covid_media_analyses_uk_usa
Dani Grant
3/9/2022
- NOTES:
- set up
- descriptives
- sum media responses
- UK correlations
- USA correlations
- I United States
- II. United Kingdom
- III. long by UK + USA models
NOTES:
UK media in survey: Daily Mail (1) Guardian (2) Sun (3) Daily Mirror (4) The Scotsman & Scotland on Sunday (5) UK Times (6) Telegraph & Sunday Telegraph (7) BBC Radio (8) BBC Broadcast (9) The Independent (10)
UK media LIWC measures: DailyMail (1) Guardian (2)
Sun (3) Mirror (4) —— Scotsman (not in survey??) UKTimes (6) Telegraph (7) BBC (8, 9???) Independent (10)
—— National (not in survey??)
set up
libraries and data sets
library(psych)
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library(parallel)
library(minqa)
library(dfoptim)
library(ggcorrplot)
##############
# United States
##############
#import wave 1 survey data
d1.usa <- 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 LIWC csv USA
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)
#import LIWC THREAT csv USA
liwcT <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_22_USA.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
##############
# United Kingdom
##############
#import UK survey data
d1.uk <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_UK_Cleaned1.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import LIWC csv UK
liwcUK <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_22_UK.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import LIWC THREAT csv UK
liwcUKT <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_22_UK_threat.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)LIWC wave 1 USA
###################################
# Create LIWC rating averages for wave 1
###################################
#move over measures of interest
w1 <- data.frame(liwc[liwc$Wave == 1,])
w1 <- w1[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
w1T <- liwcT[,c("mediaOutlet", "threat")]
# create wide data set for wave 1
w1w = w1 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
# create wide data set for wave 1 -- threat liwc 22
w1wT = w1T %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("threat",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w1w[paste0("affect_",1:21)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w1w[paste0("cogproc_",1:21)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w1w[paste0("posemo_",1:21)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w1w[paste0("negemo_",1:21)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w1w[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#threat
threat <- data.frame(w1wT[paste0("threat_",1:21)])
TR <- apply(threat, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w1 <- data.frame(w1w$mediaOutlet)
colnames(w1)[colnames(w1)=="w1w.mediaOutlet"] <- "mediaOutlet"
w1$affect <- AF
w1$cogproc <- CP
w1$analytic <- AN
w1$posemo <- PE
w1$negemo <- NE
w1$threat <- TRLIWC wave 1 UK
###################################
# Create LIWC rating averages for UK
###################################
w1UK <- liwcUK[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
w1UKT <- liwcUKT[,c("mediaOutlet", "threat")]
# create wide data set for wave 2
w1wUK = w1UK %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo", #add threat
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
# create wide data set for wave 2 UK threat
w1wUKT = w1UKT %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("threat",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w1wUK[paste0("affect_",1:21)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w1wUK[paste0("cogproc_",1:21)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w1wUK[paste0("posemo_",1:21)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w1wUK[paste0("negemo_",1:21)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w1wUK[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#threat
threat <- data.frame(w1wUKT[paste0("threat_",1:21)])
TR <- apply(threat, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w1UK <- data.frame(w1wUK$mediaOutlet)
colnames(w1UK)[colnames(w1UK)=="w1wUK.mediaOutlet"] <- "mediaOutlet"
w1UK$affect <- AF
w1UK$cogproc <- CP
w1UK$analytic <- AN
w1UK$posemo <- PE
w1UK$negemo <- NE
w1UK$threat <- TRprep wave 1 USA data
#delete any measures we don't want---exclude media exposure #3, #8, and #9
## missing Yahoo, Huff Post, Wash Post
d1 <- d1.usa[,c("s3", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"risk3", "risk4", "risk5", "risk6",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_4",
"mediaExposure_5", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11", "mediaExposure_12",
"mediaExposure_13", "mediaExposure_14", "mediaExposure_15")]
colnames(d1)[colnames(d1) == "s3"] <- "participant"
#rename exposure
colnames(d1)[colnames(d1)=="mediaExposure_1"] <- "NYT_exp"
colnames(d1)[colnames(d1)=="mediaExposure_2"] <- "WSJ_exp"
colnames(d1)[colnames(d1)=="mediaExposure_4"] <- "USAT_exp"
colnames(d1)[colnames(d1)=="mediaExposure_5"] <- "Fox_exp"
colnames(d1)[colnames(d1)=="mediaExposure_6"] <- "CNN_exp"
colnames(d1)[colnames(d1)=="mediaExposure_7"] <- "MSNBC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_10"] <-"AOL_exp"
colnames(d1)[colnames(d1)=="mediaExposure_11"] <-"NPR_exp"
colnames(d1)[colnames(d1)=="mediaExposure_12"] <-"ABC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_13"] <-"NBC_exp"
colnames(d1)[colnames(d1)=="mediaExposure_14"] <-"CBS_exp"
colnames(d1)[colnames(d1)=="mediaExposure_15"] <-"PBS_exp"
#change to 0-4 rating
d1$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"]
## threat
d1$ABC_TR <- w1$threat[w1$mediaOutlet == "ABC"]
d1$CBS_TR <- w1$threat[w1$mediaOutlet == "CBS"]
d1$CNN_TR <- w1$threat[w1$mediaOutlet == "CNN"]
d1$Fox_TR <- w1$threat[w1$mediaOutlet == "Fox"]
d1$MSNBC_TR <- w1$threat[w1$mediaOutlet == "MSNBC"]
d1$NBC_TR <- w1$threat[w1$mediaOutlet == "NBC"]
d1$NPR_TR <- w1$threat[w1$mediaOutlet == "NPR"]
d1$NYT_TR <- w1$threat[w1$mediaOutlet == "NYT"]
d1$PBS_TR <- w1$threat[w1$mediaOutlet == "PBS"]
d1$USAT_TR <- w1$threat[w1$mediaOutlet == "USAToday"]
d1$WSJ_TR <- w1$threat[w1$mediaOutlet == "WSJ"]
d1$AOL_TR <- w1$threat[w1$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$index_AFexp <- rowMeans(x, na.rm = T)
#individual media anylitic thinking
d1$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)
#individual media threat
d1$ABC_TRexp <- d1$ABC_TR * d1$ABC_exp
d1$CBS_TRexp <- d1$CBS_TR * d1$CBS_exp
d1$CNN_TRexp <- d1$CNN_TR * d1$CNN_exp
d1$Fox_TRexp <- d1$Fox_TR * d1$Fox_exp
d1$MSNBC_TRexp <- d1$MSNBC_TR * d1$MSNBC_exp
d1$NBC_TRexp <- d1$NBC_TR * d1$NBC_exp
d1$NPR_TRexp <- d1$NPR_TR * d1$NPR_exp
d1$NYT_TRexp <- d1$NYT_TR * d1$NYT_exp
d1$PBS_TRexp <- d1$PBS_TR * d1$PBS_exp
d1$USAT_TRexp <- d1$USAT_TR * d1$USAT_exp
d1$WSJ_TRexp <- d1$WSJ_TR * d1$WSJ_exp
d1$AOL_TRexp <- d1$AOL_TR * d1$AOL_exp
x <- cbind(d1$ABC_TRexp,
d1$CBS_TRexp,
d1$CNN_TRexp,
d1$Fox_TRexp,
d1$MSNBC_TRexp,
d1$NBC_TRexp,
d1$NPR_TRexp,
d1$NYT_TRexp,
d1$PBS_TRexp,
d1$USAT_TRexp,
d1$WSJ_TRexp,
d1$AOL_TRexp)
d1$index_TRexp <- rowMeans(x, na.rm = T)
#####################################################
# codes for party
####################################################
d1$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$partyClose <- NULL
colnames(d1)[colnames(d1) == "risk3"] <- "healthRisk"
colnames(d1)[colnames(d1) == "risk4"] <- "econRisk"
colnames(d1)[colnames(d1) == "risk5"] <- "pEconRisk"
colnames(d1)[colnames(d1) == "risk6"] <- "worstAB"prep wave 1 UK data
d1UK <- d1.uk[,c("X", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"risk3", "risk4", "risk5", "risk6",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_3",
"mediaExposure_4", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_9", "mediaExposure_10")]
d1UK$ownvote_conf <- NA
d1UK$overallvote_conf <- NA
d1UK$election_timing <- NA
colnames(d1UK)[colnames(d1UK) == "X"] <- "participant"
#rename exposure
colnames(d1UK)[colnames(d1UK)=="mediaExposure_1"] <- "DAM_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_2"] <- "GAR_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_3"] <- "SUN_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_4"] <- "MIR_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_6"] <- "UKT_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_7"] <- "TEL_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_9"] <- "BBC_exp"
colnames(d1UK)[colnames(d1UK)=="mediaExposure_10"] <-"IND_exp"
#change to 0-4 rating
d1UK$DAM_exp <- d1UK$DAM_exp - 1
d1UK$GAR_exp <- d1UK$GAR_exp - 1
d1UK$SUN_exp <- d1UK$SUN_exp - 1
d1UK$MIR_exp <- d1UK$MIR_exp - 1
d1UK$UKT_exp <- d1UK$UKT_exp - 1
d1UK$TEL_exp <- d1UK$TEL_exp - 1
d1UK$BBC_exp <- d1UK$BBC_exp - 1
d1UK$IND_exp <- d1UK$IND_exp - 1
x <- cbind(d1UK$DAM_exp,
d1UK$GAR_exp,
d1UK$SUN_exp,
d1UK$MIR_exp,
d1UK$UKT_exp,
d1UK$TEL_exp,
d1UK$BBC_exp,
d1UK$IND_exp)
d1UK$sum.media.exp <- rowSums(x, na.rm = T)
## affect
d1UK$BBC_AF <- w1UK$affect[w1UK$mediaOutlet == "BBC"]
d1UK$DAM_AF <- w1UK$affect[w1UK$mediaOutlet == "DailyMail"]
d1UK$GAR_AF <- w1UK$affect[w1UK$mediaOutlet == "GuardianObserve"]
d1UK$IND_AF <- w1UK$affect[w1UK$mediaOutlet == "Independent"]
d1UK$MIR_AF <- w1UK$affect[w1UK$mediaOutlet == "Mirror"]
d1UK$SUN_AF <- w1UK$affect[w1UK$mediaOutlet == "Sun"]
d1UK$TEL_AF <- w1UK$affect[w1UK$mediaOutlet == "Telegraph"]
d1UK$UKT_AF <- w1UK$affect[w1UK$mediaOutlet == "UKTimes"]
## analytic
d1UK$BBC_AN <- w1UK$analytic[w1UK$mediaOutlet == "BBC"]
d1UK$DAM_AN <- w1UK$analytic[w1UK$mediaOutlet == "DailyMail"]
d1UK$GAR_AN <- w1UK$analytic[w1UK$mediaOutlet == "GuardianObserve"]
d1UK$IND_AN <- w1UK$analytic[w1UK$mediaOutlet == "Independent"]
d1UK$MIR_AN <- w1UK$analytic[w1UK$mediaOutlet == "Mirror"]
d1UK$SUN_AN <- w1UK$analytic[w1UK$mediaOutlet == "Sun"]
d1UK$TEL_AN <- w1UK$analytic[w1UK$mediaOutlet == "Telegraph"]
d1UK$UKT_AN <- w1UK$analytic[w1UK$mediaOutlet == "UKTimes"]
## threat
d1UK$BBC_TR <- w1UK$threat[w1UK$mediaOutlet == "BBC"]
d1UK$DAM_TR <- w1UK$threat[w1UK$mediaOutlet == "DailyMail"]
d1UK$GAR_TR <- w1UK$threat[w1UK$mediaOutlet == "GuardianObserve"]
d1UK$IND_TR <- w1UK$threat[w1UK$mediaOutlet == "Independent"]
d1UK$MIR_TR <- w1UK$threat[w1UK$mediaOutlet == "Mirror"]
d1UK$SUN_TR <- w1UK$threat[w1UK$mediaOutlet == "Sun"]
d1UK$TEL_TR <- w1UK$threat[w1UK$mediaOutlet == "Telegraph"]
d1UK$UKT_TR <- w1UK$threat[w1UK$mediaOutlet == "UKTimes"]
#individual media affect
d1UK$BBC_AFexp <- d1UK$BBC_AF * d1UK$BBC_exp
d1UK$DAM_AFexp <- d1UK$DAM_AF * d1UK$DAM_exp
d1UK$GAR_AFexp <- d1UK$GAR_AF * d1UK$GAR_exp
d1UK$IND_AFexp <- d1UK$IND_AF * d1UK$IND_exp
d1UK$MIR_AFexp <- d1UK$MIR_AF * d1UK$MIR_exp
d1UK$SUN_AFexp <- d1UK$SUN_AF * d1UK$SUN_exp
d1UK$TEL_AFexp <- d1UK$TEL_AF * d1UK$TEL_exp
d1UK$UKT_AFexp <- d1UK$UKT_AF * d1UK$UKT_exp
x <- cbind(d1UK$BBC_AFexp,
d1UK$DAM_AFexp,
d1UK$GAR_AFexp,
d1UK$IND_AFexp,
d1UK$MIR_AFexp,
d1UK$SUN_AFexp,
d1UK$TEL_AFexp,
d1UK$UKT_AFexp)
d1UK$index_AFexp <- rowMeans(x, na.rm = T)
#individual media analytic thinking
d1UK$BBC_ANexp <- d1UK$BBC_AN * d1UK$BBC_exp
d1UK$DAM_ANexp <- d1UK$DAM_AN * d1UK$DAM_exp
d1UK$GAR_ANexp <- d1UK$GAR_AN * d1UK$GAR_exp
d1UK$IND_ANexp <- d1UK$IND_AN * d1UK$IND_exp
d1UK$MIR_ANexp <- d1UK$MIR_AN * d1UK$MIR_exp
d1UK$SUN_ANexp <- d1UK$SUN_AN * d1UK$SUN_exp
d1UK$TEL_ANexp <- d1UK$TEL_AN * d1UK$TEL_exp
d1UK$UKT_ANexp <- d1UK$UKT_AN * d1UK$UKT_exp
x <- cbind(d1UK$BBC_ANexp,
d1UK$DAM_ANexp,
d1UK$GAR_ANexp,
d1UK$IND_ANexp,
d1UK$MIR_ANexp,
d1UK$SUN_ANexp,
d1UK$TEL_ANexp,
d1UK$UKT_ANexp)
d1UK$index_ANexp <- rowMeans(x, na.rm = T)
#individual media threat
d1UK$BBC_TRexp <- d1UK$BBC_TR * d1UK$BBC_exp
d1UK$DAM_TRexp <- d1UK$DAM_TR * d1UK$DAM_exp
d1UK$GAR_TRexp <- d1UK$GAR_TR * d1UK$GAR_exp
d1UK$IND_TRexp <- d1UK$IND_TR * d1UK$IND_exp
d1UK$MIR_TRexp <- d1UK$MIR_TR * d1UK$MIR_exp
d1UK$SUN_TRexp <- d1UK$SUN_TR * d1UK$SUN_exp
d1UK$TEL_TRexp <- d1UK$TEL_TR * d1UK$TEL_exp
d1UK$UKT_TRexp <- d1UK$UKT_TR * d1UK$UKT_exp
x <- cbind(d1UK$BBC_TRexp,
d1UK$DAM_TRexp,
d1UK$GAR_TRexp,
d1UK$IND_TRexp,
d1UK$MIR_TRexp,
d1UK$SUN_TRexp,
d1UK$TEL_TRexp,
d1UK$UKT_TRexp)
d1UK$index_TRexp <- rowMeans(x, na.rm = T)
#####################################################
# codes for party
####################################################
d1UK$partyCont <- NA
d1UK$partyCont[d1UK$demStrength == 1] <- -3
d1UK$partyCont[d1UK$demStrength == 2] <- -2
d1UK$partyCont[d1UK$partyClose == 1] <- -1
d1UK$partyCont[d1UK$partyClose == 3] <- 0
d1UK$partyCont[d1UK$partyClose == 2] <- 1
d1UK$partyCont[d1UK$repStrength == 2] <- 2
d1UK$partyCont[d1UK$repStrength == 1] <- 3
## party factor
d1UK$party_factor <- NA
d1UK$party_factor[d1UK$partyCont < 0] <- 'Democrat'
d1UK$party_factor[d1UK$partyCont == 0] <- 'Independent'
d1UK$party_factor[d1UK$partyCont > 0] <- 'Republican'
## Order of party variable
d1UK$party_factor <- factor(d1UK$party_factor,
levels = c('Democrat', 'Republican', 'Independent'))
## contrast codes
d1UK$DvR <- NA
d1UK$DvR[d1UK$party_factor == 'Democrat'] <- -.5
d1UK$DvR[d1UK$party_factor == 'Independent'] <- 0
d1UK$DvR[d1UK$party_factor == 'Republican'] <- .5
d1UK$IvDR <- NA
d1UK$IvDR[d1UK$party_factor == 'Democrat'] <- .33
d1UK$IvDR[d1UK$party_factor == 'Independent'] <- -.67
d1UK$IvDR[d1UK$party_factor == 'Republican'] <- .33
## dummy codes
d1UK$Rep_1[d1UK$party_factor == 'Democrat'] <- 0
d1UK$Rep_1[d1UK$party_factor == 'Republican'] <- 1
d1UK$Rep_1[d1UK$party_factor == 'Independent'] <- 0
d1UK$Ind_1[d1UK$party_factor == 'Democrat'] <- 0
d1UK$Ind_1[d1UK$party_factor == 'Republican'] <- 0
d1UK$Ind_1[d1UK$party_factor == 'Independent'] <- 1
d1UK$Dem_1[d1UK$party_factor == 'Democrat'] <- 1
d1UK$Dem_1[d1UK$party_factor == 'Republican'] <- 0
d1UK$Dem_1[d1UK$party_factor == 'Independent'] <- 0
## delete unnecessary columns
d1UK$party <- NULL
d1UK$demStrength <- NULL
d1UK$repStrength <- NULL
d1UK$partyClose <- NULL
colnames(d1UK)[colnames(d1UK) == "risk3"] <- "healthRisk"
colnames(d1UK)[colnames(d1UK) == "risk4"] <- "econRisk"
colnames(d1UK)[colnames(d1UK) == "risk5"] <- "pEconRisk"
colnames(d1UK)[colnames(d1UK) == "risk6"] <- "worstAB"LONG merged data w1 USA + UK
d1.UK <- d1UK[,c("participant", "vaxxAttitudes",
"party_factor", "DvR", "IvDR", "Rep_1", "Dem_1", "Ind_1",
"healthRisk", "econRisk", "pEconRisk", "worstAB",
"index_AFexp", "index_ANexp", "index_TRexp", "sum.media.exp")]
d1.UK$country <- "UK"
d1.US <- d1[,c("participant", "vaxxAttitudes",
"party_factor", "DvR", "IvDR", "Rep_1", "Dem_1", "Ind_1",
"healthRisk", "econRisk", "pEconRisk", "worstAB",
"index_AFexp", "index_ANexp", "index_TRexp", "sum.media.exp")]
d1.US$country <- "US"
dm <- rbind.data.frame(d1.UK, d1.US)
dm$participant <- as.factor(dm$participant)
dm$USvUK <- NA
dm$USvUK[dm$country == "US"] <- -.5
dm$USvUK[dm$country == "UK"] <- .5
dm$US_0 <- NA
dm$US_0[dm$country == "US"] <- 0
dm$US_0[dm$country == "UK"] <- 1
dm$UK_0 <- NA
dm$UK_0[dm$country == "US"] <- 1
dm$UK_0[dm$country == "UK"] <- 0wave 1 usa
## mediaOutlet affect cogproc analytic posemo negemo threat
## 1 ABC 3.920000 7.787500 79.10750 2.635000 1.232500 0.9775000
## 2 AOL 3.504762 6.856667 95.42000 2.103810 1.355238 1.0442857
## 3 CBS 3.890000 8.275000 78.76000 2.500000 1.330000 0.9250000
## 4 CNN 3.800000 9.780000 73.61000 2.280000 1.460000 0.7733333
## 5 Fox 4.390000 10.480000 60.43000 2.690000 1.650000 0.7433333
## 6 MSNBC 3.880000 9.930000 70.52000 2.360000 1.450000 0.7850000
## 7 NBC 6.590000 6.870000 80.54500 2.730000 3.810000 0.6700000
## 8 NPR 3.425000 9.825000 71.83000 2.125000 1.240000 0.8075000
## 9 NYT 3.537222 7.912222 93.49444 1.941111 1.535556 1.0095238
## 10 PBS 3.830000 8.970000 78.86000 2.320000 1.460000 0.9150000
## 11 USAToday 3.653333 8.883333 91.28000 2.206667 1.390000 0.9600000
## 12 WSJ 1.846000 4.416000 96.59800 1.014000 0.800000 0.8880000wave 1 uk
## mediaOutlet affect cogproc analytic posemo negemo threat
## 1 BBC 3.738095 9.417619 79.84048 2.293810 1.400952 1.0247619
## 2 DailyMail 3.991667 8.436667 91.27500 2.181667 1.746667 1.1000000
## 3 GuardianObserve 3.551250 8.099375 93.39312 1.972500 1.526250 1.2350000
## 4 Independent 3.843750 9.095625 92.31937 2.099375 1.691875 1.2731250
## 5 Mirror 4.070909 7.661818 88.95909 2.531818 1.487273 0.9118182
## 6 National 4.062500 9.392500 91.00000 2.537500 1.470000 1.1100000
## 7 Sun 4.140000 8.094286 87.43429 2.542143 1.539286 0.8550000
## 8 Telegraph 3.770769 8.242308 92.57846 2.189231 1.533077 1.0369231
## 9 UKTimes 3.695238 7.991429 93.42571 2.088571 1.557143 1.1004762descriptives
nrow(d1.UK) #1520## [1] 1520nrow(d1.US) #3311## [1] 3311print("United States")## [1] "United States"print("Affect")## [1] "Affect"tapply(d1.US$index_AFexp, d1.US$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 5.469034 3.406757 3.935891print("Analytic")## [1] "Analytic"tapply(d1.US$index_ANexp, d1.US$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 108.65344 65.97917 78.10298print("Threat")## [1] "Threat"tapply(d1.US$index_TRexp, d1.US$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 1.1735885 0.7212608 0.8479896print("United Kingdom")## [1] "United Kingdom"print("Affect")## [1] "Affect"tapply(d1.UK$index_AFexp, d1.UK$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 3.370642 3.338829 2.738850print("Analytic")## [1] "Analytic"tapply(d1.UK$index_ANexp, d1.UK$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 78.63168 76.41622 63.13080print("Threat")## [1] "Threat"tapply(d1.UK$index_TRexp, d1.UK$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 0.9766135 0.9325797 0.7696900sum media responses
sum(table(d1.US$vaxxAttitudes)) # 3051 responses for vaxxAttitudes in USA## [1] 3051sum(table(d1.UK$vaxxAttitudes)) # 1502 responses for vaxxAttitudes in UK## [1] 1502print("United States")## [1] "United States"sum(table(d1$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$AOL_exp)) #3053## [1] 3053sum(table(d1$party_factor)) #3147## [1] 3147print("United Kingdom")## [1] "United Kingdom"sum(table(d1UK$DAM_exp)) #1500## [1] 1500sum(table(d1UK$GAR_exp)) #1502## [1] 1502sum(table(d1UK$SUN_exp)) #1502## [1] 1502sum(table(d1UK$MIR_exp)) #1502## [1] 1502sum(table(d1UK$UKT_exp)) #1502## [1] 1502sum(table(d1UK$TEL_exp)) #1502## [1] 1502sum(table(d1UK$BBC_exp)) #1502## [1] 1502sum(table(d1UK$IND_exp)) #1502## [1] 1502sum(table(d1UK$party_factor)) #1507## [1] 1507UK correlations
x <- cbind.data.frame(d1UK$vaxxAttitudes,
d1UK$healthRisk, d1UK$econRisk, d1UK$pEconRisk, d1UK$worstAB,
d1UK$index_AFexp, d1UK$index_ANexp, d1UK$index_TRexp)
cor <- cor(x, use = "complete.obs")
ggcorrplot(cor, type = "lower",
lab = TRUE, title = "general correlations")
x1 <- cbind.data.frame(w1UK$affect,
w1UK$analytic,
w1UK$cogproc,
w1UK$posemo,
w1UK$negemo,
w1UK$threat)
cor1 <- cor(x1, use = "complete.obs")
ggcorrplot(cor1, type = "lower",
lab = TRUE, title = "media analytic + affect correlations wave 1 & wave 2")
USA correlations
x <- cbind.data.frame(d1$vaxxAttitudes,
d1$healthRisk, d1$econRisk, d1$pEconRisk, d1$worstAB,
d1$index_AFexp, d1$index_ANexp, d1$index_TRexp)
cor <- cor(x, use = "complete.obs")
ggcorrplot(cor, type = "lower",
lab = TRUE, title = "general correlations")
x1 <- cbind.data.frame(w1$affect,
w1$analytic,
w1$cogproc,
w1$posemo,
w1$negemo,
w1$threat)
cor1 <- cor(x1, use = "complete.obs")
ggcorrplot(cor1, type = "lower",
lab = TRUE, title = "media analytic + affect correlations wave 1 & wave 2")
I United States
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-163. vaxxAttitudes ~ THREAT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_TRexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_TRexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6689 -1.5991 0.2306 1.8009 3.4268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003531 0.063677 0.055 0.955779
## DvR -1.210103 0.135900 -8.904 < 2e-16 ***
## IvDR 0.529604 0.143665 3.686 0.000231 ***
## index_TRexp -1.169856 1.034523 -1.131 0.258222
## sum.media.exp 0.122579 0.075356 1.627 0.103911
## DvR:index_TRexp 0.509404 0.114657 4.443 9.19e-06 ***
## IvDR:index_TRexp 0.054731 0.124095 0.441 0.659213
## ---
## 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: 0.0878, Adjusted R-squared: 0.08599
## F-statistic: 48.68 on 6 and 3035 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_TRexp, data = d1)
plot_model(m1, type = "pred", terms = c("index_TRexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media threat") +
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_TRexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_TRexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6689 -1.5991 0.2306 1.8009 3.4268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78335 0.10435 7.507 7.89e-14 ***
## Rep_1 -1.21010 0.13590 -8.904 < 2e-16 ***
## Ind_1 -1.13466 0.16271 -6.973 3.78e-12 ***
## index_TRexp -1.40650 1.03380 -1.361 0.174
## sum.media.exp 0.12258 0.07536 1.627 0.104
## Rep_1:index_TRexp 0.50940 0.11466 4.443 9.19e-06 ***
## Ind_1:index_TRexp 0.19997 0.13201 1.515 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: 0.0878, Adjusted R-squared: 0.08599
## F-statistic: 48.68 on 6 and 3035 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_TRexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_TRexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6689 -1.5991 0.2306 1.8009 3.4268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.42675 0.09183 -4.647 3.50e-06 ***
## Dem_1 1.21010 0.13590 8.904 < 2e-16 ***
## Ind_1 0.07545 0.15504 0.487 0.6266
## index_TRexp -0.89709 1.03510 -0.867 0.3862
## sum.media.exp 0.12258 0.07536 1.627 0.1039
## Dem_1:index_TRexp -0.50940 0.11466 -4.443 9.19e-06 ***
## Ind_1:index_TRexp -0.30943 0.14122 -2.191 0.0285 *
## ---
## 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: 0.0878, Adjusted R-squared: 0.08599
## F-statistic: 48.68 on 6 and 3035 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_TRexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_TRexp +
## sum.media.exp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6689 -1.5991 0.2306 1.8009 3.4268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.35130 0.12600 -2.788 0.00534 **
## Rep_1 -0.07545 0.15504 -0.487 0.62656
## Dem_1 1.13466 0.16271 6.973 3.78e-12 ***
## index_TRexp -1.20653 1.04285 -1.157 0.24739
## sum.media.exp 0.12258 0.07536 1.627 0.10391
## Rep_1:index_TRexp 0.30943 0.14122 2.191 0.02852 *
## Dem_1:index_TRexp -0.19997 0.13201 -1.515 0.12993
## ---
## 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: 0.0878, Adjusted R-squared: 0.08599
## F-statistic: 48.68 on 6 and 3035 DF, p-value: < 2.2e-165. 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-166. THREAT ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_TRexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = index_TRexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11046 -0.01646 0.00012 0.01302 1.15133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0068829 0.0010368 -6.639 3.73e-11 ***
## DvR -0.0043195 0.0022628 -1.909 0.0564 .
## IvDR -0.0106496 0.0023518 -4.528 6.18e-06 ***
## index_AFexp -0.0442526 0.0018425 -24.017 < 2e-16 ***
## sum.media.exp 0.0870978 0.0005989 145.434 < 2e-16 ***
## DvR:index_AFexp 0.0004849 0.0004148 1.169 0.2424
## IvDR:index_AFexp 0.0008969 0.0004447 2.017 0.0438 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03323 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.9982
## F-statistic: 2.757e+05 on 6 and 3038 DF, p-value: < 2.2e-16plot
m1 <- lm(index_TRexp ~ 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 threat ") +
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_TRexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11046 -0.01646 0.00012 0.01302 1.15133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0082375 0.0017268 -4.771 1.92e-06 ***
## Rep_1 -0.0043195 0.0022628 -1.909 0.05636 .
## Ind_1 0.0084898 0.0026776 3.171 0.00154 **
## index_AFexp -0.0441991 0.0018597 -23.767 < 2e-16 ***
## sum.media.exp 0.0870978 0.0005989 145.434 < 2e-16 ***
## Rep_1:index_AFexp 0.0004849 0.0004148 1.169 0.24244
## Ind_1:index_AFexp -0.0006544 0.0004743 -1.380 0.16775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03323 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.9982
## F-statistic: 2.757e+05 on 6 and 3038 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_TRexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = index_TRexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11046 -0.01646 0.00012 0.01302 1.15133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0125570 0.0014936 -8.407 < 2e-16 ***
## Dem_1 0.0043195 0.0022628 1.909 0.0564 .
## Ind_1 0.0128093 0.0025401 5.043 4.86e-07 ***
## index_AFexp -0.0437142 0.0018580 -23.528 < 2e-16 ***
## sum.media.exp 0.0870978 0.0005989 145.434 < 2e-16 ***
## Dem_1:index_AFexp -0.0004849 0.0004148 -1.169 0.2424
## Ind_1:index_AFexp -0.0011394 0.0005065 -2.250 0.0245 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03323 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.9982
## F-statistic: 2.757e+05 on 6 and 3038 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_TRexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11046 -0.01646 0.00012 0.01302 1.15133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0002523 0.0020665 0.122 0.90283
## Rep_1 -0.0128093 0.0025401 -5.043 4.86e-07 ***
## Dem_1 -0.0084898 0.0026776 -3.171 0.00154 **
## index_AFexp -0.0448535 0.0018688 -24.001 < 2e-16 ***
## sum.media.exp 0.0870978 0.0005989 145.434 < 2e-16 ***
## Rep_1:index_AFexp 0.0011394 0.0005065 2.250 0.02454 *
## Dem_1:index_AFexp 0.0006544 0.0004743 1.380 0.16775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03323 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.9982
## F-statistic: 2.757e+05 on 6 and 3038 DF, p-value: < 2.2e-167. THREAT ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(index_TRexp ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = index_TRexp ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081727 -0.009300 0.000370 0.009997 0.290134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.923e-04 6.258e-04 1.586 0.112914
## DvR 4.569e-03 1.310e-03 3.487 0.000495 ***
## IvDR -9.715e-04 1.389e-03 -0.700 0.484276
## index_ANexp 8.249e-03 9.909e-05 83.243 < 2e-16 ***
## sum.media.exp 1.714e-02 6.690e-04 25.623 < 2e-16 ***
## DvR:index_ANexp 1.621e-05 1.196e-05 1.354 0.175708
## IvDR:index_ANexp -9.642e-06 1.300e-05 -0.742 0.458202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 7.631e+05 on 6 and 3038 DF, p-value: < 2.2e-16plot
m1 <- lm(index_TRexp ~ 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("media threat ") +
xlim(0, 15) +
theme_minimal()+
labs(color ='partisan identity') ## Warning: Removed 51 row(s) containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
b. simple effects Dem
model1.dem <- lm(index_TRexp ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081727 -0.009300 0.000370 0.009997 0.290134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.613e-03 1.012e-03 -1.593 0.111226
## Rep_1 4.569e-03 1.310e-03 3.487 0.000495 ***
## Ind_1 3.256e-03 1.571e-03 2.072 0.038311 *
## index_ANexp 8.237e-03 9.885e-05 83.331 < 2e-16 ***
## sum.media.exp 1.714e-02 6.690e-04 25.623 < 2e-16 ***
## Rep_1:index_ANexp 1.621e-05 1.196e-05 1.354 0.175708
## Ind_1:index_ANexp 1.774e-05 1.382e-05 1.284 0.199331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 7.631e+05 on 6 and 3038 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_TRexp ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = index_TRexp ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081727 -0.009300 0.000370 0.009997 0.290134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.956e-03 9.007e-04 3.282 0.001042 **
## Dem_1 -4.569e-03 1.310e-03 -3.487 0.000495 ***
## Ind_1 -1.313e-03 1.499e-03 -0.876 0.381182
## index_ANexp 8.254e-03 9.917e-05 83.224 < 2e-16 ***
## sum.media.exp 1.714e-02 6.690e-04 25.623 < 2e-16 ***
## Dem_1:index_ANexp -1.621e-05 1.196e-05 -1.354 0.175708
## Ind_1:index_ANexp 1.539e-06 1.477e-05 0.104 0.917050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 7.631e+05 on 6 and 3038 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_TRexp ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp,
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081727 -0.009300 0.000370 0.009997 0.290134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.643e-03 1.219e-03 1.348 0.1778
## Rep_1 1.313e-03 1.499e-03 0.876 0.3812
## Dem_1 -3.256e-03 1.571e-03 -2.072 0.0383 *
## index_ANexp 8.255e-03 1.002e-04 82.402 <2e-16 ***
## sum.media.exp 1.714e-02 6.690e-04 25.623 <2e-16 ***
## Rep_1:index_ANexp -1.539e-06 1.477e-05 -0.104 0.9170
## Dem_1:index_ANexp -1.774e-05 1.382e-05 -1.284 0.1993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 3038 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 7.631e+05 on 6 and 3038 DF, p-value: < 2.2e-16II. United Kingdom
1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp) +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1853 -0.9735 0.5113 1.3487 4.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.654818 0.095056 6.889 8.27e-12 ***
## DvR -0.702085 0.197342 -3.558 0.000386 ***
## IvDR 0.342483 0.186750 1.834 0.066867 .
## index_AFexp -2.543045 0.639094 -3.979 7.25e-05 ***
## index_ANexp -0.115258 0.025051 -4.601 4.56e-06 ***
## sum.media.exp 2.562079 0.365673 7.006 3.69e-12 ***
## DvR:index_AFexp 3.168158 1.039661 3.047 0.002350 **
## DvR:index_ANexp -0.129760 0.044119 -2.941 0.003321 **
## IvDR:index_AFexp -0.245864 1.131145 -0.217 0.827959
## IvDR:index_ANexp 0.009208 0.048199 0.191 0.848520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.758 on 1492 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.05926
## F-statistic: 11.51 on 9 and 1492 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d1UK)
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') ## Warning: Removed 3 row(s) containing missing values (geom_path).
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d1UK)
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 = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1853 -0.9735 0.5113 1.3487 4.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.11888 0.11292 9.909 < 2e-16 ***
## Rep_1 -0.70208 0.19734 -3.558 0.000386 ***
## Ind_1 -0.69353 0.18750 -3.699 0.000224 ***
## index_AFexp -4.20826 0.65057 -6.469 1.34e-10 ***
## index_ANexp -0.04734 0.02670 -1.773 0.076396 .
## sum.media.exp 2.56208 0.36567 7.006 3.69e-12 ***
## Rep_1:index_AFexp 3.16816 1.03966 3.047 0.002350 **
## Rep_1:index_ANexp -0.12976 0.04412 -2.941 0.003321 **
## Ind_1:index_AFexp 1.82994 1.12915 1.621 0.105307
## Ind_1:index_ANexp -0.07409 0.04807 -1.541 0.123476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.758 on 1492 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.05926
## F-statistic: 11.51 on 9 and 1492 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 = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1853 -0.9735 0.5113 1.3487 4.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.416795 0.178557 2.334 0.019715 *
## Dem_1 0.702085 0.197342 3.558 0.000386 ***
## Ind_1 0.008559 0.232525 0.037 0.970641
## index_AFexp -1.040101 1.006512 -1.033 0.301598
## index_ANexp -0.177100 0.040556 -4.367 1.35e-05 ***
## sum.media.exp 2.562079 0.365673 7.006 3.69e-12 ***
## Dem_1:index_AFexp -3.168158 1.039661 -3.047 0.002350 **
## Dem_1:index_ANexp 0.129760 0.044119 2.941 0.003321 **
## Ind_1:index_AFexp -1.338215 1.350720 -0.991 0.321972
## Ind_1:index_ANexp 0.055672 0.057521 0.968 0.333276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.758 on 1492 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.05926
## F-statistic: 11.51 on 9 and 1492 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 = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp +
## index_ANexp) + sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1853 -0.9735 0.5113 1.3487 4.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.425355 0.158759 2.679 0.007460 **
## Rep_1 -0.008559 0.232525 -0.037 0.970641
## Dem_1 0.693525 0.187498 3.699 0.000224 ***
## index_AFexp -2.378317 1.088816 -2.184 0.029094 *
## index_ANexp -0.121427 0.045309 -2.680 0.007444 **
## sum.media.exp 2.562079 0.365673 7.006 3.69e-12 ***
## Rep_1:index_AFexp 1.338215 1.350720 0.991 0.321972
## Rep_1:index_ANexp -0.055672 0.057521 -0.968 0.333276
## Dem_1:index_AFexp -1.829943 1.129150 -1.621 0.105307
## Dem_1:index_ANexp 0.074088 0.048071 1.541 0.123476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.758 on 1492 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06491, Adjusted R-squared: 0.05926
## F-statistic: 11.51 on 9 and 1492 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9677 -0.9956 0.5035 1.4135 2.5035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.794239 0.093335 8.510 < 2e-16 ***
## DvR -0.481645 0.179089 -2.689 0.00724 **
## IvDR 0.444366 0.177189 2.508 0.01225 *
## index_ANexp -0.096545 0.023091 -4.181 3.07e-05 ***
## sum.media.exp 1.132558 0.260858 4.342 1.51e-05 ***
## DvR:index_ANexp 0.003300 0.001791 1.842 0.06563 .
## IvDR:index_ANexp -0.001394 0.001945 -0.717 0.47376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.0341
## F-statistic: 9.831 on 6 and 1495 DF, p-value: 1.185e-10plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_ANexp, data = d1UK)
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') ## Warning: Removed 3 row(s) containing missing values (geom_path).
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9677 -0.9956 0.5035 1.4135 2.5035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.181703 0.110867 10.659 < 2e-16 ***
## Rep_1 -0.481645 0.179089 -2.689 0.007237 **
## Ind_1 -0.685189 0.180840 -3.789 0.000157 ***
## index_ANexp -0.098656 0.023066 -4.277 2.01e-05 ***
## sum.media.exp 1.132558 0.260858 4.342 1.51e-05 ***
## Rep_1:index_ANexp 0.003300 0.001791 1.842 0.065634 .
## Ind_1:index_ANexp 0.003044 0.001983 1.535 0.125067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.0341
## F-statistic: 9.831 on 6 and 1495 DF, p-value: 1.185e-10c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9677 -0.9956 0.5035 1.4135 2.5035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7000580 0.1641494 4.265 2.13e-05 ***
## Dem_1 0.4816445 0.1790892 2.689 0.00724 **
## Ind_1 -0.2035441 0.2147675 -0.948 0.34341
## index_ANexp -0.0953551 0.0230229 -4.142 3.64e-05 ***
## sum.media.exp 1.1325582 0.2608575 4.342 1.51e-05 ***
## Dem_1:index_ANexp -0.0033004 0.0017915 -1.842 0.06563 .
## Ind_1:index_ANexp -0.0002565 0.0022884 -0.112 0.91076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.0341
## F-statistic: 9.831 on 6 and 1495 DF, p-value: 1.185e-10d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9677 -0.9956 0.5035 1.4135 2.5035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4965139 0.1545376 3.213 0.001342 **
## Rep_1 0.2035441 0.2147675 0.948 0.343414
## Dem_1 0.6851886 0.1808397 3.789 0.000157 ***
## index_ANexp -0.0956117 0.0232735 -4.108 4.20e-05 ***
## sum.media.exp 1.1325582 0.2608575 4.342 1.51e-05 ***
## Rep_1:index_ANexp 0.0002565 0.0022884 0.112 0.910761
## Dem_1:index_ANexp -0.0030439 0.0019833 -1.535 0.125067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.781 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.0341
## F-statistic: 9.831 on 6 and 1495 DF, p-value: 1.185e-103. vaxxAttitudes ~ AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1331 -0.9781 0.5366 1.3987 2.7756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.89687 0.08044 11.150 < 2e-16 ***
## DvR -0.39592 0.17974 -2.203 0.02776 *
## IvDR 0.47530 0.17646 2.694 0.00715 **
## index_AFexp -3.45326 0.58112 -5.942 3.49e-09 ***
## sum.media.exp 1.69871 0.27876 6.094 1.40e-09 ***
## DvR:index_AFexp 0.10664 0.04225 2.524 0.01170 *
## IvDR:index_AFexp -0.04720 0.04514 -1.046 0.29592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.771 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.04962, Adjusted R-squared: 0.04581
## F-statistic: 13.01 on 6 and 1495 DF, p-value: 2.185e-14plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_AFexp, data = d1UK)
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 = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1331 -0.9781 0.5366 1.3987 2.7756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.25168 0.10201 12.271 < 2e-16 ***
## Rep_1 -0.39592 0.17974 -2.203 0.027764 *
## Ind_1 -0.67326 0.18128 -3.714 0.000212 ***
## index_AFexp -3.52215 0.58343 -6.037 1.98e-09 ***
## sum.media.exp 1.69871 0.27876 6.094 1.40e-09 ***
## Rep_1:index_AFexp 0.10664 0.04225 2.524 0.011702 *
## Ind_1:index_AFexp 0.10052 0.04619 2.176 0.029684 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.771 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.04962, Adjusted R-squared: 0.04581
## F-statistic: 13.01 on 6 and 1495 DF, p-value: 2.185e-14c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1331 -0.9781 0.5366 1.3987 2.7756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.855759 0.151672 5.642 2.01e-08 ***
## Dem_1 0.395918 0.179737 2.203 0.0278 *
## Ind_1 -0.277340 0.213461 -1.299 0.1941
## index_AFexp -3.415513 0.579014 -5.899 4.52e-09 ***
## sum.media.exp 1.698707 0.278764 6.094 1.40e-09 ***
## Dem_1:index_AFexp -0.106636 0.042247 -2.524 0.0117 *
## Ind_1:index_AFexp -0.006119 0.053243 -0.115 0.9085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.771 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.04962, Adjusted R-squared: 0.04581
## F-statistic: 13.01 on 6 and 1495 DF, p-value: 2.185e-14d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1331 -0.9781 0.5366 1.3987 2.7756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.578419 0.151497 3.818 0.000140 ***
## Rep_1 0.277340 0.213461 1.299 0.194058
## Dem_1 0.673258 0.181279 3.714 0.000212 ***
## index_AFexp -3.421632 0.582848 -5.871 5.34e-09 ***
## sum.media.exp 1.698707 0.278764 6.094 1.40e-09 ***
## Rep_1:index_AFexp 0.006119 0.053243 0.115 0.908523
## Dem_1:index_AFexp -0.100518 0.046186 -2.176 0.029684 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.771 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.04962, Adjusted R-squared: 0.04581
## F-statistic: 13.01 on 6 and 1495 DF, p-value: 2.185e-143. vaxxAttitudes ~ THREAT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_TRexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_TRexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.040 -1.014 0.553 1.433 2.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.01668 0.07922 12.834 < 2e-16 ***
## DvR -0.36801 0.18186 -2.024 0.043189 *
## IvDR 0.56079 0.17728 3.163 0.001591 **
## index_TRexp 3.25008 0.98249 3.308 0.000962 ***
## sum.media.exp -0.39717 0.13308 -2.984 0.002888 **
## DvR:index_TRexp 0.28576 0.15065 1.897 0.058036 .
## IvDR:index_TRexp -0.21059 0.16166 -1.303 0.192891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.786 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03332, Adjusted R-squared: 0.02945
## F-statistic: 8.59 on 6 and 1495 DF, p-value: 3.345e-09plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_TRexp, data = d1UK)
plot_model(m1, type = "pred", terms = c("index_TRexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media threat") +
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_TRexp + sum.media.exp, data = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_TRexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.040 -1.014 0.553 1.433 2.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3857 0.1002 13.826 < 2e-16 ***
## Rep_1 -0.3680 0.1819 -2.024 0.04319 *
## Ind_1 -0.7448 0.1824 -4.083 4.68e-05 ***
## index_TRexp 3.0377 0.9756 3.114 0.00188 **
## sum.media.exp -0.3972 0.1331 -2.984 0.00289 **
## Rep_1:index_TRexp 0.2858 0.1506 1.897 0.05804 .
## Ind_1:index_TRexp 0.3535 0.1651 2.141 0.03243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.786 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03332, Adjusted R-squared: 0.02945
## F-statistic: 8.59 on 6 and 1495 DF, p-value: 3.345e-09c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_TRexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_TRexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.040 -1.014 0.553 1.433 2.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.01774 0.15186 6.702 2.91e-11 ***
## Dem_1 0.36801 0.18186 2.024 0.04319 *
## Ind_1 -0.37679 0.21474 -1.755 0.07954 .
## index_TRexp 3.32347 0.99228 3.349 0.00083 ***
## sum.media.exp -0.39717 0.13308 -2.984 0.00289 **
## Dem_1:index_TRexp -0.28576 0.15065 -1.897 0.05804 .
## Ind_1:index_TRexp 0.06771 0.19069 0.355 0.72259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.786 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03332, Adjusted R-squared: 0.02945
## F-statistic: 8.59 on 6 and 1495 DF, p-value: 3.345e-09d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_TRexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_TRexp +
## sum.media.exp, data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.040 -1.014 0.553 1.433 2.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.64095 0.15249 4.203 2.79e-05 ***
## Rep_1 0.37679 0.21474 1.755 0.079535 .
## Dem_1 0.74480 0.18242 4.083 4.68e-05 ***
## index_TRexp 3.39118 0.99423 3.411 0.000665 ***
## sum.media.exp -0.39717 0.13308 -2.984 0.002888 **
## Rep_1:index_TRexp -0.06771 0.19069 -0.355 0.722591
## Dem_1:index_TRexp -0.35347 0.16509 -2.141 0.032426 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.786 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.03332, Adjusted R-squared: 0.02945
## F-statistic: 8.59 on 6 and 1495 DF, p-value: 3.345e-095. ANALYTIC ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = index_ANexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0528 -1.5130 0.0573 1.4144 21.7089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.11426 0.09050 -23.361 < 2e-16 ***
## DvR -0.83019 0.20223 -4.105 4.26e-05 ***
## IvDR -1.10769 0.19854 -5.579 2.86e-08 ***
## index_AFexp 1.78442 0.65382 2.729 0.00642 **
## sum.media.exp 10.43752 0.31364 33.278 < 2e-16 ***
## DvR:index_AFexp 0.06101 0.04753 1.284 0.19949
## IvDR:index_AFexp 0.19826 0.05079 3.904 9.90e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 2.449e+05 on 6 and 1495 DF, p-value: < 2.2e-16plot
m1 <- lm(index_ANexp ~ party_factor * index_AFexp, data = d1UK)
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 = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0528 -1.5130 0.0573 1.4144 21.7089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.06471 0.11477 -17.990 < 2e-16 ***
## Rep_1 -0.83019 0.20223 -4.105 4.26e-05 ***
## Ind_1 0.69260 0.20396 3.396 0.000702 ***
## index_AFexp 1.81933 0.65642 2.772 0.005648 **
## sum.media.exp 10.43752 0.31364 33.278 < 2e-16 ***
## Rep_1:index_AFexp 0.06101 0.04753 1.284 0.199485
## Ind_1:index_AFexp -0.16775 0.05196 -3.228 0.001273 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 2.449e+05 on 6 and 1495 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0528 -1.5130 0.0573 1.4144 21.7089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.89490 0.17065 -16.964 < 2e-16 ***
## Dem_1 0.83019 0.20223 4.105 4.26e-05 ***
## Ind_1 1.52279 0.24017 6.340 3.03e-10 ***
## index_AFexp 1.88035 0.65146 2.886 0.00395 **
## sum.media.exp 10.43752 0.31364 33.278 < 2e-16 ***
## Dem_1:index_AFexp -0.06101 0.04753 -1.284 0.19949
## Ind_1:index_AFexp -0.22877 0.05990 -3.819 0.00014 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 2.449e+05 on 6 and 1495 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0528 -1.5130 0.0573 1.4144 21.7089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.37211 0.17045 -8.050 1.68e-15 ***
## Rep_1 -1.52279 0.24017 -6.340 3.03e-10 ***
## Dem_1 -0.69260 0.20396 -3.396 0.000702 ***
## index_AFexp 1.65158 0.65577 2.519 0.011888 *
## sum.media.exp 10.43752 0.31364 33.278 < 2e-16 ***
## Rep_1:index_AFexp 0.22877 0.05990 3.819 0.000140 ***
## Dem_1:index_AFexp 0.16775 0.05196 3.228 0.001273 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.992 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.999, Adjusted R-squared: 0.999
## F-statistic: 2.449e+05 on 6 and 1495 DF, p-value: < 2.2e-166. THREAT ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_TRexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = index_TRexp ~ (DvR + IvDR) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11150 -0.02333 -0.00069 0.01555 0.69560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0162863 0.0015819 -10.295 < 2e-16 ***
## DvR -0.0107314 0.0035347 -3.036 0.00244 **
## IvDR -0.0089423 0.0034702 -2.577 0.01007 *
## index_AFexp -0.4054344 0.0114282 -35.477 < 2e-16 ***
## sum.media.exp 0.3297258 0.0054822 60.145 < 2e-16 ***
## DvR:index_AFexp 0.0006536 0.0008308 0.787 0.43159
## IvDR:index_AFexp 0.0023812 0.0008878 2.682 0.00739 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03482 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978
## F-statistic: 1.159e+05 on 6 and 1495 DF, p-value: < 2.2e-16plot
m1 <- lm(index_TRexp ~ party_factor * index_AFexp, data = d1UK)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("media threat ") +
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_TRexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11150 -0.02333 -0.00069 0.01555 0.69560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0138716 0.0020061 -6.915 6.92e-12 ***
## Rep_1 -0.0107314 0.0035347 -3.036 0.00244 **
## Ind_1 0.0035766 0.0035650 1.003 0.31590
## index_AFexp -0.4049754 0.0114737 -35.296 < 2e-16 ***
## sum.media.exp 0.3297258 0.0054822 60.145 < 2e-16 ***
## Rep_1:index_AFexp 0.0006536 0.0008308 0.787 0.43159
## Ind_1:index_AFexp -0.0020544 0.0009083 -2.262 0.02385 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03482 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978
## F-statistic: 1.159e+05 on 6 and 1495 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_TRexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = index_TRexp ~ (Dem_1 + Ind_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11150 -0.02333 -0.00069 0.01555 0.69560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0246029 0.0029828 -8.248 3.48e-16 ***
## Dem_1 0.0107314 0.0035347 3.036 0.002439 **
## Ind_1 0.0143080 0.0041979 3.408 0.000671 ***
## index_AFexp -0.4043218 0.0113869 -35.508 < 2e-16 ***
## sum.media.exp 0.3297258 0.0054822 60.145 < 2e-16 ***
## Dem_1:index_AFexp -0.0006536 0.0008308 -0.787 0.431594
## Ind_1:index_AFexp -0.0027080 0.0010471 -2.586 0.009796 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03482 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978
## F-statistic: 1.159e+05 on 6 and 1495 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_TRexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Dem_1) * index_AFexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11150 -0.02333 -0.00069 0.01555 0.69560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0102950 0.0029793 -3.455 0.000565 ***
## Rep_1 -0.0143080 0.0041979 -3.408 0.000671 ***
## Dem_1 -0.0035766 0.0035650 -1.003 0.315903
## index_AFexp -0.4070298 0.0114623 -35.510 < 2e-16 ***
## sum.media.exp 0.3297258 0.0054822 60.145 < 2e-16 ***
## Rep_1:index_AFexp 0.0027080 0.0010471 2.586 0.009796 **
## Dem_1:index_AFexp 0.0020544 0.0009083 2.262 0.023850 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03482 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978
## F-statistic: 1.159e+05 on 6 and 1495 DF, p-value: < 2.2e-167. THREAT ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(index_TRexp ~ (DvR + IvDR) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.cc)##
## Call:
## lm(formula = index_TRexp ~ (DvR + IvDR) * index_ANexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17331 -0.02048 -0.00025 0.02082 0.15157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.035e-02 2.171e-03 9.373 < 2e-16 ***
## DvR -4.994e-03 4.165e-03 -1.199 0.230697
## IvDR 1.374e-02 4.121e-03 3.333 0.000879 ***
## index_ANexp 1.130e-02 5.370e-04 21.034 < 2e-16 ***
## sum.media.exp 7.714e-03 6.067e-03 1.271 0.203790
## DvR:index_ANexp -1.664e-04 4.167e-05 -3.994 6.82e-05 ***
## IvDR:index_ANexp -2.542e-05 4.524e-05 -0.562 0.574304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04143 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.997, Adjusted R-squared: 0.997
## F-statistic: 8.18e+04 on 6 and 1495 DF, p-value: < 2.2e-16plot
m1 <- lm(index_TRexp ~ party_factor * index_ANexp, data = d1UK)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("media threat ") +
xlim(0, 15) +
theme_minimal()+
labs(color ='partisan identity') ## Warning: Removed 54 row(s) containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
b. simple effects Dem
model1.dem <- lm(index_TRexp ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.dem)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17331 -0.02048 -0.00025 0.02082 0.15157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.738e-02 2.579e-03 10.618 < 2e-16 ***
## Rep_1 -4.994e-03 4.165e-03 -1.199 0.230697
## Ind_1 -1.623e-02 4.206e-03 -3.860 0.000118 ***
## index_ANexp 1.137e-02 5.365e-04 21.196 < 2e-16 ***
## sum.media.exp 7.714e-03 6.067e-03 1.271 0.203790
## Rep_1:index_ANexp -1.664e-04 4.167e-05 -3.994 6.82e-05 ***
## Ind_1:index_ANexp -5.778e-05 4.613e-05 -1.253 0.210519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04143 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.997, Adjusted R-squared: 0.997
## F-statistic: 8.18e+04 on 6 and 1495 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_TRexp ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.rep)##
## Call:
## lm(formula = index_TRexp ~ (Dem_1 + Ind_1) * index_ANexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17331 -0.02048 -0.00025 0.02082 0.15157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.238e-02 3.818e-03 5.863 5.59e-09 ***
## Dem_1 4.994e-03 4.165e-03 1.199 0.2307
## Ind_1 -1.124e-02 4.995e-03 -2.250 0.0246 *
## index_ANexp 1.120e-02 5.355e-04 20.925 < 2e-16 ***
## sum.media.exp 7.714e-03 6.067e-03 1.271 0.2038
## Dem_1:index_ANexp 1.664e-04 4.167e-05 3.994 6.82e-05 ***
## Ind_1:index_ANexp 1.086e-04 5.322e-05 2.041 0.0415 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04143 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.997, Adjusted R-squared: 0.997
## F-statistic: 8.18e+04 on 6 and 1495 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_TRexp ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp, data = d1UK)
summary(model1.ind)##
## Call:
## lm(formula = index_TRexp ~ (Rep_1 + Dem_1) * index_ANexp + sum.media.exp,
## data = d1UK)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17331 -0.02048 -0.00025 0.02082 0.15157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.114e-02 3.594e-03 3.100 0.001969 **
## Rep_1 1.124e-02 4.995e-03 2.250 0.024577 *
## Dem_1 1.623e-02 4.206e-03 3.860 0.000118 ***
## index_ANexp 1.131e-02 5.413e-04 20.901 < 2e-16 ***
## sum.media.exp 7.714e-03 6.067e-03 1.271 0.203790
## Rep_1:index_ANexp -1.086e-04 5.322e-05 -2.041 0.041450 *
## Dem_1:index_ANexp 5.778e-05 4.613e-05 1.253 0.210519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04143 on 1495 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.997, Adjusted R-squared: 0.997
## F-statistic: 8.18e+04 on 6 and 1495 DF, p-value: < 2.2e-16III. long by UK + USA models
1. vaxxAttitudes ~ USvUK + ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_ANexp), data = dm)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_ANexp),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.493 -1.325 0.308 1.600 3.247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4435926 0.0483695 9.171 < 2e-16 ***
## USvUK 0.7338275 0.0639625 11.473 < 2e-16 ***
## DvR -0.9648791 0.1068596 -9.029 < 2e-16 ***
## IvDR 0.4809054 0.1108919 4.337 1.48e-05 ***
## index_ANexp 0.0051812 0.0004673 11.088 < 2e-16 ***
## DvR:index_ANexp 0.0047322 0.0010133 4.670 3.10e-06 ***
## IvDR:index_ANexp 0.0002462 0.0010969 0.224 0.822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.095, Adjusted R-squared: 0.0938
## F-statistic: 79.37 on 6 and 4537 DF, p-value: < 2.2e-16- plot
m1 <- lm(vaxxAttitudes ~ country + index_ANexp * party_factor, 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') ## Warning: Removed 3 row(s) containing missing values (geom_path).
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ USvUK + index_ANexp * (Rep_1 + Ind_1), data = dm)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_ANexp * (Rep_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.493 -1.325 0.308 1.600 3.247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0847309 0.0723705 14.989 < 2e-16 ***
## USvUK 0.7338275 0.0639625 11.473 < 2e-16 ***
## index_ANexp 0.0028963 0.0006144 4.714 2.50e-06 ***
## Rep_1 -0.9648791 0.1068596 -9.029 < 2e-16 ***
## Ind_1 -0.9633450 0.1219602 -7.899 3.51e-15 ***
## index_ANexp:Rep_1 0.0047322 0.0010133 4.670 3.10e-06 ***
## index_ANexp:Ind_1 0.0021199 0.0011494 1.844 0.0652 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.095, Adjusted R-squared: 0.0938
## F-statistic: 79.37 on 6 and 4537 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ USvUK + index_ANexp * (Dem_1 + Ind_1), data = dm)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_ANexp * (Dem_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.493 -1.325 0.308 1.600 3.247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1198518 0.0781633 1.533 0.1253
## USvUK 0.7338275 0.0639625 11.473 < 2e-16 ***
## index_ANexp 0.0076285 0.0008024 9.507 < 2e-16 ***
## Dem_1 0.9648791 0.1068596 9.029 < 2e-16 ***
## Ind_1 0.0015342 0.1242143 0.012 0.9901
## index_ANexp:Dem_1 -0.0047322 0.0010133 -4.670 3.1e-06 ***
## index_ANexp:Ind_1 -0.0026123 0.0012644 -2.066 0.0389 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.095, Adjusted R-squared: 0.0938
## F-statistic: 79.37 on 6 and 4537 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ USvUK + index_ANexp * (Dem_1 + Rep_1), data = dm)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_ANexp * (Dem_1 + Rep_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.493 -1.325 0.308 1.600 3.247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1213860 0.0980091 1.239 0.2156
## USvUK 0.7338275 0.0639625 11.473 < 2e-16 ***
## index_ANexp 0.0050162 0.0009758 5.141 2.85e-07 ***
## Dem_1 0.9633450 0.1219602 7.899 3.51e-15 ***
## Rep_1 -0.0015342 0.1242143 -0.012 0.9901
## index_ANexp:Dem_1 -0.0021199 0.0011494 -1.844 0.0652 .
## index_ANexp:Rep_1 0.0026123 0.0012644 2.066 0.0389 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.095, Adjusted R-squared: 0.0938
## F-statistic: 79.37 on 6 and 4537 DF, p-value: < 2.2e-162. vaxxAttitudes ~ USvUK + AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ USvUK + (DvR + IvDR) * index_AFexp, data = dm)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (DvR + IvDR) * index_AFexp,
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4739 -1.3336 0.3102 1.5933 3.3070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.426820 0.048649 8.773 < 2e-16 ***
## USvUK 0.795842 0.064942 12.255 < 2e-16 ***
## DvR -0.977148 0.109386 -8.933 < 2e-16 ***
## IvDR 0.462552 0.112105 4.126 3.76e-05 ***
## index_AFexp 0.113613 0.009970 11.396 < 2e-16 ***
## DvR:index_AFexp 0.101966 0.021586 4.724 2.39e-06 ***
## IvDR:index_AFexp 0.008532 0.023087 0.370 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09644, Adjusted R-squared: 0.09524
## F-statistic: 80.71 on 6 and 4537 DF, p-value: < 2.2e-16- plot
m1 <- lm(vaxxAttitudes ~ USvUK + index_AFexp * party_factor, 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 <- lm(vaxxAttitudes ~ USvUK + index_AFexp * (Rep_1 + Ind_1), data = dm)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_AFexp * (Rep_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4739 -1.3336 0.3102 1.5933 3.3070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.06804 0.07323 14.584 < 2e-16 ***
## USvUK 0.79584 0.06494 12.255 < 2e-16 ***
## index_AFexp 0.06545 0.01316 4.975 6.78e-07 ***
## Rep_1 -0.97715 0.10939 -8.933 < 2e-16 ***
## Ind_1 -0.95113 0.12318 -7.721 1.41e-14 ***
## index_AFexp:Rep_1 0.10197 0.02159 4.724 2.39e-06 ***
## index_AFexp:Ind_1 0.04245 0.02418 1.756 0.0792 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09644, Adjusted R-squared: 0.09524
## F-statistic: 80.71 on 6 and 4537 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ USvUK + index_AFexp * (Dem_1 + Ind_1), data = dm)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_AFexp * (Dem_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4739 -1.3336 0.3102 1.5933 3.3070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09089 0.07992 1.137 0.256
## USvUK 0.79584 0.06494 12.255 < 2e-16 ***
## index_AFexp 0.16741 0.01713 9.772 < 2e-16 ***
## Dem_1 0.97715 0.10939 8.933 < 2e-16 ***
## Ind_1 0.02602 0.12627 0.206 0.837
## index_AFexp:Dem_1 -0.10197 0.02159 -4.724 2.39e-06 ***
## index_AFexp:Ind_1 -0.05951 0.02673 -2.227 0.026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09644, Adjusted R-squared: 0.09524
## F-statistic: 80.71 on 6 and 4537 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ USvUK + index_AFexp * (Dem_1 + Rep_1), data = dm)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_AFexp * (Dem_1 + Rep_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4739 -1.3336 0.3102 1.5933 3.3070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11691 0.09868 1.185 0.2362
## USvUK 0.79584 0.06494 12.255 < 2e-16 ***
## index_AFexp 0.10790 0.02052 5.257 1.53e-07 ***
## Dem_1 0.95113 0.12318 7.721 1.41e-14 ***
## Rep_1 -0.02602 0.12627 -0.206 0.8367
## index_AFexp:Dem_1 -0.04245 0.02418 -1.756 0.0792 .
## index_AFexp:Rep_1 0.05951 0.02673 2.227 0.0260 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09644, Adjusted R-squared: 0.09524
## F-statistic: 80.71 on 6 and 4537 DF, p-value: < 2.2e-163. vaxxAttitudes ~ USvUK + (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_AFexp + index_ANexp), data = dm)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_AFexp +
## index_ANexp), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3789 -1.3191 0.3283 1.5755 3.4168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.422440 0.048903 8.638 < 2e-16 ***
## USvUK 0.868662 0.079933 10.867 < 2e-16 ***
## DvR -0.979157 0.110588 -8.854 < 2e-16 ***
## IvDR 0.450192 0.112571 3.999 6.46e-05 ***
## index_AFexp 0.235281 0.083341 2.823 0.00478 **
## index_ANexp -0.005711 0.003904 -1.463 0.14362
## DvR:index_AFexp 0.188111 0.142005 1.325 0.18534
## DvR:index_ANexp -0.004159 0.006664 -0.624 0.53258
## IvDR:index_AFexp 0.067566 0.171463 0.394 0.69356
## IvDR:index_ANexp -0.002768 0.008144 -0.340 0.73395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4534 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09703, Adjusted R-squared: 0.09524
## F-statistic: 54.13 on 9 and 4534 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) * party_factor, 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') ## Warning: Removed 3 row(s) containing missing values (geom_path).
m1 <- lm(vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) * party_factor, 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 <- lm(vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) * (Rep_1 + Ind_1), data = dm)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) *
## (Rep_1 + Ind_1), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3789 -1.3191 0.3283 1.5755 3.4168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0605821 0.0733864 14.452 < 2e-16 ***
## USvUK 0.8686621 0.0799326 10.867 < 2e-16 ***
## index_AFexp 0.1635222 0.0918450 1.780 0.0751 .
## index_ANexp -0.0045449 0.0042760 -1.063 0.2879
## Rep_1 -0.9791566 0.1105880 -8.854 < 2e-16 ***
## Ind_1 -0.9397702 0.1234272 -7.614 3.21e-14 ***
## index_AFexp:Rep_1 0.1881106 0.1420046 1.325 0.1853
## index_AFexp:Ind_1 0.0264892 0.1749487 0.151 0.8797
## index_ANexp:Rep_1 -0.0041591 0.0066640 -0.624 0.5326
## index_ANexp:Ind_1 0.0006885 0.0083201 0.083 0.9341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4534 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09703, Adjusted R-squared: 0.09524
## F-statistic: 54.13 on 9 and 4534 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) * (Dem_1 + Ind_1), data = dm)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) *
## (Dem_1 + Ind_1), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3789 -1.3191 0.3283 1.5755 3.4168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.081425 0.081361 1.001 0.31698
## USvUK 0.868662 0.079933 10.867 < 2e-16 ***
## index_AFexp 0.351633 0.124888 2.816 0.00489 **
## index_ANexp -0.008704 0.005846 -1.489 0.13661
## Dem_1 0.979157 0.110588 8.854 < 2e-16 ***
## Ind_1 0.039386 0.127377 0.309 0.75717
## index_AFexp:Dem_1 -0.188111 0.142005 -1.325 0.18534
## index_AFexp:Ind_1 -0.161621 0.195639 -0.826 0.40878
## index_ANexp:Dem_1 0.004159 0.006664 0.624 0.53258
## index_ANexp:Ind_1 0.004848 0.009253 0.524 0.60038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4534 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09703, Adjusted R-squared: 0.09524
## F-statistic: 54.13 on 9 and 4534 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) * (Dem_1 + Rep_1), data = dm)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (index_AFexp + index_ANexp) *
## (Dem_1 + Rep_1), data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3789 -1.3191 0.3283 1.5755 3.4168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1208119 0.0987815 1.223 0.221
## USvUK 0.8686621 0.0799326 10.867 < 2e-16 ***
## index_AFexp 0.1900114 0.1630470 1.165 0.244
## index_ANexp -0.0038564 0.0077402 -0.498 0.618
## Dem_1 0.9397702 0.1234272 7.614 3.21e-14 ***
## Rep_1 -0.0393864 0.1273769 -0.309 0.757
## index_AFexp:Dem_1 -0.0264892 0.1749487 -0.151 0.880
## index_AFexp:Rep_1 0.1616215 0.1956395 0.826 0.409
## index_ANexp:Dem_1 -0.0006885 0.0083201 -0.083 0.934
## index_ANexp:Rep_1 -0.0048476 0.0092531 -0.524 0.600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.982 on 4534 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09703, Adjusted R-squared: 0.09524
## F-statistic: 54.13 on 9 and 4534 DF, p-value: < 2.2e-164. vaxxAttitudes ~ USvUK + THREAT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_TRexp), data = dm)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + (DvR + IvDR) * (index_TRexp),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4706 -1.3219 0.3047 1.6181 3.2533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.429414 0.049226 8.723 < 2e-16 ***
## USvUK 0.686882 0.063489 10.819 < 2e-16 ***
## DvR -1.005061 0.107681 -9.334 < 2e-16 ***
## IvDR 0.494632 0.111738 4.427 9.79e-06 ***
## index_TRexp 0.468722 0.041995 11.161 < 2e-16 ***
## DvR:index_TRexp 0.457531 0.091200 5.017 5.45e-07 ***
## IvDR:index_TRexp 0.005454 0.098867 0.055 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09539, Adjusted R-squared: 0.0942
## F-statistic: 79.74 on 6 and 4537 DF, p-value: < 2.2e-16- plot
m1 <- lm(vaxxAttitudes ~ country + index_TRexp * party_factor, data = dm)
plot_model(m1, type = "pred", terms = c("index_TRexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media threat") +
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 ~ USvUK + index_TRexp * (Rep_1 + Ind_1), data = dm)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_TRexp * (Rep_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4706 -1.3219 0.3047 1.6181 3.2533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.09517 0.07352 14.897 < 2e-16 ***
## USvUK 0.68688 0.06349 10.819 < 2e-16 ***
## index_TRexp 0.24176 0.05520 4.379 1.22e-05 ***
## Rep_1 -1.00506 0.10768 -9.334 < 2e-16 ***
## Ind_1 -0.99716 0.12301 -8.106 6.65e-16 ***
## index_TRexp:Rep_1 0.45753 0.09120 5.017 5.45e-07 ***
## index_TRexp:Ind_1 0.22331 0.10367 2.154 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09539, Adjusted R-squared: 0.0942
## F-statistic: 79.74 on 6 and 4537 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ USvUK + index_TRexp * (Dem_1 + Ind_1), data = dm)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_TRexp * (Dem_1 + Ind_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4706 -1.3219 0.3047 1.6181 3.2533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.090112 0.079087 1.139 0.2546
## USvUK 0.686882 0.063489 10.819 < 2e-16 ***
## index_TRexp 0.699288 0.072267 9.676 < 2e-16 ***
## Dem_1 1.005061 0.107681 9.334 < 2e-16 ***
## Ind_1 0.007899 0.125048 0.063 0.9496
## index_TRexp:Dem_1 -0.457531 0.091200 -5.017 5.45e-07 ***
## index_TRexp:Ind_1 -0.234220 0.113842 -2.057 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09539, Adjusted R-squared: 0.0942
## F-statistic: 79.74 on 6 and 4537 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ USvUK + index_TRexp * (Dem_1 + Rep_1), data = dm)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ USvUK + index_TRexp * (Dem_1 + Rep_1),
## data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4706 -1.3219 0.3047 1.6181 3.2533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.098011 0.098780 0.992 0.3211
## USvUK 0.686882 0.063489 10.819 < 2e-16 ***
## index_TRexp 0.465068 0.087862 5.293 1.26e-07 ***
## Dem_1 0.997162 0.123009 8.106 6.65e-16 ***
## Rep_1 -0.007899 0.125048 -0.063 0.9496
## index_TRexp:Dem_1 -0.223312 0.103672 -2.154 0.0313 *
## index_TRexp:Rep_1 0.234220 0.113842 2.057 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.983 on 4537 degrees of freedom
## (287 observations deleted due to missingness)
## Multiple R-squared: 0.09539, Adjusted R-squared: 0.0942
## F-statistic: 79.74 on 6 and 4537 DF, p-value: < 2.2e-16