diff 100 |
0.17 | -0.00 – 0.34 | 0.055 |
|
(wave [2] × Neg Emo c) × FT Diff 100 |
0.02 | -0.16 – 0.20 | 0.817 |
|
(wave [3] × Neg Emo c) × FT Diff 100 |
0.15 | -0.03 – 0.32 | 0.104 |
|
(wave [4] × Neg Emo c) × FT Diff 100 |
-0.07 | -0.25 – 0.11 | 0.454 |
|
(trustGovt c × Pos Emo c) × FT Diff 100 |
0.07 | -0.04 – 0.17 | 0.209 |
|
(trustGovt c × Neg Emo c) × FT Diff 100 |
-0.01 | -0.13 – 0.10 | 0.828 |
|
(pDem Rep × wave [2] × trustGovt c) × Pos Emo c |
-0.11 | -0.31 – 0.08 | 0.239 |
|
(pDem Rep × wave [3] × trustGovt c) × Pos Emo c |
-0.11 | -0.30 – 0.07 | 0.215 |
|
(pDem Rep × wave [4] × trustGovt c) × Pos Emo c |
-0.02 | -0.20 – 0.16 | 0.826 |
|
(pDem Rep × wave [2] × trustGovt c) × Neg Emo c |
-0.07 | -0.27 – 0.13 | 0.495 |
|
(pDem Rep × wave [3] × trustGovt c) × Neg Emo c |
-0.06 | -0.27 – 0.14 | 0.533 |
|
(pDem Rep × wave [4] × trustGovt c) × Neg Emo c |
-0.05 | -0.26 – 0.16 | 0.669 |
|
(pDem Rep × wave [2] × trustGovt c) × FT Diff 100 |
-0.13 | -0.53 – 0.27 | 0.529 |
|
(pDem Rep × wave [3] × trustGovt c) × FT Diff 100 |
-0.26 | -0.70 – 0.18 | 0.246 |
|
(pDem Rep × wave [4] × trustGovt c) × FT Diff 100 |
-0.16 | -0.61 – 0.29 | 0.483 |
|
(pDem Rep × wave [2] × Pos Emo c) × FT Diff 100 |
-0.07 | -0.41 – 0.28 | 0.714 |
|
(pDem Rep × wave [3] × Pos Emo c) × FT Diff 100 |
-0.09 | -0.43 – 0.25 | 0.590 |
|
(pDem Rep × wave [4] × Pos Emo c) × FT Diff 100 |
0.14 | -0.21 – 0.48 | 0.433 |
|
(pDem Rep × wave [2] × Neg Emo c) × FT Diff 100 |
-0.12 | -0.47 – 0.24 | 0.514 |
|
(pDem Rep × wave [3] × Neg Emo c) × FT Diff 100 |
0.14 | -0.22 – 0.49 | 0.448 |
|
(pDem Rep × wave [4] × Neg Emo c) × FT Diff 100 |
0.18 | -0.18 – 0.55 | 0.327 |
|
(pDem Rep × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.09 | -0.30 – 0.12 | 0.396 |
|
(pDem Rep × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.14 | -0.37 – 0.09 | 0.238 |
|
(wave [2] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.15 | -0.30 – -0.00 | 0.043 |
|
(wave [3] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.13 | -0.27 – 0.02 | 0.080 |
|
(wave [4] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.04 | -0.18 – 0.10 | 0.548 |
|
(wave [2] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.05 | -0.20 – 0.11 | 0.556 |
|
(wave [3] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.07 | -0.23 – 0.08 | 0.361 |
|
(wave [4] × trustGovt c × Neg Emo c) × FT Diff 100 |
0.02 | -0.14 – 0.19 | 0.796 |
|
(pDem Rep × wave [2] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.14 | -0.15 – 0.44 | 0.341 |
|
(pDem Rep × wave [3] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.18 | -0.11 – 0.46 | 0.232 |
|
(pDem Rep × wave [4] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.02 | -0.30 – 0.26 | 0.887 |
|
(pDem Rep × wave [2] × trustGovt c × Neg Emo c) × FT Diff 100 |
0.12 | -0.19 – 0.44 | 0.438 |
|
(pDem Rep × wave [3] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.05 | -0.36 – 0.26 | 0.770 |
|
(pDem Rep × wave [4] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.04 | -0.37 – 0.29 | 0.809 |
| Random Effects | |||
| σ2 | 0.45 | ||
| τ00 pid | 0.51 | ||
| ICC | 0.53 | ||
| N pid | 1159 | ||
| Observations | 4423 | ||
| Marginal R2 / Conditional R2 | 0.272 / 0.659 | ||
| demNorms | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.38 | 0.93 – 1.83 | <0.001 |
| pDem Rep | -0.25 | -0.51 – 0.01 | 0.058 |
| wave [2] | 0.26 | 0.10 – 0.42 | 0.002 |
| wave [3] | 0.09 | -0.07 – 0.25 | 0.261 |
| wave [4] | 0.17 | 0.01 – 0.33 | 0.033 |
| trustGovt c | -0.09 | -0.21 – 0.02 | 0.115 |
| Pos Emo c | -0.11 | -0.21 – -0.00 | 0.042 |
| Neg Emo c | -0.04 | -0.15 – 0.07 | 0.438 |
| FT Diff 100 | -0.07 | -0.28 – 0.14 | 0.499 |
| representation Dem | -0.07 | -0.12 – -0.01 | 0.024 |
| representation Rep | -0.04 | -0.09 – 0.01 | 0.150 |
| GCB c | -0.34 | -0.42 – -0.27 | <0.001 |
| pollAcc | -0.01 | -0.03 – 0.02 | 0.709 |
| pollFreq c | 0.03 | 0.00 – 0.06 | 0.036 |
| vs age | 0.01 | 0.00 – 0.01 | <0.001 |
| vs race [Black] | -0.69 | -0.90 – -0.48 | <0.001 |
| vs race [Hispanic] | -0.46 | -0.69 – -0.23 | <0.001 |
| vs race [Other] | -0.04 | -0.28 – 0.20 | 0.724 |
| male female | 0.02 | -0.11 – 0.15 | 0.784 |
| nonbinary mf | -0.40 | -1.37 – 0.58 | 0.424 |
| pDem Rep × wave [2] | -0.08 | -0.40 – 0.24 | 0.633 |
| pDem Rep × wave [3] | 0.04 | -0.28 – 0.36 | 0.797 |
| pDem Rep × wave [4] | 0.26 | -0.06 – 0.58 | 0.111 |
| pDem Rep × trustGovt c | -0.02 | -0.25 – 0.20 | 0.844 |
| wave [2] × trustGovt c | -0.02 | -0.16 – 0.13 | 0.811 |
| wave [3] × trustGovt c | -0.03 | -0.19 – 0.13 | 0.707 |
| wave [4] × trustGovt c | 0.03 | -0.14 – 0.19 | 0.738 |
| pDem Rep × Pos Emo c | -0.00 | -0.21 – 0.20 | 0.977 |
| pDem Rep × Neg Emo c | -0.07 | -0.29 – 0.14 | 0.519 |
| wave [2] × Pos Emo c | 0.02 | -0.12 – 0.15 | 0.823 |
| wave [3] × Pos Emo c | 0.07 | -0.06 – 0.21 | 0.285 |
| wave [4] × Pos Emo c | 0.08 | -0.06 – 0.22 | 0.245 |
| wave [2] × Neg Emo c | -0.18 | -0.32 – -0.04 | 0.011 |
| wave [3] × Neg Emo c | -0.10 | -0.24 – 0.04 | 0.175 |
| wave [4] × Neg Emo c | -0.12 | -0.25 – 0.02 | 0.105 |
| trustGovt c × Pos Emo c | 0.06 | -0.03 – 0.14 | 0.188 |
| trustGovt c × Neg Emo c | -0.09 | -0.19 – 0.01 | 0.067 |
| pDem Rep × FT Diff 100 | -0.52 | -0.94 – -0.10 | 0.015 |
| wave [2] × FT Diff 100 | -0.16 | -0.45 – 0.13 | 0.280 |
| wave [3] × FT Diff 100 | 0.00 | -0.28 – 0.28 | 0.988 |
| wave [4] × FT Diff 100 | -0.07 | -0.36 – 0.21 | 0.620 |
| trustGovt c × FT Diff 100 | 0.04 | -0.15 – 0.23 | 0.687 |
| Pos Emo c × FT Diff 100 | 0.16 | 0.00 – 0.32 | 0.045 |
| Neg Emo c × FT Diff 100 | 0.01 | -0.16 – 0.18 | 0.920 |
|
(pDem Rep × wave [2]) × trustGovt c |
0.14 | -0.15 – 0.44 | 0.334 |
|
(pDem Rep × wave [3]) × trustGovt c |
-0.22 | -0.54 – 0.11 | 0.187 |
|
(pDem Rep × wave [4]) × trustGovt c |
-0.07 | -0.39 – 0.26 | 0.688 |
|
(pDem Rep × wave [2]) × Pos Emo c |
0.18 | -0.10 – 0.46 | 0.203 |
|
(pDem Rep × wave [3]) × Pos Emo c |
0.09 | -0.19 – 0.36 | 0.538 |
|
(pDem Rep × wave [4]) × Pos Emo c |
0.12 | -0.16 – 0.41 | 0.387 |
|
(pDem Rep × wave [2]) × Neg Emo c |
0.17 | -0.11 – 0.45 | 0.240 |
|
(pDem Rep × wave [3]) × Neg Emo c |
-0.10 | -0.38 – 0.18 | 0.494 |
|
(pDem Rep × wave [4]) × Neg Emo c |
0.18 | -0.10 – 0.46 | 0.203 |
|
(pDem Rep × trustGovt c) × Pos Emo c |
0.03 | -0.14 – 0.20 | 0.706 |
|
(pDem Rep × trustGovt c) × Neg Emo c |
-0.16 | -0.36 – 0.04 | 0.118 |
|
(wave [2] × trustGovt c) × Pos Emo c |
-0.08 | -0.20 – 0.04 | 0.169 |
|
(wave [3] × trustGovt c) × Pos Emo c |
-0.03 | -0.14 – 0.09 | 0.621 |
|
(wave [4] × trustGovt c) × Pos Emo c |
0.02 | -0.10 – 0.13 | 0.786 |
|
(wave [2] × trustGovt c) × Neg Emo c |
0.17 | 0.05 – 0.30 | 0.008 |
|
(wave [3] × trustGovt c) × Neg Emo c |
0.01 | -0.12 – 0.14 | 0.856 |
|
(wave [4] × trustGovt c) × Neg Emo c |
0.01 | -0.12 – 0.14 | 0.900 |
|
(pDem Rep × wave [2]) × FT Diff 100 |
0.41 | -0.17 – 0.98 | 0.165 |
|
(pDem Rep × wave [3]) × FT Diff 100 |
0.19 | -0.36 – 0.75 | 0.497 |
|
(pDem Rep × wave [4]) × FT Diff 100 |
0.27 | -0.31 – 0.85 | 0.360 |
|
(pDem Rep × trustGovt c) × FT Diff 100 |
0.17 | -0.21 – 0.54 | 0.383 |
|
(wave [2] × trustGovt c) × FT Diff 100 |
0.03 | -0.22 – 0.29 | 0.791 |
|
(wave [3] × trustGovt c) × FT Diff 100 |
-0.11 | -0.39 – 0.17 | 0.427 |
|
(wave [4] × trustGovt c) × FT Diff 100 |
-0.03 | -0.32 – 0.25 | 0.810 |
|
(pDem Rep × Pos Emo c) × FT Diff 100 |
-0.05 | -0.36 – 0.26 | 0.749 |
|
(pDem Rep × Neg Emo c) × FT Diff 100 |
0.11 | -0.23 – 0.46 | 0.518 |
|
(wave [2] × Pos Emo c) × FT Diff 100 |
-0.19 | -0.41 – 0.03 | 0.084 |
|
(wave [3] × Pos Emo c) × FT Diff 100 |
-0.18 | -0.39 – 0.04 | 0.103 |
|
(wave [4] × Pos Emo c) × FT Diff 100 |
-0.24 | -0.46 – -0.02 | 0.031 |
|
(wave [2] × Neg Emo c) × FT Diff 100 |
0.02 | -0.21 – 0.24 | 0.872 |
|
(wave [3] × Neg Emo c) × FT Diff 100 |
-0.00 | -0.23 – 0.22 | 0.966 |
|
(wave [4] × Neg Emo c) × FT Diff 100 |
0.11 | -0.12 – 0.34 | 0.363 |
|
(trustGovt c × Pos Emo c) × FT Diff 100 |
-0.16 | -0.29 – -0.03 | 0.017 |
|
(trustGovt c × Neg Emo c) × FT Diff 100 |
0.15 | 0.01 – 0.30 | 0.041 |
|
(pDem Rep × wave [2] × trustGovt c) × Pos Emo c |
0.07 | -0.17 – 0.31 | 0.575 |
|
(pDem Rep × wave [3] × trustGovt c) × Pos Emo c |
0.17 | -0.06 – 0.40 | 0.157 |
|
(pDem Rep × wave [4] × trustGovt c) × Pos Emo c |
-0.11 | -0.34 – 0.12 | 0.362 |
|
(pDem Rep × wave [2] × trustGovt c) × Neg Emo c |
0.06 | -0.19 – 0.32 | 0.625 |
|
(pDem Rep × wave [3] × trustGovt c) × Neg Emo c |
0.14 | -0.12 – 0.40 | 0.287 |
|
(pDem Rep × wave [4] × trustGovt c) × Neg Emo c |
0.13 | -0.14 – 0.39 | 0.352 |
|
(pDem Rep × wave [2] × trustGovt c) × FT Diff 100 |
-0.39 | -0.90 – 0.12 | 0.135 |
|
(pDem Rep × wave [3] × trustGovt c) × FT Diff 100 |
0.07 | -0.49 – 0.63 | 0.799 |
|
(pDem Rep × wave [4] × trustGovt c) × FT Diff 100 |
-0.16 | -0.72 – 0.41 | 0.593 |
|
(pDem Rep × wave [2] × Pos Emo c) × FT Diff 100 |
-0.20 | -0.64 – 0.25 | 0.385 |
|
(pDem Rep × wave [3] × Pos Emo c) × FT Diff 100 |
-0.00 | -0.43 – 0.43 | 0.991 |
|
(pDem Rep × wave [4] × Pos Emo c) × FT Diff 100 |
-0.02 | -0.46 – 0.42 | 0.919 |
|
(pDem Rep × wave [2] × Neg Emo c) × FT Diff 100 |
-0.29 | -0.74 – 0.16 | 0.213 |
|
(pDem Rep × wave [3] × Neg Emo c) × FT Diff 100 |
-0.02 | -0.47 – 0.42 | 0.914 |
|
(pDem Rep × wave [4] × Neg Emo c) × FT Diff 100 |
-0.08 | -0.55 – 0.38 | 0.725 |
|
(pDem Rep × trustGovt c × Pos Emo c) × FT Diff 100 |
0.03 | -0.23 – 0.30 | 0.794 |
|
(pDem Rep × trustGovt c × Neg Emo c) × FT Diff 100 |
0.28 | -0.02 – 0.57 | 0.068 |
|
(wave [2] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.18 | -0.01 – 0.36 | 0.063 |
|
(wave [3] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.09 | -0.10 – 0.27 | 0.354 |
|
(wave [4] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.00 | -0.18 – 0.18 | 0.989 |
|
(wave [2] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.27 | -0.47 – -0.07 | 0.008 |
|
(wave [3] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.05 | -0.25 – 0.15 | 0.618 |
|
(wave [4] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.11 | -0.31 – 0.10 | 0.324 |
|
(pDem Rep × wave [2] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.18 | -0.55 – 0.20 | 0.354 |
|
(pDem Rep × wave [3] × trustGovt c × Pos Emo c) × FT Diff 100 |
-0.23 | -0.59 – 0.13 | 0.215 |
|
(pDem Rep × wave [4] × trustGovt c × Pos Emo c) × FT Diff 100 |
0.19 | -0.16 – 0.55 | 0.279 |
|
(pDem Rep × wave [2] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.21 | -0.60 – 0.19 | 0.307 |
|
(pDem Rep × wave [3] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.44 | -0.84 – -0.05 | 0.027 |
|
(pDem Rep × wave [4] × trustGovt c × Neg Emo c) × FT Diff 100 |
-0.23 | -0.65 – 0.19 | 0.283 |
| Random Effects | |||
| σ2 | 0.72 | ||
| τ00 pid | 0.99 | ||
| ICC | 0.58 | ||
| N pid | 1159 | ||
| Observations | 4423 | ||
| Marginal R2 / Conditional R2 | 0.165 / 0.650 | ||
Overall Summary
As in previous research, perceived election legitimacy (PEL) is responsive to election outcome: Democrats’ PEL decreased after the 2024 election and Republicans’ PEL increased. This correlates with changes in positive and negative emotions: emotions predict PEL in line with their valence (positive emotions predict PEL positively, negative emotions predict PEL negatively), and partisans’ emotions follow expected patterns relative to the 2024 election: from wave 1 to wave 4, Democrats increase in negative and decrease in positive emotions, while Republicans decrease in negative and increase in positive emotions, with the steepest changes happening from wave 1 (pre-election) to wave 2 (immediately post-election).
Trust in government seems to buffer against these trends—for those high in governmental trust, PEL is also high, regardless of party ID. Governmental trust may also moderate the effect of negative emotions: there was a significant 4-way interaction of governmental trust, negative emotions, party ID (Dem vs. Rep), and linear time (b = -0.10, p = .032); there was also a significant 4-way interaction of governmental trust, negative emotions, party ID (partisans vs. Ind), and linear time (b = -0.22, p = .005). No such interactions were present for positive emotions.