grant analyses
Dani Grant
1/4/2021
- NOTES:
- set up
- descriptives
- LIWC means
- corr matrix
- I. AVG Wave 1 + Wave 2
- II. WAVE 1
- III. WAVE 2
- 1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
- 2. vaxxAttitudes ~ ANALYTIC * party
- 3. vaxxAttitudes ~ AFFECT * party
- 4. ANALYTIC ~ AFFECT * party
- 5. vote legitimacy ~ ANALYTIC * party
- 6. voteLegit ~ ANALYTIC * party * election timing + sum.media.exp
- 7. voteLegit ~ ANALYTIC * party * election timing
- 8. voteLegit ~ ANALYTIC * party + sum.media.exposure
- 9. voteLegit ~ ANALYTIC * party
- IV. LONGITUDINAL MODELS
- V. long by W1 + W2 models
NOTES:
- all analytic and affect indexes are averages (to account for participants who didn’t fill out all the media exposure ratings)
set up
libraries and data sets
library(psych)
library(lme4)## Loading required package: Matrixlibrary(dplyr)##
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library(minqa)
library(dfoptim)
library(ggcorrplot)
#import wave 1
d1.1 <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_US_Cleaned1.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import wave 2
d2.1 <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_US_Wave2_Cleaned.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import LIWC csv
liwc <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_w1w2_Dec_2021.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)LIWC wave 1 & wave 2
###################################
# Create LIWC rating averages for wave 1
###################################
#move over measures of interest
w1 <- data.frame(liwc[liwc$Wave == 1,])
w1 <- w1[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
# create wide data set for wave 2
w1w = w1 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w1w[paste0("affect_",1:21)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w1w[paste0("cogproc_",1:21)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w1w[paste0("posemo_",1:21)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w1w[paste0("negemo_",1:21)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w1w[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w1 <- data.frame(w1w$mediaOutlet)
colnames(w1)[colnames(w1)=="w1w.mediaOutlet"] <- "mediaOutlet"
w1$affect <- AF
w1$cogproc <- CP
w1$analytic <- AN
w1$posemo <- PE
w1$negemo <- NE
##############################################
# Create LIWC rating averages for wave 2
##############################################
#move over measures of interest
w2 <- data.frame(liwc[liwc$Wave == 2,])
w2 <- w2[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
# create wide data set for wave 2
w2w = w2 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w2w[paste0("affect_",1:22)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w2w[paste0("cogproc_",1:22)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w2w[paste0("posemo_",1:22)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w2w[paste0("negemo_",1:22)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w2w[paste0("analytic_",1:22)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w2 <- data.frame(w2w$mediaOutlet)
colnames(w2)[colnames(w2) == "w2w.mediaOutlet"] <- "mediaOutlet"
w2$affect <- AF
w2$cogproc <- CP
w2$analytic <- AN
w2$posemo <- PE
w2$negemo <- NEprep wave 1 data
#delete any measures we don't want---exclude media exposure #3, #8, and #9
## missing Yahoo, Huff Post, Wash Post
d1 <- d1.1[,c("s3", "Wave", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_4",
"mediaExposure_5", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11", "mediaExposure_12",
"mediaExposure_13", "mediaExposure_14", "mediaExposure_15")]
d1$ownvote_conf <- NA
d1$overallvote_conf <- NA
d1$election_timing <- NA
colnames(d1)[colnames(d1) == "s3"] <- "participant"
#rename exposure
colnames(d1)[colnames(d1)=="mediaExposure_1"] <- "NYT_exp"
colnames(d1)[colnames(d1)=="mediaExposure_2"] <- "WSJ_exp"
colnames(d1)[colnames(d1)=="mediaExposure_4"] <- "USAT_exp"
colnames(d1)[colnames(d1)=="mediaExposure_5"] <- "Fox_exp"
colnames(d1)[colnames(d1)=="mediaExposure_6"] <- "CNN_exp"
colnames(d1)[colnames(d1)=="mediaExposure_7"] <- "MSNBC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_10"] <-"AOL_exp"
colnames(d1)[colnames(d1)=="mediaExposure_11"] <-"NPR_exp"
colnames(d1)[colnames(d1)=="mediaExposure_12"] <-"ABC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_13"] <-"NBC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_14"] <-"CBS_exp"
colnames(d1)[colnames(d1)=="mediaExposure_15"] <-"PBS_exp"
#change to 0-4 rating
d1$ABC_exp <- d1$ABC_exp - 1
d1$CBS_exp <- d1$CBS_exp - 1
d1$CNN_exp <- d1$CNN_exp - 1
d1$Fox_exp <- d1$Fox_exp - 1
d1$MSNBC_exp <- d1$MSNBC_exp - 1
d1$NBC_exp <- d1$NBC_exp - 1
d1$NPR_exp <- d1$NPR_exp - 1
d1$NYT_exp <- d1$NYT_exp - 1
d1$PBS_exp <- d1$PBS_exp - 1
d1$USAT_exp <- d1$USAT_exp - 1
d1$WSJ_exp <- d1$WSJ_exp - 1
d1$AOL_exp <- d1$AOL_exp - 1
x <- cbind(d1$ABC_exp,
d1$CBS_exp,
d1$CNN_exp,
d1$Fox_exp,
d1$MSNBC_exp,
d1$NBC_exp,
d1$NPR_exp,
d1$NYT_exp,
d1$PBS_exp,
d1$USAT_exp,
d1$WSJ_exp,
d1$AOL_exp)
d1$sum.media.exp <- rowSums(x, na.rm = T)
## affect
d1$ABC_AF <- w1$affect[w1$mediaOutlet == "ABC"]
d1$CBS_AF <- w1$affect[w1$mediaOutlet == "CBS"]
d1$CNN_AF <- w1$affect[w1$mediaOutlet == "CNN"]
d1$Fox_AF <- w1$affect[w1$mediaOutlet == "Fox"]
d1$MSNBC_AF <- w1$affect[w1$mediaOutlet == "MSNBC"]
d1$NBC_AF <- w1$affect[w1$mediaOutlet == "NBC"]
d1$NPR_AF <- w1$affect[w1$mediaOutlet == "NPR"]
d1$NYT_AF <- w1$affect[w1$mediaOutlet == "NYT"]
d1$PBS_AF <- w1$affect[w1$mediaOutlet == "PBS"]
d1$USAT_AF <- w1$affect[w1$mediaOutlet == "USAToday"]
d1$WSJ_AF <- w1$affect[w1$mediaOutlet == "WSJ"]
d1$AOL_AF <- w1$affect[w1$mediaOutlet == "AOL"]
## analytic thinking
d1$ABC_AN <- w1$analytic[w1$mediaOutlet == "ABC"]
d1$CBS_AN <- w1$analytic[w1$mediaOutlet == "CBS"]
d1$CNN_AN <- w1$analytic[w1$mediaOutlet == "CNN"]
d1$Fox_AN <- w1$analytic[w1$mediaOutlet == "Fox"]
d1$MSNBC_AN <- w1$analytic[w1$mediaOutlet == "MSNBC"]
d1$NBC_AN <- w1$analytic[w1$mediaOutlet == "NBC"]
d1$NPR_AN <- w1$analytic[w1$mediaOutlet == "NPR"]
d1$NYT_AN <- w1$analytic[w1$mediaOutlet == "NYT"]
d1$PBS_AN <- w1$analytic[w1$mediaOutlet == "PBS"]
d1$USAT_AN <- w1$analytic[w1$mediaOutlet == "USAToday"]
d1$WSJ_AN <- w1$analytic[w1$mediaOutlet == "WSJ"]
d1$AOL_AN <- w1$analytic[w1$mediaOutlet == "AOL"]
#individual media affect
d1$ABC_AFexp <- d1$ABC_AF * d1$ABC_exp
d1$CBS_AFexp <- d1$CBS_AF * d1$CBS_exp
d1$CNN_AFexp <- d1$CNN_AF * d1$CNN_exp
d1$Fox_AFexp <- d1$Fox_AF * d1$Fox_exp
d1$MSNBC_AFexp <- d1$MSNBC_AF * d1$MSNBC_exp
d1$NBC_AFexp <- d1$NBC_AF * d1$NBC_exp
d1$NPR_AFexp <- d1$NPR_AF * d1$NPR_exp
d1$NYT_AFexp <- d1$NYT_AF * d1$NYT_exp
d1$PBS_AFexp <- d1$PBS_AF * d1$PBS_exp
d1$USAT_AFexp <- d1$USAT_AF * d1$USAT_exp
d1$WSJ_AFexp <- d1$WSJ_AF * d1$WSJ_exp
d1$AOL_AFexp <- d1$AOL_AF * d1$AOL_exp
x <- cbind(d1$ABC_AFexp,
d1$CBS_AFexp,
d1$CNN_AFexp,
d1$Fox_AFexp,
d1$MSNBC_AFexp,
d1$NBC_AFexp,
d1$NPR_AFexp,
d1$NYT_AFexp,
d1$PBS_AFexp,
d1$USAT_AFexp,
d1$WSJ_AFexp,
d1$AOL_AFexp)
d1$index_AFexp <- rowMeans(x, na.rm = T)
#individual media affect
d1$ABC_ANexp <- d1$ABC_AN * d1$ABC_exp
d1$CBS_ANexp <- d1$CBS_AN * d1$CBS_exp
d1$CNN_ANexp <- d1$CNN_AN * d1$CNN_exp
d1$Fox_ANexp <- d1$Fox_AN * d1$Fox_exp
d1$MSNBC_ANexp <- d1$MSNBC_AN * d1$MSNBC_exp
d1$NBC_ANexp <- d1$NBC_AN * d1$NBC_exp
d1$NPR_ANexp <- d1$NPR_AN * d1$NPR_exp
d1$NYT_ANexp <- d1$NYT_AN * d1$NYT_exp
d1$PBS_ANexp <- d1$PBS_AN * d1$PBS_exp
d1$USAT_ANexp <- d1$USAT_AN * d1$USAT_exp
d1$WSJ_ANexp <- d1$WSJ_AN * d1$WSJ_exp
d1$AOL_ANexp <- d1$AOL_AN * d1$AOL_exp
x <- cbind(d1$ABC_ANexp,
d1$CBS_ANexp,
d1$CNN_ANexp,
d1$Fox_ANexp,
d1$MSNBC_ANexp,
d1$NBC_ANexp,
d1$NPR_ANexp,
d1$NYT_ANexp,
d1$PBS_ANexp,
d1$USAT_ANexp,
d1$WSJ_ANexp,
d1$AOL_ANexp)
d1$index_ANexp <- rowMeans(x, na.rm = T)
#################################################
# Election Timing
#################################################
d1$election_timing[d1$election_timing == 'Pre-election'] <- NA
# Contrast codes
d1$tDur_Post <- NA
# Dummy codes
## During
d1$tDur <- NA
## Post
d1$tPost <- NA
d1$electiontiming <- NA
# Factor order
d1$election_timing <- factor(d1$election_timing, levels = c('During-election','Post-election'))
####################################################
# vote legitimacy
####################################################
d1$voteLegit <- NA
#####################################################
# codes for party
####################################################
d1$partyCont <- NA
d1$partyCont[d1$demStrength == 1] <- -3
d1$partyCont[d1$demStrength == 2] <- -2
d1$partyCont[d1$partyClose == 1] <- -1
d1$partyCont[d1$partyClose == 3] <- 0
d1$partyCont[d1$partyClose == 2] <- 1
d1$partyCont[d1$repStrength == 2] <- 2
d1$partyCont[d1$repStrength == 1] <- 3
## party factor
d1$party_factor <- NA
d1$party_factor[d1$partyCont < 0] <- 'Democrat'
d1$party_factor[d1$partyCont == 0] <- 'Independent'
d1$party_factor[d1$partyCont > 0] <- 'Republican'
## Order of party variable
d1$party_factor <- factor(d1$party_factor,
levels = c('Democrat', 'Republican', 'Independent'))
## contrast codes
d1$DvR <- NA
d1$DvR[d1$party_factor == 'Democrat'] <- -.5
d1$DvR[d1$party_factor == 'Independent'] <- 0
d1$DvR[d1$party_factor == 'Republican'] <- .5
d1$IvDR <- NA
d1$IvDR[d1$party_factor == 'Democrat'] <- .33
d1$IvDR[d1$party_factor == 'Independent'] <- -.67
d1$IvDR[d1$party_factor == 'Republican'] <- .33
## dummy codes
d1$Rep_1[d1$party_factor == 'Democrat'] <- 0
d1$Rep_1[d1$party_factor == 'Republican'] <- 1
d1$Rep_1[d1$party_factor == 'Independent'] <- 0
d1$Ind_1[d1$party_factor == 'Democrat'] <- 0
d1$Ind_1[d1$party_factor == 'Republican'] <- 0
d1$Ind_1[d1$party_factor == 'Independent'] <- 1
d1$Dem_1[d1$party_factor == 'Democrat'] <- 1
d1$Dem_1[d1$party_factor == 'Republican'] <- 0
d1$Dem_1[d1$party_factor == 'Independent'] <- 0
## delete unnecessary columns
d1$party <- NULL
d1$demStrength <- NULL
d1$repStrength <- NULL
d1$partyClose <- NULLprep wave 2 data
# delete any measures we don't want---exclude media exposure #3, #8, and #9
# missing Yahoo, Huff Post, Wash Post
d2 <- d2.1[,c("s3", "Wave", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_4",
"mediaExposure_5", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11", "mediaExposure_12",
"mediaExposure_13", "mediaExposure_14", "mediaExposure_15",
"ownvote_conf", "overallvote_conf", "election_timing")]
colnames(d2)[colnames(d2) == "s3"] <- "participant"
# rename exposure
colnames(d2)[colnames(d2) == "mediaExposure_1"] <- "NYT_exp"
colnames(d2)[colnames(d2) == "mediaExposure_2"] <- "WSJ_exp"
colnames(d2)[colnames(d2) == "mediaExposure_4"] <- "USAT_exp"
colnames(d2)[colnames(d2) == "mediaExposure_5"] <- "Fox_exp"
colnames(d2)[colnames(d2) == "mediaExposure_6"] <- "CNN_exp"
colnames(d2)[colnames(d2) == "mediaExposure_7"] <- "MSNBC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_10"] <- "AOL_exp"
colnames(d2)[colnames(d2) == "mediaExposure_11"] <- "NPR_exp"
colnames(d2)[colnames(d2) == "mediaExposure_12"] <- "ABC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_13"] <- "NBC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_14"] <- "CBS_exp"
colnames(d2)[colnames(d2) == "mediaExposure_15"] <- "PBS_exp"
# change to 0-4 rating
d2$ABC_exp <- d2$ABC_exp - 1
d2$CBS_exp <- d2$CBS_exp - 1
d2$CNN_exp <- d2$CNN_exp - 1
d2$Fox_exp <- d2$Fox_exp - 1
d2$MSNBC_exp <- d2$MSNBC_exp - 1
d2$NBC_exp <- d2$NBC_exp - 1
d2$NPR_exp <- d2$NPR_exp - 1
d2$NYT_exp <- d2$NYT_exp - 1
d2$PBS_exp <- d2$PBS_exp - 1
d2$USAT_exp <- d2$USAT_exp - 1
d2$WSJ_exp <- d2$WSJ_exp - 1
d2$AOL_exp <- d2$AOL_exp - 1
x <- cbind(d2$ABC_exp,
d2$CBS_exp,
d2$CNN_exp,
d2$Fox_exp,
d2$MSNBC_exp,
d2$NBC_exp,
d2$NPR_exp,
d2$NYT_exp,
d2$PBS_exp,
d2$USAT_exp,
d2$WSJ_exp,
d2$AOL_exp)
d2$sum.media.exp <- rowSums(x, na.rm = T)
## affect
d2$ABC_AF <- w2$affect[w2$mediaOutlet == "ABC"]
d2$CBS_AF <- w2$affect[w2$mediaOutlet == "CBS"]
d2$CNN_AF <- w2$affect[w2$mediaOutlet == "CNN"]
d2$Fox_AF <- w2$affect[w2$mediaOutlet == "Fox"]
d2$MSNBC_AF <- w2$affect[w2$mediaOutlet == "MSNBC"]
d2$NBC_AF <- w2$affect[w2$mediaOutlet == "NBC"]
d2$NPR_AF <- w2$affect[w2$mediaOutlet == "NPR"]
d2$NYT_AF <- w2$affect[w2$mediaOutlet == "NYT"]
d2$PBS_AF <- w2$affect[w2$mediaOutlet == "PBS"]
d2$USAT_AF <- w2$affect[w2$mediaOutlet == "USAToday"]
d2$WSJ_AF <- w2$affect[w2$mediaOutlet == "WSJ"]
d2$AOL_AF <- w2$affect[w2$mediaOutlet == "AOL"]
## analytic thinking
d2$ABC_AN <- w2$analytic[w2$mediaOutlet == "ABC"]
d2$CBS_AN <- w2$analytic[w2$mediaOutlet == "CBS"]
d2$CNN_AN <- w2$analytic[w2$mediaOutlet == "CNN"]
d2$Fox_AN <- w2$analytic[w2$mediaOutlet == "Fox"]
d2$MSNBC_AN <- w2$analytic[w2$mediaOutlet == "MSNBC"]
d2$NBC_AN <- w2$analytic[w2$mediaOutlet == "NBC"]
d2$NPR_AN <- w2$analytic[w2$mediaOutlet == "NPR"]
d2$NYT_AN <- w2$analytic[w2$mediaOutlet == "NYT"]
d2$PBS_AN <- w2$analytic[w2$mediaOutlet == "PBS"]
d2$USAT_AN <- w2$analytic[w2$mediaOutlet == "USAToday"]
d2$WSJ_AN <- w2$analytic[w2$mediaOutlet == "WSJ"]
d2$AOL_AN <- w2$analytic[w2$mediaOutlet == "AOL"]
#individual media affect
d2$ABC_AFexp <- d2$ABC_AF * d2$ABC_exp
d2$CBS_AFexp <- d2$CBS_AF * d2$CBS_exp
d2$CNN_AFexp <- d2$CNN_AF * d2$CNN_exp
d2$Fox_AFexp <- d2$Fox_AF * d2$Fox_exp
d2$MSNBC_AFexp <- d2$MSNBC_AF * d2$MSNBC_exp
d2$NBC_AFexp <- d2$NBC_AF * d2$NBC_exp
d2$NPR_AFexp <- d2$NPR_AF * d2$NPR_exp
d2$NYT_AFexp <- d2$NYT_AF * d2$NYT_exp
d2$PBS_AFexp <- d2$PBS_AF * d2$PBS_exp
d2$USAT_AFexp <- d2$USAT_AF * d2$USAT_exp
d2$WSJ_AFexp <- d2$WSJ_AF * d2$WSJ_exp
d2$AOL_AFexp <- d2$AOL_AF * d2$AOL_exp
x <- cbind(d2$ABC_AFexp,
d2$CBS_AFexp,
d2$CNN_AFexp,
d2$Fox_AFexp,
d2$MSNBC_AFexp,
d2$NBC_AFexp,
d2$NPR_AFexp,
d2$NYT_AFexp,
d2$PBS_AFexp,
d2$USAT_AFexp,
d2$WSJ_AFexp,
d2$AOL_AFexp)
d2$index_AFexp <- rowMeans(x, na.rm = T)
#individual media affect
d2$ABC_ANexp <- d2$ABC_AN * d2$ABC_exp
d2$CBS_ANexp <- d2$CBS_AN * d2$CBS_exp
d2$CNN_ANexp <- d2$CNN_AN * d2$CNN_exp
d2$Fox_ANexp <- d2$Fox_AN * d2$Fox_exp
d2$MSNBC_ANexp <- d2$MSNBC_AN * d2$MSNBC_exp
d2$NBC_ANexp <- d2$NBC_AN * d2$NBC_exp
d2$NPR_ANexp <- d2$NPR_AN * d2$NPR_exp
d2$NYT_ANexp <- d2$NYT_AN * d2$NYT_exp
d2$PBS_ANexp <- d2$PBS_AN * d2$PBS_exp
d2$USAT_ANexp <- d2$USAT_AN * d2$USAT_exp
d2$WSJ_ANexp <- d2$WSJ_AN * d2$WSJ_exp
d2$AOL_ANexp <- d2$AOL_AN * d2$AOL_exp
x <- cbind(d2$ABC_ANexp,
d2$CBS_ANexp,
d2$CNN_ANexp,
d2$Fox_ANexp,
d2$MSNBC_ANexp,
d2$NBC_ANexp,
d2$NPR_ANexp,
d2$NYT_ANexp,
d2$PBS_ANexp,
d2$USAT_ANexp,
d2$WSJ_ANexp,
d2$AOL_ANexp)
d2$index_ANexp <- rowMeans(x, na.rm = T)
##################
# Election Timing
##################
d2$election_timing[d2$election_timing == 'Pre-election'] <- NA
# Contrast codes
d2$tDur_Post <- NA
d2$tDur_Post[d2$election_timing == 'During-election'] <- -.5
d2$tDur_Post[d2$election_timing == 'Post-election'] <- .5
# Dummy codes
## During
d2$tDur <- NA
d2$tDur[d2$election_timing == 'During-election'] <- 0
d2$tDur[d2$election_timing == 'Post-election'] <- 1
## Post
d2$tPost <- NA
d2$tPost[d2$election_timing == 'During-election'] <- 1
d2$tPost[d2$election_timing == 'Post-election'] <- 0
# Factor order
d2$election_timing <- factor(d2$election_timing, levels = c('During-election','Post-election'))
# Renaming election factor to ms language
d2$electiontiming <- recode_factor(d2$election_timing, "During-election" = "Undeclared", "Post-election" = "Declared")
#####################################################
# vote legit
####################################################
x <- cbind(d2$ownvote_conf,
d2$overallvote_conf)
d2$voteLegit <- rowMeans(x, na.rm = T)
#####################################################
# codes for party
####################################################
d2$partyCont <- NA
d2$partyCont[d2$demStrength == 1] <- -3
d2$partyCont[d2$demStrength == 2] <- -2
d2$partyCont[d2$partyClose == 1] <- -1
d2$partyCont[d2$partyClose == 3] <- 0
d2$partyCont[d2$partyClose == 2] <- 1
d2$partyCont[d2$repStrength == 2] <- 2
d2$partyCont[d2$repStrength == 1] <- 3
# party factor
d2$party_factor <- NA
d2$party_factor[d2$partyCont < 0] <- 'Democrat'
d2$party_factor[d2$partyCont == 0] <- 'Independent'
d2$party_factor[d2$partyCont > 0] <- 'Republican'
## Order of party variable
d2$party_factor <- factor(d2$party_factor,
levels = c('Democrat', 'Republican','Independent'))
## Contrast codes
d2$DvR <- NA
d2$DvR[d2$party_factor == 'Democrat'] <- -.5
d2$DvR[d2$party_factor == 'Independent'] <- 0
d2$DvR[d2$party_factor == 'Republican'] <- .5
d2$IvDR <- NA
d2$IvDR[d2$party_factor == 'Democrat'] <- .33
d2$IvDR[d2$party_factor == 'Independent'] <- -.67
d2$IvDR[d2$party_factor == 'Republican'] <- .33
## dummy codes
d2$Rep_1[d2$party_factor == 'Democrat'] <- 0
d2$Rep_1[d2$party_factor == 'Republican'] <- 1
d2$Rep_1[d2$party_factor == 'Independent'] <- 0
d2$Ind_1[d2$party_factor == 'Democrat'] <- 0
d2$Ind_1[d2$party_factor == 'Republican'] <- 0
d2$Ind_1[d2$party_factor == 'Independent'] <- 1
d2$Dem_1[d2$party_factor == 'Democrat'] <- 1
d2$Dem_1[d2$party_factor == 'Republican'] <- 0
d2$Dem_1[d2$party_factor == 'Independent'] <- 0
## delete unnecessary columns
d2$party <- NULL
d2$demStrength <- NULL
d2$repStrength <- NULL
d2$partyClose <- NULLLONG merged data
names(d1) == names(d2)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUEdm <- rbind.data.frame(d1, d2)
dm$participant <- as.factor(dm$participant)
dm$W1vW2 <- NA
dm$W1vW2[dm$Wave == 1] <- -.5
dm$W1vW2[dm$Wave == 2] <- .5
dm$W1_0 <- NA
dm$W1_0[dm$Wave == 1] <- 0
dm$W1_0[dm$Wave == 2] <- 1
dm$W2_0 <- NA
dm$W2_0[dm$Wave == 1] <- 1
dm$W2_0[dm$Wave == 2] <- 0WIDE merged data
#d1 = x; d2 = y
dw <- merge(d1, d2, by = c("participant"), all.x = T, all.y = T)
dw$party_match <- TRUE
dw$party_match[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Republican'] <- FALSE
dw$party_match[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Democrat']<- FALSE
#make weak leaners
dw$party_factor.y[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Independent']<- 'Democrat'
dw$party_factor.y[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Republican']<- 'Republican'
dw$party_factor.x[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Democrat']<- 'Democrat'
dw$party_factor.x[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Independent']<- 'Republican'
dw$partyCont.y[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Independent']<- -1
dw$partyCont.y[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Republican']<- 1
dw$partyCont.x[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Democrat']<- -1
dw$partyCont.x[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Independent']<- 1
#get rid of party swaps
dw <- dw[dw$party_match,]
colnames(dw)[colnames(dw) == "vaxxAttitudes.x"] <- "vaxxAttitudes.w1"
colnames(dw)[colnames(dw) == "vaxxAttitudes.y"] <- "vaxxAttitudes.w2"
dw$vaxxAttitudes.c.w1 <- dw$vaxxAttitudes.w1 - mean(dw$vaxxAttitudes.w1, na.rm = T)
colnames(dw)[colnames(dw) == "party_factor.x"] <- "party_factor"
dw$party_factor.y <- NULL
colnames(dw)[colnames(dw) == "partyCont.x"] <- "partyCont.w1"
colnames(dw)[colnames(dw) == "partyCont.y"] <- "partyCont.w2"
colnames(dw)[colnames(dw) == "index_AFexp.x"] <- "index_AFexp.w1"
colnames(dw)[colnames(dw) == "index_AFexp.y"] <- "index_AFexp.w2"
colnames(dw)[colnames(dw) == "index_ANexp.x"] <- "index_ANexp.w1"
colnames(dw)[colnames(dw) == "index_ANexp.y"] <- "index_ANexp.w2"
colnames(dw)[colnames(dw) == "DvR.x"] <- "DvR"
colnames(dw)[colnames(dw) == "IvDR.x"] <- "IvDR"
colnames(dw)[colnames(dw) == "Ind_1.x"] <- "Ind_1"
colnames(dw)[colnames(dw) == "Rep_1.x"] <- "Rep_1"
colnames(dw)[colnames(dw) == "Dem_1.x"] <- "Dem_1"
#get averages
x <- cbind(dw$vaxxAttitudes.w1, dw$vaxxAttitudes.w2)
dw$avgVaxxAttitudes <- rowMeans(x, na.rm = T)
x <- cbind(dw$index_AFexp.w1, dw$index_AFexp.w2)
dw$avg_AFexp <- rowMeans(x, na.rm = T)
x <- cbind(dw$index_ANexp.w1, dw$index_ANexp.w2)
dw$avg_ANexp <- rowMeans(x, na.rm = T)
x <- cbind(dw$sum.media.exp.x, dw$sum.media.exp.y)
dw$avg.sum.media.exp <- rowMeans(x, na.rm = T)descriptives
sum(table(d1$vaxxAttitudes)) #3051 responses for vaxxAttitudes in wave 1## [1] 3051sum(table(d2$vaxxAttitudes)) #2419 responses for vaxxAttitudes in wave 2## [1] 2419print("wave 1")## [1] "wave 1"sum(table(d1$ABC_exp)) #3051## [1] 3051sum(table(d1$CBS_exp)) #3051## [1] 3051sum(table(d1$CNN_exp)) #3053## [1] 3053sum(table(d1$Fox_exp))#3053## [1] 3053sum(table(d1$MSNBC_exp)) #3052## [1] 3052sum(table(d1$NBC_exp)) #3053## [1] 3053sum(table(d1$NPR_exp)) #3052## [1] 3052sum(table(d1$NYT_exp)) #3052## [1] 3052sum(table(d1$PBS_exp)) #3053## [1] 3053sum(table(d1$USAT_exp)) #3053## [1] 3053sum(table(d1$WSJ_exp)) #3052## [1] 3052sum(table(d1$AOL_exp)) #3053## [1] 3053print("wave 2")## [1] "wave 2"sum(table(d2$ABC_exp)) #2412## [1] 2412sum(table(d2$CBS_exp)) #2413## [1] 2413sum(table(d2$CNN_exp))#2411## [1] 2411sum(table(d2$Fox_exp)) #2414## [1] 2414sum(table(d2$MSNBC_exp)) #2413## [1] 2413sum(table(d2$NBC_exp)) #2412## [1] 2412sum(table(d2$NPR_exp)) #2413## [1] 2413sum(table(d2$NYT_exp)) #2412## [1] 2412sum(table(d2$PBS_exp))#2412## [1] 2412sum(table(d2$USAT_exp))#2413## [1] 2413sum(table(d2$WSJ_exp)) #2413## [1] 2413sum(table(d2$AOL_exp)) #2411## [1] 2411sum(table(d1$party_factor))## [1] 3147sum(table(d2$party_factor))## [1] 2426tapply(d1$index_AFexp, d1$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 5.469034 3.406757 3.935891tapply(d1$index_ANexp, d1$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 108.65344 65.97917 78.10298tapply(d2$index_AFexp, d2$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 5.095026 2.902290 3.173599tapply(d2$index_ANexp, d2$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 102.32919 57.04594 63.54699LIWC means
wave 1
## mediaOutlet affect cogproc analytic posemo negemo
## 1 ABC 3.920000 7.787500 79.10750 2.635000 1.232500
## 2 AOL 3.504762 6.856667 95.42000 2.103810 1.355238
## 3 CBS 3.890000 8.275000 78.76000 2.500000 1.330000
## 4 CNN 3.800000 9.780000 73.61000 2.280000 1.460000
## 5 Fox 4.390000 10.480000 60.43000 2.690000 1.650000
## 6 MSNBC 3.880000 9.930000 70.52000 2.360000 1.450000
## 7 NBC 6.590000 6.870000 80.54500 2.730000 3.810000
## 8 NPR 3.425000 9.825000 71.83000 2.125000 1.240000
## 9 NYT 3.537222 7.912222 93.49444 1.941111 1.535556
## 10 PBS 3.830000 8.970000 78.86000 2.320000 1.460000
## 11 USAToday 3.653333 8.883333 91.28000 2.206667 1.390000
## 12 WSJ 1.846000 4.416000 96.59800 1.014000 0.800000wave 2
## mediaOutlet affect cogproc analytic posemo negemo
## 1 ABC 3.870000 8.015000 75.97750 2.750000 1.077500
## 2 AOL 3.715455 7.033182 95.29773 2.212727 1.451364
## 3 CBS 3.760000 7.620000 84.05500 2.325000 1.395000
## 4 CNN 3.678000 9.964000 64.72000 2.404000 1.226000
## 5 Fox 4.135000 10.445000 61.09500 2.775000 1.315000
## 6 MSNBC 3.660000 9.900000 69.56000 2.380000 1.220000
## 7 NBC 6.190000 7.150000 78.12000 2.665000 3.490000
## 8 NPR 3.595000 9.865000 70.81500 2.385000 1.145000
## 9 NYT 3.607895 7.792105 93.36368 2.074737 1.466842
## 10 PBS 3.680000 9.080000 77.06000 2.410000 1.230000
## 11 USAToday 3.830000 8.490000 91.85500 2.390000 1.390000
## 12 WSJ 1.628000 4.020000 96.43200 1.002000 0.582000corr matrix
x <- cbind.data.frame(dw$index_ANexp.w1, dw$index_ANexp.w2,
dw$index_AFexp.w1, dw$index_AFexp.w2,
dw$avg_ANexp, dw$avg_AFexp,
dw$partyCont.w1, dw$partyCont.w2)
cor <- cor(x, use = "complete.obs")
ggcorrplot(cor, type = "lower",
lab = TRUE, title = "general correlations")
x1 <- cbind.data.frame(w1$affect, w2$affect,
w1$analytic, w2$analytic)
cor1 <- cor(x1, use = "complete.obs")
ggcorrplot(cor1, type = "lower",
lab = TRUE, title = "media analytic + affect correlations wave 1 & wave 2")
I. AVG Wave 1 + Wave 2
1. avg_vaxxAttitudes ~ (avg_ANALYTIC + avg_AFFECT) * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * (avg_AFexp + avg_ANexp) + avg.sum.media.exp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * (avg_AFexp + avg_ANexp) +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5653 -1.4627 0.2175 1.6419 3.9552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.163290 0.063304 -2.579 0.00994 **
## DvR -1.176575 0.142533 -8.255 2.25e-16 ***
## IvDR 0.600460 0.144195 4.164 3.21e-05 ***
## avg_AFexp 0.251521 0.107015 2.350 0.01882 *
## avg_ANexp -0.013215 0.005924 -2.231 0.02577 *
## avg.sum.media.exp 0.048202 0.028362 1.700 0.08932 .
## DvR:avg_AFexp 1.013395 0.215098 4.711 2.57e-06 ***
## DvR:avg_ANexp -0.043847 0.010313 -4.252 2.19e-05 ***
## IvDR:avg_AFexp -0.220338 0.254223 -0.867 0.38617
## IvDR:avg_ANexp 0.010831 0.012316 0.879 0.37921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2996 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0919, Adjusted R-squared: 0.08917
## F-statistic: 33.69 on 9 and 2996 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) *
(avg_AFexp + avg_ANexp) + avg.sum.media.exp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * (avg_AFexp +
## avg_ANexp) + avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5653 -1.4627 0.2175 1.6419 3.9552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.623149 0.107436 5.800 7.32e-09 ***
## Rep_1 -1.176575 0.142533 -8.255 2.25e-16 ***
## Ind_1 -1.188747 0.165003 -7.204 7.34e-13 ***
## avg_AFexp -0.327888 0.133160 -2.462 0.01386 *
## avg_ANexp 0.012283 0.006841 1.796 0.07267 .
## avg.sum.media.exp 0.048202 0.028362 1.700 0.08932 .
## Rep_1:avg_AFexp 1.013395 0.215098 4.711 2.57e-06 ***
## Rep_1:avg_ANexp -0.043847 0.010313 -4.252 2.19e-05 ***
## Ind_1:avg_AFexp 0.727035 0.264750 2.746 0.00607 **
## Ind_1:avg_ANexp -0.032755 0.012806 -2.558 0.01059 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2996 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0919, Adjusted R-squared: 0.08917
## F-statistic: 33.69 on 9 and 2996 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) *
(avg_AFexp + avg_ANexp) + avg.sum.media.exp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * (avg_AFexp +
## avg_ANexp) + avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5653 -1.4627 0.2175 1.6419 3.9552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.553426 0.094024 -5.886 4.40e-09 ***
## Dem_1 1.176575 0.142533 8.255 2.25e-16 ***
## Ind_1 -0.012172 0.156576 -0.078 0.938039
## avg_AFexp 0.685507 0.171847 3.989 6.79e-05 ***
## avg_ANexp -0.031564 0.008841 -3.570 0.000363 ***
## avg.sum.media.exp 0.048202 0.028362 1.700 0.089324 .
## Dem_1:avg_AFexp -1.013395 0.215098 -4.711 2.57e-06 ***
## Dem_1:avg_ANexp 0.043847 0.010313 4.252 2.19e-05 ***
## Ind_1:avg_AFexp -0.286360 0.286879 -0.998 0.318268
## Ind_1:avg_ANexp 0.011093 0.013875 0.799 0.424090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2996 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0919, Adjusted R-squared: 0.08917
## F-statistic: 33.69 on 9 and 2996 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) *
(avg_AFexp + avg_ANexp) + avg.sum.media.exp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * (avg_AFexp +
## avg_ANexp) + avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5653 -1.4627 0.2175 1.6419 3.9552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56560 0.12528 -4.515 6.58e-06 ***
## Rep_1 0.01217 0.15658 0.078 0.93804
## Dem_1 1.18875 0.16500 7.204 7.34e-13 ***
## avg_AFexp 0.39915 0.23132 1.725 0.08454 .
## avg_ANexp -0.02047 0.01162 -1.761 0.07828 .
## avg.sum.media.exp 0.04820 0.02836 1.700 0.08932 .
## Rep_1:avg_AFexp 0.28636 0.28688 0.998 0.31827
## Rep_1:avg_ANexp -0.01109 0.01388 -0.799 0.42409
## Dem_1:avg_AFexp -0.72704 0.26475 -2.746 0.00607 **
## Dem_1:avg_ANexp 0.03275 0.01281 2.558 0.01059 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2996 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0919, Adjusted R-squared: 0.08917
## F-statistic: 33.69 on 9 and 2996 DF, p-value: < 2.2e-162. avg_vaxxAttitudes ~ avg_AFFECT * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * avg_AFexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5835 -1.4660 0.2051 1.6555 3.5264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1296062 0.0615146 -2.107 0.0352 *
## DvR -1.0010529 0.1368788 -7.313 3.33e-13 ***
## IvDR 0.5922333 0.1410635 4.198 2.77e-05 ***
## avg_AFexp 0.0487198 0.0726431 0.671 0.5025
## avg.sum.media.exp 0.0254917 0.0236508 1.078 0.2812
## DvR:avg_AFexp 0.1056871 0.0266707 3.963 7.58e-05 ***
## IvDR:avg_AFexp 0.0007336 0.0279732 0.026 0.9791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0855, Adjusted R-squared: 0.08367
## F-statistic: 46.73 on 6 and 2999 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_AFexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5835 -1.4660 0.2051 1.6555 3.5264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.566357 0.104697 5.409 6.82e-08 ***
## Rep_1 -1.001053 0.136879 -7.313 3.33e-13 ***
## Ind_1 -1.092760 0.161684 -6.759 1.67e-11 ***
## avg_AFexp -0.003882 0.074089 -0.052 0.9582
## avg.sum.media.exp 0.025492 0.023651 1.078 0.2812
## Rep_1:avg_AFexp 0.105687 0.026671 3.963 7.58e-05 ***
## Ind_1:avg_AFexp 0.052110 0.029836 1.747 0.0808 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0855, Adjusted R-squared: 0.08367
## F-statistic: 46.73 on 6 and 2999 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_AFexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5835 -1.4660 0.2051 1.6555 3.5264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.43470 0.08846 -4.914 9.41e-07 ***
## Dem_1 1.00105 0.13688 7.313 3.33e-13 ***
## Ind_1 -0.09171 0.15174 -0.604 0.5456
## avg_AFexp 0.10181 0.07449 1.367 0.1719
## avg.sum.media.exp 0.02549 0.02365 1.078 0.2812
## Dem_1:avg_AFexp -0.10569 0.02667 -3.963 7.58e-05 ***
## Ind_1:avg_AFexp -0.05358 0.03210 -1.669 0.0952 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0855, Adjusted R-squared: 0.08367
## F-statistic: 46.73 on 6 and 2999 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_AFexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5835 -1.4660 0.2051 1.6555 3.5264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52640 0.12344 -4.265 2.07e-05 ***
## Rep_1 0.09171 0.15174 0.604 0.5456
## Dem_1 1.09276 0.16168 6.759 1.67e-11 ***
## avg_AFexp 0.04823 0.07530 0.640 0.5219
## avg.sum.media.exp 0.02549 0.02365 1.078 0.2812
## Rep_1:avg_AFexp 0.05358 0.03210 1.669 0.0952 .
## Dem_1:avg_AFexp -0.05211 0.02984 -1.747 0.0808 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.0855, Adjusted R-squared: 0.08367
## F-statistic: 46.73 on 6 and 2999 DF, p-value: < 2.2e-163. avg_vaxxAttitudes ~ avg_ANALYTIC * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * avg_ANexp + avg.sum.media.exp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * avg_ANexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6426 -1.4758 0.2057 1.6449 3.5074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1226627 0.0603775 -2.032 0.042282 *
## DvR -0.9245308 0.1322568 -6.990 3.36e-12 ***
## IvDR 0.5742146 0.1381944 4.155 3.34e-05 ***
## avg_ANexp -0.0012690 0.0040775 -0.311 0.755659
## avg.sum.media.exp 0.0492535 0.0275911 1.785 0.074343 .
## DvR:avg_ANexp 0.0043829 0.0012793 3.426 0.000621 ***
## IvDR:avg_ANexp 0.0001313 0.0013549 0.097 0.922819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08419, Adjusted R-squared: 0.08236
## F-statistic: 45.95 on 6 and 2999 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_ANexp + avg.sum.media.exp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_ANexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6426 -1.4758 0.2057 1.6449 3.5074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.529093 0.101785 5.198 2.15e-07 ***
## Rep_1 -0.924531 0.132257 -6.990 3.36e-12 ***
## Ind_1 -1.036480 0.157940 -6.562 6.21e-11 ***
## avg_ANexp -0.003417 0.004116 -0.830 0.406530
## avg.sum.media.exp 0.049253 0.027591 1.785 0.074343 .
## Rep_1:avg_ANexp 0.004383 0.001279 3.426 0.000621 ***
## Ind_1:avg_ANexp 0.002060 0.001442 1.429 0.153163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08419, Adjusted R-squared: 0.08236
## F-statistic: 45.95 on 6 and 2999 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_ANexp + avg.sum.media.exp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_ANexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6426 -1.4758 0.2057 1.6449 3.5074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3954373 0.0860708 -4.594 4.52e-06 ***
## Dem_1 0.9245308 0.1322568 6.990 3.36e-12 ***
## Ind_1 -0.1119492 0.1483113 -0.755 0.450413
## avg_ANexp 0.0009658 0.0041480 0.233 0.815905
## avg.sum.media.exp 0.0492535 0.0275911 1.785 0.074343 .
## Dem_1:avg_ANexp -0.0043829 0.0012793 -3.426 0.000621 ***
## Ind_1:avg_ANexp -0.0023227 0.0015527 -1.496 0.134766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08419, Adjusted R-squared: 0.08236
## F-statistic: 45.95 on 6 and 2999 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_ANexp + avg.sum.media.exp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_ANexp +
## avg.sum.media.exp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6426 -1.4758 0.2057 1.6449 3.5074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.507387 0.121174 -4.187 2.90e-05 ***
## Rep_1 0.111949 0.148311 0.755 0.4504
## Dem_1 1.036480 0.157940 6.562 6.21e-11 ***
## avg_ANexp -0.001357 0.004216 -0.322 0.7476
## avg.sum.media.exp 0.049253 0.027591 1.785 0.0743 .
## Rep_1:avg_ANexp 0.002323 0.001553 1.496 0.1348
## Dem_1:avg_ANexp -0.002060 0.001442 -1.429 0.1532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 2999 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08419, Adjusted R-squared: 0.08236
## F-statistic: 45.95 on 6 and 2999 DF, p-value: < 2.2e-164. avg_ANALYTIC ~ avg_AFFECT * party
a. contrast model
model1.cc <- lm(avg_ANexp ~ (DvR + IvDR) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avg_ANexp ~ (DvR + IvDR) * avg_AFexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.475 -3.734 0.463 3.365 70.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.780e+00 2.055e-01 -13.530 < 2e-16 ***
## DvR 2.530e-02 4.571e-01 0.055 0.955867
## IvDR -1.809e+00 4.714e-01 -3.838 0.000126 ***
## avg_AFexp 1.189e+01 2.427e-01 48.974 < 2e-16 ***
## avg.sum.media.exp 2.886e+00 7.903e-02 36.521 < 2e-16 ***
## DvR:avg_AFexp -4.813e-05 8.908e-02 -0.001 0.999569
## IvDR:avg_AFexp 1.387e-01 9.348e-02 1.484 0.137838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.514 on 3002 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9907, Adjusted R-squared: 0.9906
## F-statistic: 5.307e+04 on 6 and 3002 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avg_ANexp ~ (Rep_1 + Ind_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avg_ANexp ~ (Rep_1 + Ind_1) * avg_AFexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.475 -3.734 0.463 3.365 70.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.390e+00 3.499e-01 -9.688 < 2e-16 ***
## Rep_1 2.530e-02 4.571e-01 0.055 0.955867
## Ind_1 1.822e+00 5.404e-01 3.372 0.000756 ***
## avg_AFexp 1.193e+01 2.475e-01 48.205 < 2e-16 ***
## avg.sum.media.exp 2.886e+00 7.903e-02 36.521 < 2e-16 ***
## Rep_1:avg_AFexp -4.813e-05 8.908e-02 -0.001 0.999569
## Ind_1:avg_AFexp -1.388e-01 9.971e-02 -1.392 0.164115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.514 on 3002 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9907, Adjusted R-squared: 0.9906
## F-statistic: 5.307e+04 on 6 and 3002 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avg_ANexp ~ (Dem_1 + Ind_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avg_ANexp ~ (Dem_1 + Ind_1) * avg_AFexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.475 -3.734 0.463 3.365 70.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.365e+00 2.951e-01 -11.403 < 2e-16 ***
## Dem_1 -2.530e-02 4.571e-01 -0.055 0.955867
## Ind_1 1.797e+00 5.068e-01 3.545 0.000398 ***
## avg_AFexp 1.193e+01 2.489e-01 47.943 < 2e-16 ***
## avg.sum.media.exp 2.886e+00 7.903e-02 36.521 < 2e-16 ***
## Dem_1:avg_AFexp 4.813e-05 8.908e-02 0.001 0.999569
## Ind_1:avg_AFexp -1.387e-01 1.072e-01 -1.294 0.195920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.514 on 3002 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9907, Adjusted R-squared: 0.9906
## F-statistic: 5.307e+04 on 6 and 3002 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avg_ANexp ~ (Rep_1 + Dem_1) * avg_AFexp + avg.sum.media.exp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avg_ANexp ~ (Rep_1 + Dem_1) * avg_AFexp + avg.sum.media.exp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.475 -3.734 0.463 3.365 70.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.56816 0.41255 -3.801 0.000147 ***
## Rep_1 -1.79670 0.50680 -3.545 0.000398 ***
## Dem_1 -1.82200 0.54038 -3.372 0.000756 ***
## avg_AFexp 11.79415 0.25162 46.872 < 2e-16 ***
## avg.sum.media.exp 2.88619 0.07903 36.521 < 2e-16 ***
## Rep_1:avg_AFexp 0.13872 0.10724 1.294 0.195920
## Dem_1:avg_AFexp 0.13877 0.09971 1.392 0.164115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.514 on 3002 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9907, Adjusted R-squared: 0.9906
## F-statistic: 5.307e+04 on 6 and 3002 DF, p-value: < 2.2e-16II. WAVE 1
1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7814 -1.5854 0.2658 1.7978 3.8167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.017886 0.065554 -0.273 0.784993
## DvR -1.395220 0.146930 -9.496 < 2e-16 ***
## IvDR 0.589510 0.149672 3.939 8.38e-05 ***
## index_AFexp 0.400071 0.131402 3.045 0.002350 **
## index_ANexp 0.003505 0.011179 0.314 0.753868
## sum.media.exp -0.116321 0.095976 -1.212 0.225615
## DvR:index_AFexp 0.782035 0.199563 3.919 9.10e-05 ***
## DvR:index_ANexp -0.031875 0.009577 -3.328 0.000884 ***
## IvDR:index_AFexp -0.249645 0.244603 -1.021 0.307521
## IvDR:index_ANexp 0.012590 0.011872 1.060 0.289009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3032 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09379, Adjusted R-squared: 0.0911
## F-statistic: 34.87 on 9 and 3032 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d1)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d1)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7814 -1.5854 0.2658 1.7978 3.8167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.874263 0.109222 8.004 1.69e-15 ***
## Rep_1 -1.395220 0.146930 -9.496 < 2e-16 ***
## Ind_1 -1.287121 0.169887 -7.576 4.69e-14 ***
## index_AFexp -0.073329 0.147142 -0.498 0.618268
## index_ANexp 0.023597 0.011660 2.024 0.043083 *
## sum.media.exp -0.116321 0.095976 -1.212 0.225615
## Rep_1:index_AFexp 0.782035 0.199563 3.919 9.10e-05 ***
## Rep_1:index_ANexp -0.031875 0.009577 -3.328 0.000884 ***
## Ind_1:index_AFexp 0.640662 0.253752 2.525 0.011629 *
## Ind_1:index_ANexp -0.028527 0.012292 -2.321 0.020360 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3032 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09379, Adjusted R-squared: 0.0911
## F-statistic: 34.87 on 9 and 3032 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7814 -1.5854 0.2658 1.7978 3.8167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.520958 0.098994 -5.263 1.52e-07 ***
## Dem_1 1.395220 0.146930 9.496 < 2e-16 ***
## Ind_1 0.108100 0.163511 0.661 0.508589
## index_AFexp 0.708705 0.181177 3.912 9.37e-05 ***
## index_ANexp -0.008277 0.012615 -0.656 0.511763
## sum.media.exp -0.116321 0.095976 -1.212 0.225615
## Dem_1:index_AFexp -0.782035 0.199563 -3.919 9.10e-05 ***
## Dem_1:index_ANexp 0.031875 0.009577 3.328 0.000884 ***
## Ind_1:index_AFexp -0.141373 0.274197 -0.516 0.606180
## Ind_1:index_ANexp 0.003347 0.013291 0.252 0.801176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3032 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09379, Adjusted R-squared: 0.0911
## F-statistic: 34.87 on 9 and 3032 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7814 -1.5854 0.2658 1.7978 3.8167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.412858 0.130118 -3.173 0.00152 **
## Rep_1 -0.108100 0.163511 -0.661 0.50859
## Dem_1 1.287121 0.169887 7.576 4.69e-14 ***
## index_AFexp 0.567333 0.239422 2.370 0.01787 *
## index_ANexp -0.004930 0.014836 -0.332 0.73970
## sum.media.exp -0.116321 0.095976 -1.212 0.22561
## Rep_1:index_AFexp 0.141373 0.274197 0.516 0.60618
## Rep_1:index_ANexp -0.003347 0.013291 -0.252 0.80118
## Dem_1:index_AFexp -0.640662 0.253752 -2.525 0.01163 *
## Dem_1:index_ANexp 0.028527 0.012292 2.321 0.02036 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3032 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09379, Adjusted R-squared: 0.0911
## F-statistic: 34.87 on 9 and 3032 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7145 -1.6353 0.2294 1.8119 3.3982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0214020 0.0646780 0.331 0.740743
## DvR -1.1985609 0.1354453 -8.849 < 2e-16 ***
## IvDR 0.5444572 0.1434898 3.794 0.000151 ***
## index_ANexp 0.0008956 0.0102429 0.087 0.930333
## sum.media.exp 0.0313584 0.0691544 0.453 0.650254
## DvR:index_ANexp 0.0054004 0.0012366 4.367 1.3e-05 ***
## IvDR:index_ANexp 0.0005394 0.0013426 0.402 0.687907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08719, Adjusted R-squared: 0.08539
## F-statistic: 48.32 on 6 and 3035 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_ANexp, data = d1)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7145 -1.6353 0.2294 1.8119 3.3982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.800353 0.104572 7.654 2.61e-14 ***
## Rep_1 -1.198561 0.135445 -8.849 < 2e-16 ***
## Ind_1 -1.143738 0.162294 -7.047 2.25e-12 ***
## index_ANexp -0.001627 0.010218 -0.159 0.874
## sum.media.exp 0.031358 0.069154 0.453 0.650
## Rep_1:index_ANexp 0.005400 0.001237 4.367 1.30e-05 ***
## Ind_1:index_ANexp 0.002161 0.001428 1.513 0.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08719, Adjusted R-squared: 0.08539
## F-statistic: 48.32 on 6 and 3035 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7145 -1.6353 0.2294 1.8119 3.3982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.398208 0.093225 -4.271 2.0e-05 ***
## Dem_1 1.198561 0.135445 8.849 < 2e-16 ***
## Ind_1 0.054823 0.154958 0.354 0.7235
## index_ANexp 0.003774 0.010251 0.368 0.7128
## sum.media.exp 0.031358 0.069154 0.453 0.6503
## Dem_1:index_ANexp -0.005400 0.001237 -4.367 1.3e-05 ***
## Ind_1:index_ANexp -0.003240 0.001527 -2.122 0.0339 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08719, Adjusted R-squared: 0.08539
## F-statistic: 48.32 on 6 and 3035 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7145 -1.6353 0.2294 1.8119 3.3982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3433843 0.1259344 -2.727 0.00643 **
## Rep_1 -0.0548233 0.1549581 -0.354 0.72352
## Dem_1 1.1437376 0.1622940 7.047 2.25e-12 ***
## index_ANexp 0.0005342 0.0103558 0.052 0.95886
## sum.media.exp 0.0313584 0.0691544 0.453 0.65025
## Rep_1:index_ANexp 0.0032396 0.0015267 2.122 0.03393 *
## Dem_1:index_ANexp -0.0021608 0.0014278 -1.513 0.13029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08719, Adjusted R-squared: 0.08539
## F-statistic: 48.32 on 6 and 3035 DF, p-value: < 2.2e-163. vaxxAttitudes ~ AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6788 -1.6072 0.2408 1.7900 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.014933 0.064350 -0.232 0.816509
## DvR -1.260312 0.140485 -8.971 < 2e-16 ***
## IvDR 0.548277 0.145924 3.757 0.000175 ***
## index_AFexp 0.236597 0.114366 2.069 0.038651 *
## sum.media.exp -0.038836 0.037171 -1.045 0.296195
## DvR:index_AFexp 0.122999 0.025748 4.777 1.86e-06 ***
## IvDR:index_AFexp 0.009522 0.027589 0.345 0.730012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08965, Adjusted R-squared: 0.08785
## F-statistic: 49.81 on 6 and 3035 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_AFexp, data = d1)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6788 -1.6072 0.2408 1.7900 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.79615 0.10712 7.432 1.38e-13 ***
## Rep_1 -1.26031 0.14048 -8.971 < 2e-16 ***
## Ind_1 -1.17843 0.16611 -7.094 1.61e-12 ***
## index_AFexp 0.17824 0.11543 1.544 0.1227
## sum.media.exp -0.03884 0.03717 -1.045 0.2962
## Rep_1:index_AFexp 0.12300 0.02575 4.777 1.86e-06 ***
## Ind_1:index_AFexp 0.05198 0.02942 1.766 0.0774 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08965, Adjusted R-squared: 0.08785
## F-statistic: 49.81 on 6 and 3035 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6788 -1.6072 0.2408 1.7900 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.46416 0.09284 -5.000 6.07e-07 ***
## Dem_1 1.26031 0.14048 8.971 < 2e-16 ***
## Ind_1 0.08188 0.15768 0.519 0.60361
## index_AFexp 0.30124 0.11533 2.612 0.00904 **
## sum.media.exp -0.03884 0.03717 -1.045 0.29619
## Dem_1:index_AFexp -0.12300 0.02575 -4.777 1.86e-06 ***
## Ind_1:index_AFexp -0.07102 0.03143 -2.260 0.02392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08965, Adjusted R-squared: 0.08785
## F-statistic: 49.81 on 6 and 3035 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6788 -1.6072 0.2408 1.7900 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.38228 0.12820 -2.982 0.00289 **
## Rep_1 -0.08188 0.15768 -0.519 0.60361
## Dem_1 1.17843 0.16611 7.094 1.61e-12 ***
## index_AFexp 0.23022 0.11599 1.985 0.04725 *
## sum.media.exp -0.03884 0.03717 -1.045 0.29619
## Rep_1:index_AFexp 0.07102 0.03143 2.260 0.02392 *
## Dem_1:index_AFexp -0.05198 0.02942 -1.766 0.07742 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3035 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08965, Adjusted R-squared: 0.08785
## F-statistic: 49.81 on 6 and 3035 DF, p-value: < 2.2e-164. ANALYTIC ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.526 -1.874 0.297 1.799 99.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.1408600 0.1063953 -10.723 < 2e-16 ***
## DvR -0.9263582 0.2322042 -3.989 6.78e-05 ***
## IvDR -1.2068607 0.2413420 -5.001 6.04e-07 ***
## index_AFexp -4.1379970 0.1890828 -21.885 < 2e-16 ***
## sum.media.exp 8.0838849 0.0614576 131.536 < 2e-16 ***
## DvR:index_AFexp 0.0004984 0.0425651 0.012 0.99066
## IvDR:index_AFexp 0.1308585 0.0456307 2.868 0.00416 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
## F-statistic: 2.256e+05 on 6 and 3038 DF, p-value: < 2.2e-16plot
m1 <- lm(index_ANexp ~ party_factor * index_AFexp, data = d1)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("media analytic thinking ") +
xlim(0, 15) +
theme_minimal()+
labs(color ='partisan identity') ## Warning: Removed 3 row(s) containing missing values (geom_path).
b. simple effects Dem
model1.dem <- lm(index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.526 -1.874 0.297 1.799 99.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0759449 0.1772003 -6.072 1.42e-09 ***
## Rep_1 -0.9263582 0.2322042 -3.989 6.78e-05 ***
## Ind_1 0.7436816 0.2747792 2.706 0.00684 **
## index_AFexp -4.0950629 0.1908416 -21.458 < 2e-16 ***
## sum.media.exp 8.0838849 0.0614576 131.536 < 2e-16 ***
## Rep_1:index_AFexp 0.0004984 0.0425651 0.012 0.99066
## Ind_1:index_AFexp -0.1306093 0.0486721 -2.683 0.00733 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
## F-statistic: 2.256e+05 on 6 and 3038 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.526 -1.874 0.297 1.799 99.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.0023031 0.1532743 -13.064 < 2e-16 ***
## Dem_1 0.9263582 0.2322042 3.989 6.78e-05 ***
## Ind_1 1.6700398 0.2606678 6.407 1.72e-10 ***
## index_AFexp -4.0945644 0.1906671 -21.475 < 2e-16 ***
## sum.media.exp 8.0838849 0.0614576 131.536 < 2e-16 ***
## Dem_1:index_AFexp -0.0004984 0.0425651 -0.012 0.9907
## Ind_1:index_AFexp -0.1311077 0.0519734 -2.523 0.0117 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
## F-statistic: 2.256e+05 on 6 and 3038 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.526 -1.874 0.297 1.799 99.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33226 0.21206 -1.567 0.11726
## Rep_1 -1.67004 0.26067 -6.407 1.72e-10 ***
## Dem_1 -0.74368 0.27478 -2.706 0.00684 **
## index_AFexp -4.22567 0.19178 -22.034 < 2e-16 ***
## sum.media.exp 8.08388 0.06146 131.536 < 2e-16 ***
## Rep_1:index_AFexp 0.13111 0.05197 2.523 0.01170 *
## Dem_1:index_AFexp 0.13061 0.04867 2.683 0.00733 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
## F-statistic: 2.256e+05 on 6 and 3038 DF, p-value: < 2.2e-16III. WAVE 2
1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5047 -1.5467 0.1081 1.6937 3.5544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.171731 0.072521 -2.368 0.01796 *
## DvR -0.657304 0.156453 -4.201 2.75e-05 ***
## IvDR 0.484129 0.169175 2.862 0.00425 **
## index_AFexp 0.173546 0.158001 1.098 0.27215
## index_ANexp 0.005042 0.011172 0.451 0.65179
## sum.media.exp -0.052964 0.086093 -0.615 0.53848
## DvR:index_AFexp 0.792943 0.249857 3.174 0.00152 **
## DvR:index_ANexp -0.035920 0.011865 -3.027 0.00249 **
## IvDR:index_AFexp 0.084393 0.348676 0.242 0.80877
## IvDR:index_ANexp -0.005050 0.016735 -0.302 0.76286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2396 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05416, Adjusted R-squared: 0.05061
## F-statistic: 15.25 on 9 and 2396 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5047 -1.5467 0.1081 1.6937 3.5544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31668 0.11937 2.653 0.00803 **
## Rep_1 -0.65730 0.15645 -4.201 2.75e-05 ***
## Ind_1 -0.81278 0.19143 -4.246 2.26e-05 ***
## index_AFexp -0.19508 0.18116 -1.077 0.28166
## index_ANexp 0.02134 0.01122 1.902 0.05731 .
## sum.media.exp -0.05296 0.08609 -0.615 0.53848
## Rep_1:index_AFexp 0.79294 0.24986 3.174 0.00152 **
## Rep_1:index_ANexp -0.03592 0.01187 -3.027 0.00249 **
## Ind_1:index_AFexp 0.31208 0.36116 0.864 0.38763
## Ind_1:index_ANexp -0.01291 0.01731 -0.746 0.45589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2396 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05416, Adjusted R-squared: 0.05061
## F-statistic: 15.25 on 9 and 2396 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5047 -1.5467 0.1081 1.6937 3.5544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.34062 0.10239 -3.327 0.000892 ***
## Dem_1 0.65730 0.15645 4.201 2.75e-05 ***
## Ind_1 -0.15548 0.18120 -0.858 0.390968
## index_AFexp 0.59787 0.20627 2.898 0.003785 **
## index_ANexp -0.01458 0.01331 -1.096 0.273167
## sum.media.exp -0.05296 0.08609 -0.615 0.538480
## Dem_1:index_AFexp -0.79294 0.24986 -3.174 0.001525 **
## Dem_1:index_ANexp 0.03592 0.01187 3.027 0.002493 **
## Ind_1:index_AFexp -0.48086 0.37937 -1.268 0.205092
## Ind_1:index_ANexp 0.02301 0.01819 1.265 0.205976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2396 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05416, Adjusted R-squared: 0.05061
## F-statistic: 15.25 on 9 and 2396 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp + index_ANexp) + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5047 -1.5467 0.1081 1.6937 3.5544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.496097 0.149761 -3.313 0.000938 ***
## Rep_1 0.155477 0.181205 0.858 0.390968
## Dem_1 0.812781 0.191427 4.246 2.26e-05 ***
## index_AFexp 0.117002 0.335087 0.349 0.726993
## index_ANexp 0.008426 0.018162 0.464 0.642736
## sum.media.exp -0.052964 0.086093 -0.615 0.538480
## Rep_1:index_AFexp 0.480865 0.379374 1.268 0.205092
## Rep_1:index_ANexp -0.023010 0.018189 -1.265 0.205976
## Dem_1:index_AFexp -0.312078 0.361165 -0.864 0.387626
## Dem_1:index_ANexp 0.012910 0.017311 0.746 0.455886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2396 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05416, Adjusted R-squared: 0.05061
## F-statistic: 15.25 on 9 and 2396 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6103 -1.5707 0.1347 1.7494 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.151720 0.070910 -2.140 0.032486 *
## DvR -0.477310 0.144442 -3.305 0.000965 ***
## IvDR 0.495896 0.161408 3.072 0.002148 **
## index_ANexp 0.009690 0.010405 0.931 0.351800
## sum.media.exp -0.028923 0.069352 -0.417 0.676676
## DvR:index_ANexp 0.001514 0.001423 1.064 0.287509
## IvDR:index_ANexp -0.001062 0.001715 -0.619 0.535922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.0498, Adjusted R-squared: 0.04742
## F-statistic: 20.95 on 6 and 2399 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_ANexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6103 -1.5707 0.1347 1.7494 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.250581 0.113972 2.199 0.028001 *
## Rep_1 -0.477310 0.144442 -3.305 0.000965 ***
## Ind_1 -0.734551 0.181933 -4.037 5.57e-05 ***
## index_ANexp 0.008583 0.010394 0.826 0.409019
## sum.media.exp -0.028923 0.069352 -0.417 0.676676
## Rep_1:index_ANexp 0.001514 0.001423 1.064 0.287509
## Ind_1:index_ANexp 0.001819 0.001798 1.012 0.311793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.0498, Adjusted R-squared: 0.04742
## F-statistic: 20.95 on 6 and 2399 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6103 -1.5707 0.1347 1.7494 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2267295 0.0962763 -2.355 0.018603 *
## Dem_1 0.4773101 0.1444423 3.305 0.000965 ***
## Ind_1 -0.2572408 0.1715729 -1.499 0.133925
## index_ANexp 0.0100965 0.0104058 0.970 0.332006
## sum.media.exp -0.0289234 0.0693517 -0.417 0.676676
## Dem_1:index_ANexp -0.0015135 0.0014227 -1.064 0.287509
## Ind_1:index_ANexp 0.0003051 0.0019144 0.159 0.873374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.0498, Adjusted R-squared: 0.04742
## F-statistic: 20.95 on 6 and 2399 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6103 -1.5707 0.1347 1.7494 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4839703 0.1438261 -3.365 0.000778 ***
## Rep_1 0.2572408 0.1715729 1.499 0.133925
## Dem_1 0.7345509 0.1819332 4.037 5.57e-05 ***
## index_ANexp 0.0104017 0.0105589 0.985 0.324669
## sum.media.exp -0.0289234 0.0693517 -0.417 0.676676
## Rep_1:index_ANexp -0.0003051 0.0019144 -0.159 0.873374
## Dem_1:index_ANexp -0.0018187 0.0017977 -1.012 0.311793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.0498, Adjusted R-squared: 0.04742
## F-statistic: 20.95 on 6 and 2399 DF, p-value: < 2.2e-163. vaxxAttitudes ~ AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6556 -1.5841 0.1218 1.7638 3.5047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.181704 0.070384 -2.582 0.009893 **
## DvR -0.517549 0.149558 -3.461 0.000549 ***
## IvDR 0.482096 0.164126 2.937 0.003342 **
## index_AFexp 0.132683 0.140331 0.946 0.344497
## sum.media.exp -0.006192 0.044558 -0.139 0.889491
## DvR:index_AFexp 0.042568 0.029966 1.421 0.155581
## IvDR:index_AFexp -0.018689 0.035703 -0.523 0.600698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05018, Adjusted R-squared: 0.0478
## F-statistic: 21.12 on 6 and 2399 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_AFexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6556 -1.5841 0.1218 1.7638 3.5047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.236162 0.115604 2.043 0.041176 *
## Rep_1 -0.517549 0.149558 -3.461 0.000549 ***
## Ind_1 -0.740870 0.185755 -3.988 6.85e-05 ***
## index_AFexp 0.105232 0.141535 0.744 0.457251
## sum.media.exp -0.006192 0.044558 -0.139 0.889491
## Rep_1:index_AFexp 0.042568 0.029966 1.421 0.155581
## Ind_1:index_AFexp 0.039973 0.037507 1.066 0.286639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05018, Adjusted R-squared: 0.0478
## F-statistic: 21.12 on 6 and 2399 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6556 -1.5841 0.1218 1.7638 3.5047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.281387 0.096298 -2.922 0.003510 **
## Dem_1 0.517549 0.149558 3.461 0.000549 ***
## Ind_1 -0.223321 0.174797 -1.278 0.201512
## index_AFexp 0.147800 0.141035 1.048 0.294761
## sum.media.exp -0.006192 0.044558 -0.139 0.889491
## Dem_1:index_AFexp -0.042568 0.029966 -1.421 0.155581
## Ind_1:index_AFexp -0.002595 0.039895 -0.065 0.948146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05018, Adjusted R-squared: 0.0478
## F-statistic: 21.12 on 6 and 2399 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6556 -1.5841 0.1218 1.7638 3.5047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.504708 0.146399 -3.447 0.000576 ***
## Rep_1 0.223321 0.174797 1.278 0.201512
## Dem_1 0.740870 0.185755 3.988 6.85e-05 ***
## index_AFexp 0.145205 0.143026 1.015 0.310098
## sum.media.exp -0.006192 0.044558 -0.139 0.889491
## Rep_1:index_AFexp 0.002595 0.039895 0.065 0.948146
## Dem_1:index_AFexp -0.039973 0.037507 -1.066 0.286639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2399 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05018, Adjusted R-squared: 0.0478
## F-statistic: 21.12 on 6 and 2399 DF, p-value: < 2.2e-164. ANALYTIC ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.842 -2.197 0.514 2.101 114.644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.63006 0.13776 -11.833 < 2e-16 ***
## DvR -0.11590 0.29266 -0.396 0.6921
## IvDR -1.36072 0.32139 -4.234 2.38e-05 ***
## index_AFexp -1.21173 0.27428 -4.418 1.04e-05 ***
## sum.media.exp 7.04138 0.08709 80.849 < 2e-16 ***
## DvR:index_AFexp 0.05721 0.05867 0.975 0.3296
## IvDR:index_AFexp 0.15009 0.06992 2.147 0.0319 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.035 on 2401 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9966
## F-statistic: 1.191e+05 on 6 and 2401 DF, p-value: < 2.2e-16plot
m1 <- lm(index_ANexp ~ party_factor * index_AFexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("media analytic thinking") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.842 -2.197 0.514 2.101 114.644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.02114 0.22615 -8.937 < 2e-16 ***
## Rep_1 -0.11590 0.29266 -0.396 0.692111
## Ind_1 1.30277 0.36367 3.582 0.000347 ***
## index_AFexp -1.19080 0.27659 -4.305 1.73e-05 ***
## sum.media.exp 7.04138 0.08709 80.849 < 2e-16 ***
## Rep_1:index_AFexp 0.05721 0.05867 0.975 0.329578
## Ind_1:index_AFexp -0.12148 0.07345 -1.654 0.098262 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.035 on 2401 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9966
## F-statistic: 1.191e+05 on 6 and 2401 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.842 -2.197 0.514 2.101 114.644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.13705 0.18846 -11.339 < 2e-16 ***
## Dem_1 0.11590 0.29266 0.396 0.6921
## Ind_1 1.41867 0.34227 4.145 3.52e-05 ***
## index_AFexp -1.13359 0.27568 -4.112 4.06e-05 ***
## sum.media.exp 7.04138 0.08709 80.849 < 2e-16 ***
## Dem_1:index_AFexp -0.05721 0.05867 -0.975 0.3296
## Ind_1:index_AFexp -0.17869 0.07813 -2.287 0.0223 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.035 on 2401 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9966
## F-statistic: 1.191e+05 on 6 and 2401 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.842 -2.197 0.514 2.101 114.644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.71838 0.28672 -2.505 0.012294 *
## Rep_1 -1.41867 0.34227 -4.145 3.52e-05 ***
## Dem_1 -1.30277 0.36367 -3.582 0.000347 ***
## index_AFexp -1.31229 0.27959 -4.694 2.83e-06 ***
## sum.media.exp 7.04138 0.08709 80.849 < 2e-16 ***
## Rep_1:index_AFexp 0.17869 0.07813 2.287 0.022272 *
## Dem_1:index_AFexp 0.12148 0.07345 1.654 0.098262 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.035 on 2401 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9966
## F-statistic: 1.191e+05 on 6 and 2401 DF, p-value: < 2.2e-165. vote legitimacy ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(voteLegit ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = voteLegit ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.083889 0.053612 57.522 < 2e-16 ***
## DvR -1.331036 0.109212 -12.188 < 2e-16 ***
## IvDR 0.655348 0.123784 5.294 1.42e-07 ***
## index_ANexp 0.001512 0.006992 0.216 0.8288
## sum.media.exp 0.013901 0.046579 0.298 0.7654
## DvR:index_ANexp 0.002251 0.001054 2.136 0.0329 *
## IvDR:index_ANexp -0.001499 0.001232 -1.217 0.2238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16plot
m1 <- lm(voteLegit ~ party_factor * index_ANexp + sum.media.exp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(voteLegit ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9656722 0.0852520 46.517 <2e-16 ***
## Rep_1 -1.3310365 0.1092120 -12.188 <2e-16 ***
## Ind_1 -1.3208660 0.1389551 -9.506 <2e-16 ***
## index_ANexp -0.0001078 0.0070056 -0.015 0.9877
## sum.media.exp 0.0139005 0.0465793 0.298 0.7654
## Rep_1:index_ANexp 0.0022507 0.0010536 2.136 0.0329 *
## Ind_1:index_ANexp 0.0026247 0.0013092 2.005 0.0452 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(voteLegit ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = voteLegit ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.634636 0.072439 36.370 <2e-16 ***
## Dem_1 1.331036 0.109212 12.188 <2e-16 ***
## Ind_1 0.010171 0.131529 0.077 0.9384
## index_ANexp 0.002143 0.007005 0.306 0.7597
## sum.media.exp 0.013900 0.046579 0.298 0.7654
## Dem_1:index_ANexp -0.002251 0.001054 -2.136 0.0329 *
## Ind_1:index_ANexp 0.000374 0.001370 0.273 0.7849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(voteLegit ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.644806 0.111018 23.823 <2e-16 ***
## Rep_1 -0.010171 0.131529 -0.077 0.9384
## Dem_1 1.320866 0.138955 9.506 <2e-16 ***
## index_ANexp 0.002517 0.007077 0.356 0.7222
## sum.media.exp 0.013900 0.046579 0.298 0.7654
## Rep_1:index_ANexp -0.000374 0.001370 -0.273 0.7849
## Dem_1:index_ANexp -0.002625 0.001309 -2.005 0.0452 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-166. voteLegit ~ ANALYTIC * party * election timing + sum.media.exp
a. contrast model
model1.cc <- lm(voteLegit ~ (DvR + IvDR) * index_ANexp * tDur_Post + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = voteLegit ~ (DvR + IvDR) * index_ANexp * tDur_Post +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.080e+00 5.338e-02 57.702 < 2e-16 ***
## DvR -1.304e+00 1.086e-01 -12.017 < 2e-16 ***
## IvDR 6.417e-01 1.236e-01 5.194 2.42e-07 ***
## index_ANexp 3.689e-03 6.940e-03 0.532 0.59517
## tDur_Post 8.941e-03 1.031e-01 0.087 0.93089
## sum.media.exp -4.282e-05 4.621e-02 -0.001 0.99926
## DvR:index_ANexp 2.355e-03 1.054e-03 2.235 0.02560 *
## IvDR:index_ANexp -1.466e-03 1.226e-03 -1.196 0.23189
## DvR:tDur_Post -9.745e-01 2.173e-01 -4.485 8.00e-06 ***
## IvDR:tDur_Post 1.899e-01 2.466e-01 0.770 0.44153
## index_ANexp:tDur_Post 1.919e-03 1.017e-03 1.887 0.05934 .
## DvR:index_ANexp:tDur_Post 5.472e-03 2.112e-03 2.592 0.00967 **
## IvDR:index_ANexp:tDur_Post -1.187e-03 2.452e-03 -0.484 0.62841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1195 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2787
## F-statistic: 39.86 on 12 and 1195 DF, p-value: < 2.2e-16plot
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing + sum.media.exp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing + sum.media.exp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "election_timing"),
color = c("green", "orange")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='election timing') 
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing + sum.media.exp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor", "election_timing"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(voteLegit ~ (Rep_1 + Ind_1) * index_ANexp * tDur_Post + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Ind_1) * index_ANexp * tDur_Post +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.944e+00 8.490e-02 46.458 < 2e-16 ***
## Rep_1 -1.304e+00 1.086e-01 -12.017 < 2e-16 ***
## Ind_1 -1.294e+00 1.387e-01 -9.327 < 2e-16 ***
## index_ANexp 2.027e-03 6.953e-03 0.292 0.770690
## tDur_Post 5.589e-01 1.665e-01 3.356 0.000815 ***
## sum.media.exp -4.282e-05 4.621e-02 -0.001 0.999261
## Rep_1:index_ANexp 2.355e-03 1.054e-03 2.235 0.025596 *
## Ind_1:index_ANexp 2.644e-03 1.304e-03 2.028 0.042802 *
## Rep_1:tDur_Post -9.745e-01 2.173e-01 -4.485 8e-06 ***
## Ind_1:tDur_Post -6.771e-01 2.771e-01 -2.443 0.014693 *
## index_ANexp:tDur_Post -1.209e-03 1.379e-03 -0.876 0.380989
## Rep_1:index_ANexp:tDur_Post 5.472e-03 2.112e-03 2.592 0.009671 **
## Ind_1:index_ANexp:tDur_Post 3.923e-03 2.611e-03 1.503 0.133172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1195 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2787
## F-statistic: 39.86 on 12 and 1195 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(voteLegit ~ (Dem_1 + Ind_1) * index_ANexp * tDur_Post + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = voteLegit ~ (Dem_1 + Ind_1) * index_ANexp * tDur_Post +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.640e+00 7.166e-02 36.839 < 2e-16 ***
## Dem_1 1.304e+00 1.086e-01 12.017 < 2e-16 ***
## Ind_1 1.054e-02 1.311e-01 0.080 0.93593
## index_ANexp 4.383e-03 6.957e-03 0.630 0.52884
## tDur_Post -4.157e-01 1.395e-01 -2.980 0.00294 **
## sum.media.exp -4.282e-05 4.621e-02 -0.001 0.99926
## Dem_1:index_ANexp -2.355e-03 1.054e-03 -2.235 0.02560 *
## Ind_1:index_ANexp 2.885e-04 1.364e-03 0.212 0.83252
## Dem_1:tDur_Post 9.745e-01 2.173e-01 4.485 8e-06 ***
## Ind_1:tDur_Post 2.974e-01 2.616e-01 1.137 0.25587
## index_ANexp:tDur_Post 4.263e-03 1.597e-03 2.670 0.00768 **
## Dem_1:index_ANexp:tDur_Post -5.472e-03 2.112e-03 -2.592 0.00967 **
## Ind_1:index_ANexp:tDur_Post -1.549e-03 2.728e-03 -0.568 0.57023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1195 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2787
## F-statistic: 39.86 on 12 and 1195 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(voteLegit ~ (Rep_1 + Dem_1) * index_ANexp * tDur_Post + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Dem_1) * index_ANexp * tDur_Post +
## sum.media.exp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.650e+00 1.110e-01 23.883 <2e-16 ***
## Rep_1 -1.054e-02 1.311e-01 -0.080 0.9359
## Dem_1 1.294e+00 1.387e-01 9.327 <2e-16 ***
## index_ANexp 4.671e-03 7.023e-03 0.665 0.5061
## tDur_Post -1.183e-01 2.214e-01 -0.534 0.5933
## sum.media.exp -4.282e-05 4.621e-02 -0.001 0.9993
## Rep_1:index_ANexp -2.885e-04 1.364e-03 -0.212 0.8325
## Dem_1:index_ANexp -2.644e-03 1.304e-03 -2.028 0.0428 *
## Rep_1:tDur_Post -2.974e-01 2.616e-01 -1.137 0.2559
## Dem_1:tDur_Post 6.771e-01 2.771e-01 2.443 0.0147 *
## index_ANexp:tDur_Post 2.714e-03 2.215e-03 1.225 0.2206
## Rep_1:index_ANexp:tDur_Post 1.549e-03 2.728e-03 0.568 0.5702
## Dem_1:index_ANexp:tDur_Post -3.923e-03 2.611e-03 -1.503 0.1332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1195 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2787
## F-statistic: 39.86 on 12 and 1195 DF, p-value: < 2.2e-167. voteLegit ~ ANALYTIC * party * election timing
a. contrast model
model1.cc <- lm(voteLegit ~ (DvR + IvDR) * index_ANexp * tDur_Post, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = voteLegit ~ (DvR + IvDR) * index_ANexp * tDur_Post,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0803101 0.0515162 59.793 < 2e-16 ***
## DvR -1.3044705 0.1085065 -12.022 < 2e-16 ***
## IvDR 0.6416896 0.1232093 5.208 2.24e-07 ***
## index_ANexp 0.0036824 0.0005077 7.253 7.32e-13 ***
## tDur_Post 0.0089411 0.1030325 0.087 0.93086
## DvR:index_ANexp 0.0023555 0.0010534 2.236 0.02553 *
## IvDR:index_ANexp -0.0014662 0.0012252 -1.197 0.23164
## DvR:tDur_Post -0.9745097 0.2170131 -4.491 7.79e-06 ***
## IvDR:tDur_Post 0.1898532 0.2464186 0.770 0.44119
## index_ANexp:tDur_Post 0.0019191 0.0010155 1.890 0.05902 .
## DvR:index_ANexp:tDur_Post 0.0054721 0.0021068 2.597 0.00951 **
## IvDR:index_ANexp:tDur_Post -0.0011871 0.0024504 -0.484 0.62816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1196 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2793
## F-statistic: 43.52 on 11 and 1196 DF, p-value: < 2.2e-16plot
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "election_timing"),
color = c("green", "orange")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='election timing') 
m1 <- lm(voteLegit ~ party_factor * index_ANexp * election_timing, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor", "election_timing"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(voteLegit ~ (Rep_1 + Ind_1) * index_ANexp * tDur_Post, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Ind_1) * index_ANexp * tDur_Post,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.944303 0.083155 47.433 < 2e-16 ***
## Rep_1 -1.304471 0.108507 -12.022 < 2e-16 ***
## Ind_1 -1.293925 0.138390 -9.350 < 2e-16 ***
## index_ANexp 0.002021 0.000689 2.933 0.003422 **
## tDur_Post 0.558848 0.166310 3.360 0.000803 ***
## Rep_1:index_ANexp 0.002355 0.001053 2.236 0.025530 *
## Ind_1:index_ANexp 0.002644 0.001303 2.029 0.042701 *
## Rep_1:tDur_Post -0.974510 0.217013 -4.491 7.79e-06 ***
## Ind_1:tDur_Post -0.677108 0.276781 -2.446 0.014574 *
## index_ANexp:tDur_Post -0.001209 0.001378 -0.877 0.380575
## Rep_1:index_ANexp:tDur_Post 0.005472 0.002107 2.597 0.009509 **
## Ind_1:index_ANexp:tDur_Post 0.003923 0.002606 1.505 0.132548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1196 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2793
## F-statistic: 43.52 on 11 and 1196 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(voteLegit ~ (Dem_1 + Ind_1) * index_ANexp * tDur_Post, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = voteLegit ~ (Dem_1 + Ind_1) * index_ANexp * tDur_Post,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6398324 0.0697058 37.871 < 2e-16 ***
## Dem_1 1.3044705 0.1085065 12.022 < 2e-16 ***
## Ind_1 0.0105456 0.1307517 0.081 0.93573
## index_ANexp 0.0043763 0.0007968 5.492 4.84e-08 ***
## tDur_Post -0.4156622 0.1394116 -2.982 0.00293 **
## Dem_1:index_ANexp -0.0023555 0.0010534 -2.236 0.02553 *
## Ind_1:index_ANexp 0.0002885 0.0013633 0.212 0.83244
## Dem_1:tDur_Post 0.9745097 0.2170131 4.491 7.79e-06 ***
## Ind_1:tDur_Post 0.2974017 0.2615035 1.137 0.25565
## index_ANexp:tDur_Post 0.0042634 0.0015936 2.675 0.00757 **
## Dem_1:index_ANexp:tDur_Post -0.0054721 0.0021068 -2.597 0.00951 **
## Ind_1:index_ANexp:tDur_Post -0.0015490 0.0027266 -0.568 0.57007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1196 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2793
## F-statistic: 43.52 on 11 and 1196 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(voteLegit ~ (Rep_1 + Dem_1) * index_ANexp * tDur_Post, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Dem_1) * index_ANexp * tDur_Post,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3579 -0.8196 0.1284 0.7145 2.5680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6503780 0.1106215 23.959 < 2e-16 ***
## Rep_1 -0.0105456 0.1307517 -0.081 0.9357
## Dem_1 1.2939249 0.1383904 9.350 < 2e-16 ***
## index_ANexp 0.0046648 0.0011062 4.217 2.66e-05 ***
## tDur_Post -0.1182605 0.2212430 -0.535 0.5931
## Rep_1:index_ANexp -0.0002885 0.0013633 -0.212 0.8324
## Dem_1:index_ANexp -0.0026440 0.0013032 -2.029 0.0427 *
## Rep_1:tDur_Post -0.2974017 0.2615035 -1.137 0.2557
## Dem_1:tDur_Post 0.6771081 0.2767807 2.446 0.0146 *
## index_ANexp:tDur_Post 0.0027144 0.0022124 1.227 0.2201
## Rep_1:index_ANexp:tDur_Post 0.0015490 0.0027266 0.568 0.5701
## Dem_1:index_ANexp:tDur_Post -0.0039231 0.0026065 -1.505 0.1325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 1196 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2859, Adjusted R-squared: 0.2793
## F-statistic: 43.52 on 11 and 1196 DF, p-value: < 2.2e-168. voteLegit ~ ANALYTIC * party + sum.media.exposure
a. contrast model
model1.cc <- lm(voteLegit ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = voteLegit ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.083889 0.053612 57.522 < 2e-16 ***
## DvR -1.331036 0.109212 -12.188 < 2e-16 ***
## IvDR 0.655348 0.123784 5.294 1.42e-07 ***
## index_ANexp 0.001512 0.006992 0.216 0.8288
## sum.media.exp 0.013901 0.046579 0.298 0.7654
## DvR:index_ANexp 0.002251 0.001054 2.136 0.0329 *
## IvDR:index_ANexp -0.001499 0.001232 -1.217 0.2238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16plot
m1 <- lm(voteLegit ~ party_factor * index_ANexp + sum.media.exp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity')
b. simple effects Dem
model1.dem <- lm(voteLegit ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9656722 0.0852520 46.517 <2e-16 ***
## Rep_1 -1.3310365 0.1092120 -12.188 <2e-16 ***
## Ind_1 -1.3208660 0.1389551 -9.506 <2e-16 ***
## index_ANexp -0.0001078 0.0070056 -0.015 0.9877
## sum.media.exp 0.0139005 0.0465793 0.298 0.7654
## Rep_1:index_ANexp 0.0022507 0.0010536 2.136 0.0329 *
## Ind_1:index_ANexp 0.0026247 0.0013092 2.005 0.0452 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(voteLegit ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = voteLegit ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.634636 0.072439 36.370 <2e-16 ***
## Dem_1 1.331036 0.109212 12.188 <2e-16 ***
## Ind_1 0.010171 0.131529 0.077 0.9384
## index_ANexp 0.002143 0.007005 0.306 0.7597
## sum.media.exp 0.013900 0.046579 0.298 0.7654
## Dem_1:index_ANexp -0.002251 0.001054 -2.136 0.0329 *
## Ind_1:index_ANexp 0.000374 0.001370 0.273 0.7849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(voteLegit ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp,
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2821 -0.7710 0.1512 0.8232 2.3654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.644806 0.111018 23.823 <2e-16 ***
## Rep_1 -0.010171 0.131529 -0.077 0.9384
## Dem_1 1.320866 0.138955 9.506 <2e-16 ***
## index_ANexp 0.002517 0.007077 0.356 0.7222
## sum.media.exp 0.013900 0.046579 0.298 0.7654
## Rep_1:index_ANexp -0.000374 0.001370 -0.273 0.7849
## Dem_1:index_ANexp -0.002625 0.001309 -2.005 0.0452 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1201 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2657, Adjusted R-squared: 0.262
## F-statistic: 72.42 on 6 and 1201 DF, p-value: < 2.2e-169. voteLegit ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(voteLegit ~ (DvR + IvDR) * index_ANexp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = voteLegit ~ (DvR + IvDR) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2860 -0.7731 0.1563 0.8329 2.3604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0881180 0.0516863 59.747 < 2e-16 ***
## DvR -1.3312559 0.1091681 -12.195 < 2e-16 ***
## IvDR 0.6580432 0.1234070 5.332 1.16e-07 ***
## index_ANexp 0.0035934 0.0005093 7.056 2.90e-12 ***
## DvR:index_ANexp 0.0022471 0.0010532 2.134 0.0331 *
## IvDR:index_ANexp -0.0015063 0.0012313 -1.223 0.2214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1202 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2656, Adjusted R-squared: 0.2626
## F-statistic: 86.95 on 5 and 1202 DF, p-value: < 2.2e-16plot
m1 <- lm(voteLegit ~ party_factor * index_ANexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("vote leigitimacy") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(voteLegit ~ (Rep_1 + Ind_1) * index_ANexp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Ind_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2860 -0.7731 0.1563 0.8329 2.3604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9709002 0.0834009 47.612 < 2e-16 ***
## Rep_1 -1.3312559 0.1091681 -12.195 < 2e-16 ***
## Ind_1 -1.3236712 0.1385842 -9.551 < 2e-16 ***
## index_ANexp 0.0019728 0.0006882 2.867 0.00422 **
## Rep_1:index_ANexp 0.0022471 0.0010532 2.134 0.03307 *
## Ind_1:index_ANexp 0.0026299 0.0013086 2.010 0.04469 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1202 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2656, Adjusted R-squared: 0.2626
## F-statistic: 86.95 on 5 and 1202 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(voteLegit ~ (Dem_1 + Ind_1) * index_ANexp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = voteLegit ~ (Dem_1 + Ind_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2860 -0.7731 0.1563 0.8329 2.3604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6396443 0.0704412 37.473 < 2e-16 ***
## Dem_1 1.3312559 0.1091681 12.195 < 2e-16 ***
## Ind_1 0.0075847 0.1311939 0.058 0.9539
## index_ANexp 0.0042199 0.0007972 5.293 1.43e-07 ***
## Dem_1:index_ANexp -0.0022471 0.0010532 -2.134 0.0331 *
## Ind_1:index_ANexp 0.0003828 0.0013691 0.280 0.7798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1202 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2656, Adjusted R-squared: 0.2626
## F-statistic: 86.95 on 5 and 1202 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(voteLegit ~ (Rep_1 + Dem_1) * index_ANexp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = voteLegit ~ (Rep_1 + Dem_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2860 -0.7731 0.1563 0.8329 2.3604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6472290 0.1106791 23.918 < 2e-16 ***
## Rep_1 -0.0075847 0.1311939 -0.058 0.9539
## Dem_1 1.3236712 0.1385842 9.551 < 2e-16 ***
## index_ANexp 0.0046027 0.0011131 4.135 3.79e-05 ***
## Rep_1:index_ANexp -0.0003828 0.0013691 -0.280 0.7798
## Dem_1:index_ANexp -0.0026299 0.0013086 -2.010 0.0447 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 1202 degrees of freedom
## (1255 observations deleted due to missingness)
## Multiple R-squared: 0.2656, Adjusted R-squared: 0.2626
## F-statistic: 86.95 on 5 and 1202 DF, p-value: < 2.2e-16IV. LONGITUDINAL MODELS
1. vaxxAttitudes.w2 ~ party * (ANALYTIC.w1 + ANALYTIC.w2) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.775e-01 5.269e-02 3.368 0.00077 ***
## index_ANexp.w1 -8.643e-04 6.609e-04 -1.308 0.19111
## index_ANexp.w2 2.003e-03 6.868e-04 2.917 0.00357 **
## DvR 2.968e-01 1.141e-01 2.602 0.00934 **
## IvDR 2.567e-01 1.228e-01 2.090 0.03671 *
## vaxxAttitudes.c.w1 7.204e-01 1.489e-02 48.398 < 2e-16 ***
## index_ANexp.w1:DvR -1.973e-03 1.458e-03 -1.354 0.17598
## index_ANexp.w1:IvDR -1.061e-03 1.524e-03 -0.696 0.48654
## index_ANexp.w2:DvR 1.031e-03 1.549e-03 0.666 0.50560
## index_ANexp.w2:IvDR 6.892e-05 1.565e-03 0.044 0.96489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model2.dem <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1137476 0.0871526 1.305 0.19198
## index_ANexp.w1 -0.0002277 0.0008545 -0.266 0.78992
## index_ANexp.w2 0.0015106 0.0008737 1.729 0.08394 .
## Ind_1 -0.1082906 0.1403182 -0.772 0.44035
## Rep_1 0.2968434 0.1141037 2.602 0.00934 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Ind_1 0.0000740 0.0015892 0.047 0.96287
## index_ANexp.w1:Rep_1 -0.0019734 0.0014578 -1.354 0.17598
## index_ANexp.w2:Ind_1 0.0004467 0.0016162 0.276 0.78229
## index_ANexp.w2:Rep_1 0.0010312 0.0015487 0.666 0.50560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16c. simple effects for Rep
model2.rep <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4105909 0.0730136 5.623 2.12e-08 ***
## index_ANexp.w1 -0.0022010 0.0011827 -1.861 0.06287 .
## index_ANexp.w2 0.0025418 0.0012792 1.987 0.04704 *
## Ind_1 -0.4051340 0.1303350 -3.108 0.00191 **
## Dem_1 -0.2968434 0.1141037 -2.602 0.00934 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Ind_1 0.0020474 0.0017840 1.148 0.25125
## index_ANexp.w1:Dem_1 0.0019734 0.0014578 1.354 0.17598
## index_ANexp.w2:Ind_1 -0.0005845 0.0018677 -0.313 0.75434
## index_ANexp.w2:Dem_1 -0.0010312 0.0015487 -0.666 0.50560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16d. simple effects for Indep
model2.ind <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0054570 0.1095614 0.050 0.96028
## index_ANexp.w1 -0.0001537 0.0013408 -0.115 0.90876
## index_ANexp.w2 0.0019573 0.0013612 1.438 0.15059
## Dem_1 0.1082906 0.1403182 0.772 0.44035
## Rep_1 0.4051340 0.1303350 3.108 0.00191 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Dem_1 -0.0000740 0.0015892 -0.047 0.96287
## index_ANexp.w1:Rep_1 -0.0020474 0.0017840 -1.148 0.25125
## index_ANexp.w2:Dem_1 -0.0004467 0.0016162 -0.276 0.78229
## index_ANexp.w2:Rep_1 0.0005845 0.0018677 0.313 0.75434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-162. vaxxAttitudes.w2 ~ party * (AFFECT.w1 + AFFECT.w2) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1794202 0.0543672 3.300 0.000982 ***
## index_AFexp.w1 -0.0154658 0.0137346 -1.126 0.260272
## index_AFexp.w2 0.0384976 0.0144351 2.667 0.007712 **
## DvR 0.2768726 0.1185634 2.335 0.019623 *
## IvDR 0.2720531 0.1259446 2.160 0.030875 *
## vaxxAttitudes.c.w1 0.7204464 0.0149108 48.317 < 2e-16 ***
## index_AFexp.w1:DvR -0.0383700 0.0304108 -1.262 0.207185
## index_AFexp.w1:IvDR -0.0222708 0.0315830 -0.705 0.480792
## index_AFexp.w2:DvR 0.0243595 0.0326008 0.747 0.455020
## index_AFexp.w2:IvDR -0.0009634 0.0328450 -0.029 0.976602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model2.dem <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.130761 0.090357 1.447 0.1480
## index_AFexp.w1 -0.003630 0.018210 -0.199 0.8420
## index_AFexp.w2 0.026000 0.018740 1.387 0.1655
## Ind_1 -0.133617 0.144220 -0.926 0.3543
## Rep_1 0.276873 0.118563 2.335 0.0196 *
## vaxxAttitudes.c.w1 0.720446 0.014911 48.317 <2e-16 ***
## index_AFexp.w1:Ind_1 0.003086 0.033165 0.093 0.9259
## index_AFexp.w1:Rep_1 -0.038370 0.030411 -1.262 0.2072
## index_AFexp.w2:Ind_1 0.013143 0.034108 0.385 0.7000
## index_AFexp.w2:Rep_1 0.024360 0.032601 0.747 0.4550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16c. simple effects for Rep
model2.rep <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40763 0.07614 5.354 9.53e-08 ***
## index_AFexp.w1 -0.04200 0.02438 -1.722 0.08514 .
## index_AFexp.w2 0.05036 0.02669 1.887 0.05935 .
## Ind_1 -0.41049 0.13399 -3.064 0.00221 **
## Dem_1 -0.27687 0.11856 -2.335 0.01962 *
## vaxxAttitudes.c.w1 0.72045 0.01491 48.317 < 2e-16 ***
## index_AFexp.w1:Ind_1 0.04146 0.03684 1.125 0.26064
## index_AFexp.w1:Dem_1 0.03837 0.03041 1.262 0.20719
## index_AFexp.w2:Ind_1 -0.01122 0.03906 -0.287 0.77402
## index_AFexp.w2:Dem_1 -0.02436 0.03260 -0.747 0.45502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16d. simple effects for Indep
model2.ind <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0028554 0.1120111 -0.025 0.97966
## index_AFexp.w1 -0.0005444 0.0277329 -0.020 0.98434
## index_AFexp.w2 0.0391431 0.0285284 1.372 0.17018
## Dem_1 0.1336168 0.1442204 0.926 0.35430
## Rep_1 0.4104894 0.1339896 3.064 0.00221 **
## vaxxAttitudes.c.w1 0.7204464 0.0149108 48.317 < 2e-16 ***
## index_AFexp.w1:Dem_1 -0.0030858 0.0331647 -0.093 0.92588
## index_AFexp.w1:Rep_1 -0.0414558 0.0368440 -1.125 0.26064
## index_AFexp.w2:Dem_1 -0.0131432 0.0341080 -0.385 0.70002
## index_AFexp.w2:Rep_1 0.0112163 0.0390594 0.287 0.77402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-163. vaxxAttitudes.w2 ~ party * (ANALYTIC.w2 + ANALYTIC.w1 + AFFECT.w2 + AFFECT.w1) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.188275 0.056482 3.333 0.000873 ***
## index_ANexp.w1 -0.007988 0.005118 -1.561 0.118770
## index_AFexp.w1 0.150442 0.106175 1.417 0.156649
## index_ANexp.w2 0.011498 0.005679 2.025 0.043032 *
## index_AFexp.w2 -0.201747 0.119350 -1.690 0.091101 .
## DvR 0.198983 0.124391 1.600 0.109823
## IvDR 0.307677 0.129918 2.368 0.017961 *
## vaxxAttitudes.c.w1 0.719638 0.014939 48.172 < 2e-16 ***
## index_ANexp.w1:DvR -0.006047 0.010218 -0.592 0.554002
## index_ANexp.w1:IvDR 0.004676 0.012619 0.371 0.711015
## index_AFexp.w1:DvR 0.078136 0.212917 0.367 0.713673
## index_AFexp.w1:IvDR -0.115727 0.261061 -0.443 0.657597
## index_ANexp.w2:DvR -0.012351 0.011664 -1.059 0.289750
## index_ANexp.w2:IvDR 0.005426 0.013797 0.393 0.694178
## index_AFexp.w2:DvR 0.290564 0.245520 1.183 0.236756
## index_AFexp.w2:IvDR -0.117359 0.289591 -0.405 0.685329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
plot_model(m1, type = "pred", terms = c("index_ANexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model3.dem <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.190317 0.093459 2.036 0.04184 *
## index_ANexp.w1 -0.003421 0.005825 -0.587 0.55708
## index_AFexp.w1 0.073184 0.124092 0.590 0.55542
## index_ANexp.w2 0.019464 0.006693 2.908 0.00367 **
## index_AFexp.w2 -0.385757 0.143278 -2.692 0.00715 **
## Ind_1 -0.208186 0.148609 -1.401 0.16139
## Rep_1 0.198983 0.124391 1.600 0.10982
## vaxxAttitudes.c.w1 0.719638 0.014939 48.172 < 2e-16 ***
## index_ANexp.w1:Ind_1 -0.007699 0.012925 -0.596 0.55145
## index_ANexp.w1:Rep_1 -0.006047 0.010218 -0.592 0.55400
## index_AFexp.w1:Ind_1 0.154795 0.268775 0.576 0.56473
## index_AFexp.w1:Rep_1 0.078136 0.212917 0.367 0.71367
## index_ANexp.w2:Ind_1 -0.011601 0.014179 -0.818 0.41332
## index_ANexp.w2:Rep_1 -0.012351 0.011664 -1.059 0.28975
## index_AFexp.w2:Ind_1 0.262641 0.298782 0.879 0.37948
## index_AFexp.w2:Rep_1 0.290564 0.245520 1.183 0.23676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16c. simple effects for Rep
model3.rep <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3893005 0.0813476 4.786 1.82e-06 ***
## index_ANexp.w1 -0.0094684 0.0083951 -1.128 0.25951
## index_AFexp.w1 0.1513201 0.1730095 0.875 0.38187
## index_ANexp.w2 0.0071131 0.0095518 0.745 0.45654
## index_AFexp.w2 -0.0951931 0.1994256 -0.477 0.63317
## Ind_1 -0.4071687 0.1393172 -2.923 0.00351 **
## Dem_1 -0.1989831 0.1243914 -1.600 0.10982
## vaxxAttitudes.c.w1 0.7196384 0.0149388 48.172 < 2e-16 ***
## index_ANexp.w1:Ind_1 -0.0016520 0.0142687 -0.116 0.90784
## index_ANexp.w1:Dem_1 0.0060474 0.0102175 0.592 0.55400
## index_AFexp.w1:Ind_1 0.0766592 0.2945035 0.260 0.79466
## index_AFexp.w1:Dem_1 -0.0781357 0.2129174 -0.367 0.71367
## index_ANexp.w2:Ind_1 0.0007499 0.0157393 0.048 0.96200
## index_ANexp.w2:Dem_1 0.0123514 0.0116640 1.059 0.28975
## index_AFexp.w2:Ind_1 -0.0279231 0.3295373 -0.085 0.93248
## index_AFexp.w2:Dem_1 -0.2905640 0.2455195 -1.183 0.23676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16d. simple effects for Indep
model3.ind <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0178683 0.1150875 -0.155 0.87663
## index_ANexp.w1 -0.0111204 0.0115356 -0.964 0.33515
## index_AFexp.w1 0.2279793 0.2383603 0.956 0.33895
## index_ANexp.w2 0.0078630 0.0124998 0.629 0.52938
## index_AFexp.w2 -0.1231163 0.2621772 -0.470 0.63869
## Dem_1 0.2081856 0.1486093 1.401 0.16139
## Rep_1 0.4071687 0.1393172 2.923 0.00351 **
## vaxxAttitudes.c.w1 0.7196384 0.0149388 48.172 < 2e-16 ***
## index_ANexp.w1:Dem_1 0.0076995 0.0129253 0.596 0.55145
## index_ANexp.w1:Rep_1 0.0016520 0.0142687 0.116 0.90784
## index_AFexp.w1:Dem_1 -0.1547950 0.2687755 -0.576 0.56473
## index_AFexp.w1:Rep_1 -0.0766592 0.2945035 -0.260 0.79466
## index_ANexp.w2:Dem_1 0.0116015 0.0141789 0.818 0.41332
## index_ANexp.w2:Rep_1 -0.0007499 0.0157393 -0.048 0.96200
## index_AFexp.w2:Dem_1 -0.2626409 0.2987825 -0.879 0.37948
## index_AFexp.w2:Rep_1 0.0279231 0.3295373 0.085 0.93248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16V. long by W1 + W2 models
1. vaxxAttitudes ~ W1vW2 + ANALYTIC * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) *
(index_ANexp) +
(1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * (index_ANexp) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.121e-03 4.934e-02 4.721e+03 0.185 0.853332
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## DvR -8.157e-01 1.037e-01 5.201e+03 -7.865 4.47e-15 ***
## IvDR 3.912e-01 1.033e-01 5.413e+03 3.785 0.000155 ***
## index_ANexp 4.244e-03 4.267e-04 5.410e+03 9.947 < 2e-16 ***
## DvR:index_ANexp 3.772e-03 8.779e-04 5.281e+03 4.297 1.76e-05 ***
## IvDR:index_ANexp 9.206e-04 9.491e-04 5.060e+03 0.970 0.332096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AN DR:_AN
## W1vW2 0.006
## DvR -0.045 -0.017
## IvDR -0.237 -0.024 -0.059
## index_ANexp -0.667 0.091 -0.009 0.168
## DvR:ndx_ANx 0.011 0.031 -0.713 0.018 0.159
## IvDR:ndx_AN 0.159 -0.007 0.022 -0.688 -0.278 0.092- plot
m1 <- lmer(vaxxAttitudes ~ W1vW2 + index_ANexp * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Rep_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Rep_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.461e-01 7.698e-02 5.301e+03 7.093 1.48e-12 ***
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 2.662e-03 5.600e-04 5.303e+03 4.754 2.05e-06 ***
## Rep_1 -8.157e-01 1.037e-01 5.201e+03 -7.865 4.47e-15 ***
## Ind_1 -7.991e-01 1.183e-01 5.439e+03 -6.753 1.60e-11 ***
## index_ANexp:Rep_1 3.772e-03 8.779e-04 5.281e+03 4.297 1.76e-05 ***
## index_ANexp:Ind_1 9.654e-04 1.008e-03 5.087e+03 0.957 0.338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Rep_1 Ind_1 i_AN:R
## W1vW2 0.004
## index_ANexp -0.774 0.041
## Rep_1 -0.729 -0.017 0.564
## Ind_1 -0.608 0.014 0.484 0.490
## indx_AN:R_1 0.495 0.031 -0.611 -0.713 -0.328
## indx_AN:I_1 0.420 0.020 -0.525 -0.331 -0.717 0.349c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Dem_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Dem_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.696e-01 7.106e-02 4.697e+03 -3.795 0.00015 ***
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 6.434e-03 6.954e-04 5.389e+03 9.253 < 2e-16 ***
## Dem_1 8.157e-01 1.037e-01 5.201e+03 7.865 4.47e-15 ***
## Ind_1 1.664e-02 1.129e-01 5.441e+03 0.147 0.88278
## index_ANexp:Dem_1 -3.772e-03 8.779e-04 5.281e+03 -4.297 1.76e-05 ***
## index_ANexp:Ind_1 -2.806e-03 1.082e-03 5.120e+03 -2.595 0.00949 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Dem_1 Ind_1 i_AN:D
## W1vW2 -0.020
## index_ANexp -0.649 0.073
## Dem_1 -0.670 0.017 0.446
## Ind_1 -0.557 0.030 0.384 0.406
## indx_AN:D_1 0.504 -0.031 -0.770 -0.713 -0.311
## indx_AN:I_1 0.383 -0.007 -0.609 -0.270 -0.670 0.486d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Dem_1 + Rep_1) +
(1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Dem_1 + Rep_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.530e-01 9.408e-02 5.422e+03 -2.689 0.00718 **
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 3.628e-03 8.585e-04 5.148e+03 4.225 2.43e-05 ***
## Dem_1 7.991e-01 1.183e-01 5.439e+03 6.753 1.60e-11 ***
## Rep_1 -1.664e-02 1.129e-01 5.441e+03 -0.147 0.88278
## index_ANexp:Dem_1 -9.654e-04 1.008e-03 5.087e+03 -0.957 0.33846
## index_ANexp:Rep_1 2.806e-03 1.082e-03 5.120e+03 2.595 0.00949 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Dem_1 Rep_1 i_AN:D
## W1vW2 0.021
## index_ANexp -0.672 0.051
## Dem_1 -0.760 -0.014 0.527
## Rep_1 -0.779 -0.030 0.533 0.598
## indx_AN:D_1 0.558 -0.020 -0.832 -0.717 -0.448
## indx_AN:R_1 0.514 0.007 -0.766 -0.402 -0.670 0.6492. vaxxAttitudes ~ W1vW2 + AFFECT * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * index_AFexp + (1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * index_AFexp + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.963e-03 5.051e-02 4.766e+03 -0.158 0.874740
## W1vW2 -2.687e-01 3.284e-02 2.467e+03 -8.184 4.35e-16 ***
## DvR -8.463e-01 1.070e-01 5.227e+03 -7.906 3.21e-15 ***
## IvDR 3.919e-01 1.054e-01 5.408e+03 3.717 0.000204 ***
## index_AFexp 8.873e-02 8.886e-03 5.416e+03 9.986 < 2e-16 ***
## DvR:index_AFexp 8.215e-02 1.840e-02 5.296e+03 4.464 8.21e-06 ***
## IvDR:index_AFexp 1.814e-02 1.965e-02 5.077e+03 0.923 0.355922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AF DR:_AF
## W1vW2 -0.009
## DvR -0.052 -0.015
## IvDR -0.225 -0.028 -0.064
## index_AFexp -0.686 0.108 -0.004 0.159
## DvR:ndx_AFx 0.014 0.032 -0.733 0.022 0.149
## IvDR:ndx_AF 0.151 -0.002 0.025 -0.702 -0.264 0.085- plot
m1 <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * party_factor + (1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Rep_1 + Ind_1) + (1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Rep_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.54450 0.07975 5312.96352 6.828 9.59e-12 ***
## W1vW2 -0.26875 0.03284 2467.22199 -8.184 4.35e-16 ***
## index_AFexp 0.05364 0.01186 5339.40804 4.524 6.21e-06 ***
## Rep_1 -0.84629 0.10704 5227.49812 -7.906 3.21e-15 ***
## Ind_1 -0.81503 0.12128 5438.38387 -6.720 2.00e-11 ***
## index_AFexp:Rep_1 0.08215 0.01840 5295.62226 4.464 8.21e-06 ***
## index_AFexp:Ind_1 0.02294 0.02097 5116.70536 1.094 0.274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Rep_1 Ind_1 i_AF:R
## W1vW2 -0.008
## index_AFexp -0.791 0.055
## Rep_1 -0.732 -0.015 0.579
## Ind_1 -0.616 0.017 0.500 0.497
## indx_AF:R_1 0.510 0.032 -0.618 -0.733 -0.343
## indx_AF:I_1 0.437 0.016 -0.536 -0.345 -0.732 0.359c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Ind_1) + (1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.30178 0.07289 4773.41683 -4.140 3.53e-05 ***
## W1vW2 -0.26875 0.03284 2467.22199 -8.184 4.35e-16 ***
## index_AFexp 0.13579 0.01448 5386.99965 9.376 < 2e-16 ***
## Dem_1 0.84629 0.10704 5227.49810 7.906 3.21e-15 ***
## Ind_1 0.03126 0.11513 5438.90082 0.272 0.78601
## index_AFexp:Dem_1 -0.08215 0.01840 5295.62228 -4.464 8.21e-06 ***
## index_AFexp:Ind_1 -0.05922 0.02240 5126.66594 -2.644 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Dem_1 Ind_1 i_AF:D
## W1vW2 -0.031
## index_AFexp -0.671 0.086
## Dem_1 -0.667 0.015 0.457
## Ind_1 -0.561 0.032 0.398 0.406
## indx_AF:D_1 0.518 -0.032 -0.765 -0.733 -0.321
## indx_AF:I_1 0.399 -0.011 -0.612 -0.280 -0.686 0.486d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Rep_1) + (1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Rep_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.27053 0.09564 5425.88907 -2.829 0.00469 **
## W1vW2 -0.26875 0.03284 2467.22172 -8.184 4.35e-16 ***
## index_AFexp 0.07658 0.01772 5162.86573 4.321 1.59e-05 ***
## Dem_1 0.81503 0.12128 5438.38387 6.720 2.00e-11 ***
## Rep_1 -0.03126 0.11513 5438.90081 -0.272 0.78601
## index_AFexp:Dem_1 -0.02294 0.02097 5116.70538 -1.094 0.27421
## index_AFexp:Rep_1 0.05922 0.02240 5126.66595 2.644 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Dem_1 Rep_1 i_AF:D
## W1vW2 0.015
## index_AFexp -0.685 0.056
## Dem_1 -0.754 -0.017 0.532
## Rep_1 -0.776 -0.032 0.541 0.591
## indx_AF:D_1 0.565 -0.016 -0.825 -0.732 -0.451
## indx_AF:R_1 0.521 0.011 -0.763 -0.404 -0.686 0.6423. vaxxAttitudes ~ W1vW2 + (AFFECT + ANALYTIC) * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) *
(index_AFexp + index_ANexp) +
(1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * (index_AFexp + index_ANexp) +
## (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.021e-03 5.192e-02 4.813e+03 0.020 0.9843
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## DvR -8.977e-01 1.109e-01 5.272e+03 -8.093 7.19e-16 ***
## IvDR 4.195e-01 1.076e-01 5.395e+03 3.897 9.86e-05 ***
## index_AFexp 7.303e-02 7.536e-02 5.313e+03 0.969 0.3326
## index_ANexp 7.066e-04 3.625e-03 5.303e+03 0.195 0.8455
## DvR:index_AFexp 2.940e-01 1.429e-01 5.111e+03 2.057 0.0397 *
## DvR:index_ANexp -1.013e-02 6.822e-03 5.097e+03 -1.485 0.1377
## IvDR:index_AFexp -1.348e-01 1.753e-01 4.906e+03 -0.769 0.4420
## IvDR:index_ANexp 7.373e-03 8.479e-03 4.894e+03 0.870 0.3846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AF ind_AN DR:_AF DR:_AN IDR:_AF
## W1vW2 -0.041
## DvR -0.054 -0.013
## IvDR -0.209 -0.036 -0.068
## index_AFexp -0.298 0.134 -0.018 0.058
## index_ANexp 0.221 -0.121 0.019 -0.042 -0.993
## DvR:ndx_AFx -0.021 0.025 -0.347 0.002 0.194 -0.197
## DvR:ndx_ANx 0.024 -0.022 0.258 0.001 -0.197 0.202 -0.992
## IvDR:ndx_AF 0.047 0.014 0.001 -0.270 -0.411 0.417 0.092 -0.094
## IvDR:ndx_AN -0.032 -0.014 0.002 0.194 0.414 -0.423 -0.093 0.097 -0.994- plots
m1 <- lmer(vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lmer(vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Rep_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Rep_1 +
## Ind_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.883e-01 8.270e-02 5.310e+03 7.114 1.28e-12 ***
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp -1.185e-01 8.772e-02 5.135e+03 -1.350 0.1769
## index_ANexp 8.204e-03 4.144e-03 5.082e+03 1.980 0.0478 *
## Rep_1 -8.977e-01 1.109e-01 5.272e+03 -8.093 7.19e-16 ***
## Ind_1 -8.684e-01 1.244e-01 5.435e+03 -6.981 3.28e-12 ***
## index_AFexp:Rep_1 2.940e-01 1.429e-01 5.111e+03 2.057 0.0397 *
## index_AFexp:Ind_1 2.818e-01 1.832e-01 4.960e+03 1.538 0.1240
## index_ANexp:Rep_1 -1.013e-02 6.822e-03 5.097e+03 -1.485 0.1377
## index_ANexp:Ind_1 -1.244e-02 8.827e-03 4.936e+03 -1.409 0.1589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Rep_1 Ind_1 i_AF:R i_AF:I i_AN:R
## W1vW2 -0.033
## index_AFexp -0.367 0.104
## index_ANexp 0.266 -0.097 -0.991
## Rep_1 -0.734 -0.013 0.268 -0.194
## Ind_1 -0.624 0.026 0.232 -0.168 0.504
## indx_AF:R_1 0.221 0.025 -0.587 0.581 -0.347 -0.157
## indx_AF:I_1 0.170 -0.004 -0.451 0.446 -0.137 -0.286 0.302
## indx_AN:R_1 -0.158 -0.022 0.576 -0.581 0.258 0.114 -0.992 -0.296
## indx_AN:I_1 -0.120 0.005 0.438 -0.441 0.098 0.205 -0.294 -0.993 0.293c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Dem_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Dem_1 +
## Ind_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.094e-01 7.541e-02 4.942e+03 -4.103 4.14e-05 ***
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp 1.755e-01 1.157e-01 5.248e+03 1.517 0.1294
## index_ANexp -1.925e-03 5.557e-03 5.255e+03 -0.346 0.7291
## Dem_1 8.977e-01 1.109e-01 5.272e+03 8.093 7.19e-16 ***
## Ind_1 2.936e-02 1.177e-01 5.427e+03 0.249 0.8031
## index_AFexp:Dem_1 -2.940e-01 1.429e-01 5.111e+03 -2.057 0.0397 *
## index_AFexp:Ind_1 -1.219e-02 1.953e-01 4.918e+03 -0.062 0.9502
## index_ANexp:Dem_1 1.013e-02 6.822e-03 5.097e+03 1.485 0.1377
## index_ANexp:Ind_1 -2.309e-03 9.441e-03 4.914e+03 -0.245 0.8068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Dem_1 Ind_1 i_AF:D i_AF:I i_AN:D
## W1vW2 -0.055
## index_AFexp -0.337 0.109
## index_ANexp 0.258 -0.099 -0.992
## Dem_1 -0.667 0.013 0.226 -0.172
## Ind_1 -0.568 0.039 0.194 -0.146 0.409
## indx_AF:D_1 0.268 -0.025 -0.790 0.784 -0.347 -0.161
## indx_AF:I_1 0.183 -0.022 -0.548 0.544 -0.126 -0.281 0.448
## indx_AN:D_1 -0.207 0.022 0.788 -0.795 0.258 0.123 -0.992 -0.448
## indx_AN:I_1 -0.138 0.020 0.540 -0.545 0.095 0.205 -0.442 -0.993 0.448d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Dem_1 + Rep_1) +
(1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Dem_1 +
## Rep_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.800e-01 9.728e-02 5.426e+03 -2.879 0.00401 **
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp 1.634e-01 1.636e-01 5.036e+03 0.998 0.31811
## index_ANexp -4.233e-03 7.926e-03 5.023e+03 -0.534 0.59331
## Dem_1 8.684e-01 1.244e-01 5.435e+03 6.981 3.28e-12 ***
## Rep_1 -2.936e-02 1.177e-01 5.427e+03 -0.249 0.80306
## index_AFexp:Dem_1 -2.818e-01 1.832e-01 4.960e+03 -1.538 0.12400
## index_AFexp:Rep_1 1.219e-02 1.953e-01 4.918e+03 0.062 0.95023
## index_ANexp:Dem_1 1.244e-02 8.827e-03 4.936e+03 1.409 0.15890
## index_ANexp:Rep_1 2.309e-03 9.441e-03 4.914e+03 0.245 0.80682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Dem_1 Rep_1 i_AF:D i_AF:R i_AN:D
## W1vW2 0.005
## index_AFexp -0.255 0.052
## index_ANexp 0.183 -0.046 -0.994
## Dem_1 -0.748 -0.026 0.195 -0.140
## Rep_1 -0.770 -0.039 0.198 -0.141 0.581
## indx_AF:D_1 0.221 0.004 -0.878 0.873 -0.286 -0.173
## indx_AF:R_1 0.198 0.022 -0.806 0.802 -0.153 -0.281 0.717
## indx_AN:D_1 -0.159 -0.005 0.878 -0.883 0.205 0.124 -0.993 -0.716
## indx_AN:R_1 -0.140 -0.020 0.804 -0.809 0.109 0.205 -0.715 -0.993 0.723