grant analyses
Dani Grant
1/4/2021
- NOTES:
- set up
- descriptives
- corr matrix
- I. AVG Wave 1 + Wave 2
- II. WAVE 1
- III. WAVE 2
- IV. LONGITUDINAL MODELS
- V. long by W1 + W2 models
NOTES:
- all analytic and affect indexes are averages (to account for participants who didn’t fill out all the media exposure ratings)
set up
libraries and data sets
library(psych)
library(lme4)## Loading required package: Matrixlibrary(dplyr)##
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library(minqa)
library(dfoptim)
library(ggcorrplot)
#import wave 1
d1.1 <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_US_Cleaned1.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import wave 2
d2.1 <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_US_Wave2_Cleaned.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import LIWC csv
liwc <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_w1w2_Dec_2021.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)LIWC wave 1 & wave 2
###################################
# Create LIWC rating averages for wave 1
###################################
#move over measures of interest
w1 <- data.frame(liwc[liwc$Wave == 1,])
w1 <- w1[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
# create wide data set for wave 2
w1w = w1 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w1w[paste0("affect_",1:21)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w1w[paste0("cogproc_",1:21)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w1w[paste0("posemo_",1:21)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w1w[paste0("negemo_",1:21)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w1w[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w1 <- data.frame(w1w$mediaOutlet)
colnames(w1)[colnames(w1)=="w1w.mediaOutlet"] <- "mediaOutlet"
w1$affect <- AF
w1$cogproc <- CP
w1$analytic <- AN
w1$posemo <- PE
w1$negemo <- NE
##############################################
# Create LIWC rating averages for wave 2
##############################################
#move over measures of interest
w2 <- data.frame(liwc[liwc$Wave == 2,])
w2 <- w2[,c("mediaOutlet", "analytic", "affect", "cogproc", "posemo", "negemo")]
# create wide data set for wave 2
w2w = w2 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
"affect",
"cogproc",
"posemo",
"negemo",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
##################
### calculate averages for each ratings
##################
#affect
affect <- cbind(w2w[paste0("affect_",1:22)])
AF <- apply(affect, MARGIN = 1, FUN = mean, na.rm = T)
#cognitive processing
cogproc <- data.frame(w2w[paste0("cogproc_",1:22)])
CP <- apply(cogproc, MARGIN = 1, FUN = mean, na.rm = T)
#positive emotions
pos <- data.frame(w2w[paste0("posemo_",1:22)])
PE <- apply(pos, MARGIN = 1, FUN = mean, na.rm = T)
#negative emotions
neg <- data.frame(w2w[paste0("negemo_",1:22)])
NE <- apply(neg, MARGIN = 1, FUN = mean, na.rm = T)
#analytic
analytic <- data.frame(w2w[paste0("analytic_",1:22)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w2 <- data.frame(w2w$mediaOutlet)
colnames(w2)[colnames(w2) == "w2w.mediaOutlet"] <- "mediaOutlet"
w2$affect <- AF
w2$cogproc <- CP
w2$analytic <- AN
w2$posemo <- PE
w2$negemo <- NEprep wave 1 data
#delete any measures we don't want---exclude media exposure #3, #8, and #9
## missing Yahoo, Huff Post, Wash Post
d1 <- d1.1[,c("s3", "Wave", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_4",
"mediaExposure_5", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11", "mediaExposure_12",
"mediaExposure_13", "mediaExposure_14", "mediaExposure_15")]
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
## affect
d1$ABC_AF <- w1$affect[w1$mediaOutlet == "ABC"]
d1$CBS_AF <- w1$affect[w1$mediaOutlet == "CBS"]
d1$CNN_AF <- w1$affect[w1$mediaOutlet == "CNN"]
d1$Fox_AF <- w1$affect[w1$mediaOutlet == "Fox"]
d1$MSNBC_AF <- w1$affect[w1$mediaOutlet == "MSNBC"]
d1$NBC_AF <- w1$affect[w1$mediaOutlet == "NBC"]
d1$NPR_AF <- w1$affect[w1$mediaOutlet == "NPR"]
d1$NYT_AF <- w1$affect[w1$mediaOutlet == "NYT"]
d1$PBS_AF <- w1$affect[w1$mediaOutlet == "PBS"]
d1$USAT_AF <- w1$affect[w1$mediaOutlet == "USAToday"]
d1$WSJ_AF <- w1$affect[w1$mediaOutlet == "WSJ"]
d1$AOL_AF <- w1$affect[w1$mediaOutlet == "AOL"]
## analytic thinking
d1$ABC_AN <- w1$analytic[w1$mediaOutlet == "ABC"]
d1$CBS_AN <- w1$analytic[w1$mediaOutlet == "CBS"]
d1$CNN_AN <- w1$analytic[w1$mediaOutlet == "CNN"]
d1$Fox_AN <- w1$analytic[w1$mediaOutlet == "Fox"]
d1$MSNBC_AN <- w1$analytic[w1$mediaOutlet == "MSNBC"]
d1$NBC_AN <- w1$analytic[w1$mediaOutlet == "NBC"]
d1$NPR_AN <- w1$analytic[w1$mediaOutlet == "NPR"]
d1$NYT_AN <- w1$analytic[w1$mediaOutlet == "NYT"]
d1$PBS_AN <- w1$analytic[w1$mediaOutlet == "PBS"]
d1$USAT_AN <- w1$analytic[w1$mediaOutlet == "USAToday"]
d1$WSJ_AN <- w1$analytic[w1$mediaOutlet == "WSJ"]
d1$AOL_AN <- w1$analytic[w1$mediaOutlet == "AOL"]
#individual media affect
d1$ABC_AFexp <- d1$ABC_AF * d1$ABC_exp
d1$CBS_AFexp <- d1$CBS_AF * d1$CBS_exp
d1$CNN_AFexp <- d1$CNN_AF * d1$CNN_exp
d1$Fox_AFexp <- d1$Fox_AF * d1$Fox_exp
d1$MSNBC_AFexp <- d1$MSNBC_AF * d1$MSNBC_exp
d1$NBC_AFexp <- d1$NBC_AF * d1$NBC_exp
d1$NPR_AFexp <- d1$NPR_AF * d1$NPR_exp
d1$NYT_AFexp <- d1$NYT_AF * d1$NYT_exp
d1$PBS_AFexp <- d1$PBS_AF * d1$PBS_exp
d1$USAT_AFexp <- d1$USAT_AF * d1$USAT_exp
d1$WSJ_AFexp <- d1$WSJ_AF * d1$WSJ_exp
d1$AOL_AFexp <- d1$AOL_AF * d1$AOL_exp
x <- cbind(d1$ABC_AFexp,
d1$CBS_AFexp,
d1$CNN_AFexp,
d1$Fox_AFexp,
d1$MSNBC_AFexp,
d1$NBC_AFexp,
d1$NPR_AFexp,
d1$NYT_AFexp,
d1$PBS_AFexp,
d1$USAT_AFexp,
d1$WSJ_AFexp,
d1$AOL_AFexp)
d1$index_AFexp <- rowMeans(x, na.rm = T)
#individual media affect
d1$ABC_ANexp <- d1$ABC_AN * d1$ABC_exp
d1$CBS_ANexp <- d1$CBS_AN * d1$CBS_exp
d1$CNN_ANexp <- d1$CNN_AN * d1$CNN_exp
d1$Fox_ANexp <- d1$Fox_AN * d1$Fox_exp
d1$MSNBC_ANexp <- d1$MSNBC_AN * d1$MSNBC_exp
d1$NBC_ANexp <- d1$NBC_AN * d1$NBC_exp
d1$NPR_ANexp <- d1$NPR_AN * d1$NPR_exp
d1$NYT_ANexp <- d1$NYT_AN * d1$NYT_exp
d1$PBS_ANexp <- d1$PBS_AN * d1$PBS_exp
d1$USAT_ANexp <- d1$USAT_AN * d1$USAT_exp
d1$WSJ_ANexp <- d1$WSJ_AN * d1$WSJ_exp
d1$AOL_ANexp <- d1$AOL_AN * d1$AOL_exp
x <- cbind(d1$ABC_ANexp,
d1$CBS_ANexp,
d1$CNN_ANexp,
d1$Fox_ANexp,
d1$MSNBC_ANexp,
d1$NBC_ANexp,
d1$NPR_ANexp,
d1$NYT_ANexp,
d1$PBS_ANexp,
d1$USAT_ANexp,
d1$WSJ_ANexp,
d1$AOL_ANexp)
d1$index_ANexp <- rowMeans(x, na.rm = T)
#####################################################
# codes for party
####################################################
d1$partyCont <- NA
d1$partyCont[d1$demStrength == 1] <- -3
d1$partyCont[d1$demStrength == 2] <- -2
d1$partyCont[d1$partyClose == 1] <- -1
d1$partyCont[d1$partyClose == 3] <- 0
d1$partyCont[d1$partyClose == 2] <- 1
d1$partyCont[d1$repStrength == 2] <- 2
d1$partyCont[d1$repStrength == 1] <- 3
## party factor
d1$party_factor <- NA
d1$party_factor[d1$partyCont < 0] <- 'Democrat'
d1$party_factor[d1$partyCont == 0] <- 'Independent'
d1$party_factor[d1$partyCont > 0] <- 'Republican'
## Order of party variable
d1$party_factor <- factor(d1$party_factor,
levels = c('Democrat', 'Republican', 'Independent'))
## contrast codes
d1$DvR <- NA
d1$DvR[d1$party_factor == 'Democrat'] <- -.5
d1$DvR[d1$party_factor == 'Independent'] <- 0
d1$DvR[d1$party_factor == 'Republican'] <- .5
d1$IvDR <- NA
d1$IvDR[d1$party_factor == 'Democrat'] <- .33
d1$IvDR[d1$party_factor == 'Independent'] <- -.67
d1$IvDR[d1$party_factor == 'Republican'] <- .33
## dummy codes
d1$Rep_1[d1$party_factor == 'Democrat'] <- 0
d1$Rep_1[d1$party_factor == 'Republican'] <- 1
d1$Rep_1[d1$party_factor == 'Independent'] <- 0
d1$Ind_1[d1$party_factor == 'Democrat'] <- 0
d1$Ind_1[d1$party_factor == 'Republican'] <- 0
d1$Ind_1[d1$party_factor == 'Independent'] <- 1
d1$Dem_1[d1$party_factor == 'Democrat'] <- 1
d1$Dem_1[d1$party_factor == 'Republican'] <- 0
d1$Dem_1[d1$party_factor == 'Independent'] <- 0
## delete unnecessary columns
d1$party <- NULL
d1$demStrength <- NULL
d1$repStrength <- NULL
d1$partyClose <- NULLprep wave 2 data
# delete any measures we don't want---exclude media exposure #3, #8, and #9
# missing Yahoo, Huff Post, Wash Post
d2 <- d2.1[,c("s3", "Wave", "vaxxAttitudes",
"demStrength", "repStrength", "partyClose",
"mediaExposure_1", "mediaExposure_2", "mediaExposure_4",
"mediaExposure_5", "mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11", "mediaExposure_12",
"mediaExposure_13", "mediaExposure_14", "mediaExposure_15")]
colnames(d2)[colnames(d2) == "s3"] <- "participant"
# rename exposure
colnames(d2)[colnames(d2) == "mediaExposure_1"] <- "NYT_exp"
colnames(d2)[colnames(d2) == "mediaExposure_2"] <- "WSJ_exp"
colnames(d2)[colnames(d2) == "mediaExposure_4"] <- "USAT_exp"
colnames(d2)[colnames(d2) == "mediaExposure_5"] <- "Fox_exp"
colnames(d2)[colnames(d2) == "mediaExposure_6"] <- "CNN_exp"
colnames(d2)[colnames(d2) == "mediaExposure_7"] <- "MSNBC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_10"] <- "AOL_exp"
colnames(d2)[colnames(d2) == "mediaExposure_11"] <- "NPR_exp"
colnames(d2)[colnames(d2) == "mediaExposure_12"] <- "ABC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_13"] <- "NBC_exp"
colnames(d2)[colnames(d2) == "mediaExposure_14"] <- "CBS_exp"
colnames(d2)[colnames(d2) == "mediaExposure_15"] <- "PBS_exp"
# change to 0-4 rating
d2$ABC_exp <- d2$ABC_exp - 1
d2$CBS_exp <- d2$CBS_exp - 1
d2$CNN_exp <- d2$CNN_exp - 1
d2$Fox_exp <- d2$Fox_exp - 1
d2$MSNBC_exp <- d2$MSNBC_exp - 1
d2$NBC_exp <- d2$NBC_exp - 1
d2$NPR_exp <- d2$NPR_exp - 1
d2$NYT_exp <- d2$NYT_exp - 1
d2$PBS_exp <- d2$PBS_exp - 1
d2$USAT_exp <- d2$USAT_exp - 1
d2$WSJ_exp <- d2$WSJ_exp - 1
d2$AOL_exp <- d2$AOL_exp - 1
## affect
d2$ABC_AF <- w2$affect[w2$mediaOutlet == "ABC"]
d2$CBS_AF <- w2$affect[w2$mediaOutlet == "CBS"]
d2$CNN_AF <- w2$affect[w2$mediaOutlet == "CNN"]
d2$Fox_AF <- w2$affect[w2$mediaOutlet == "Fox"]
d2$MSNBC_AF <- w2$affect[w2$mediaOutlet == "MSNBC"]
d2$NBC_AF <- w2$affect[w2$mediaOutlet == "NBC"]
d2$NPR_AF <- w2$affect[w2$mediaOutlet == "NPR"]
d2$NYT_AF <- w2$affect[w2$mediaOutlet == "NYT"]
d2$PBS_AF <- w2$affect[w2$mediaOutlet == "PBS"]
d2$USAT_AF <- w2$affect[w2$mediaOutlet == "USAToday"]
d2$WSJ_AF <- w2$affect[w2$mediaOutlet == "WSJ"]
d2$AOL_AF <- w2$affect[w2$mediaOutlet == "AOL"]
## analytic thinking
d2$ABC_AN <- w2$analytic[w2$mediaOutlet == "ABC"]
d2$CBS_AN <- w2$analytic[w2$mediaOutlet == "CBS"]
d2$CNN_AN <- w2$analytic[w2$mediaOutlet == "CNN"]
d2$Fox_AN <- w2$analytic[w2$mediaOutlet == "Fox"]
d2$MSNBC_AN <- w2$analytic[w2$mediaOutlet == "MSNBC"]
d2$NBC_AN <- w2$analytic[w2$mediaOutlet == "NBC"]
d2$NPR_AN <- w2$analytic[w2$mediaOutlet == "NPR"]
d2$NYT_AN <- w2$analytic[w2$mediaOutlet == "NYT"]
d2$PBS_AN <- w2$analytic[w2$mediaOutlet == "PBS"]
d2$USAT_AN <- w2$analytic[w2$mediaOutlet == "USAToday"]
d2$WSJ_AN <- w2$analytic[w2$mediaOutlet == "WSJ"]
d2$AOL_AN <- w2$analytic[w2$mediaOutlet == "AOL"]
#individual media affect
d2$ABC_AFexp <- d2$ABC_AF * d2$ABC_exp
d2$CBS_AFexp <- d2$CBS_AF * d2$CBS_exp
d2$CNN_AFexp <- d2$CNN_AF * d2$CNN_exp
d2$Fox_AFexp <- d2$Fox_AF * d2$Fox_exp
d2$MSNBC_AFexp <- d2$MSNBC_AF * d2$MSNBC_exp
d2$NBC_AFexp <- d2$NBC_AF * d2$NBC_exp
d2$NPR_AFexp <- d2$NPR_AF * d2$NPR_exp
d2$NYT_AFexp <- d2$NYT_AF * d2$NYT_exp
d2$PBS_AFexp <- d2$PBS_AF * d2$PBS_exp
d2$USAT_AFexp <- d2$USAT_AF * d2$USAT_exp
d2$WSJ_AFexp <- d2$WSJ_AF * d2$WSJ_exp
d2$AOL_AFexp <- d2$AOL_AF * d2$AOL_exp
x <- cbind(d2$ABC_AFexp,
d2$CBS_AFexp,
d2$CNN_AFexp,
d2$Fox_AFexp,
d2$MSNBC_AFexp,
d2$NBC_AFexp,
d2$NPR_AFexp,
d2$NYT_AFexp,
d2$PBS_AFexp,
d2$USAT_AFexp,
d2$WSJ_AFexp,
d2$AOL_AFexp)
d2$index_AFexp <- rowMeans(x, na.rm = T)
#individual media affect
d2$ABC_ANexp <- d2$ABC_AN * d2$ABC_exp
d2$CBS_ANexp <- d2$CBS_AN * d2$CBS_exp
d2$CNN_ANexp <- d2$CNN_AN * d2$CNN_exp
d2$Fox_ANexp <- d2$Fox_AN * d2$Fox_exp
d2$MSNBC_ANexp <- d2$MSNBC_AN * d2$MSNBC_exp
d2$NBC_ANexp <- d2$NBC_AN * d2$NBC_exp
d2$NPR_ANexp <- d2$NPR_AN * d2$NPR_exp
d2$NYT_ANexp <- d2$NYT_AN * d2$NYT_exp
d2$PBS_ANexp <- d2$PBS_AN * d2$PBS_exp
d2$USAT_ANexp <- d2$USAT_AN * d2$USAT_exp
d2$WSJ_ANexp <- d2$WSJ_AN * d2$WSJ_exp
d2$AOL_ANexp <- d2$AOL_AN * d2$AOL_exp
x <- cbind(d2$ABC_ANexp,
d2$CBS_ANexp,
d2$CNN_ANexp,
d2$Fox_ANexp,
d2$MSNBC_ANexp,
d2$NBC_ANexp,
d2$NPR_ANexp,
d2$NYT_ANexp,
d2$PBS_ANexp,
d2$USAT_ANexp,
d2$WSJ_ANexp,
d2$AOL_ANexp)
d2$index_ANexp <- rowMeans(x, na.rm = T)
#####################################################
# codes for party
####################################################
d2$partyCont <- NA
d2$partyCont[d2$demStrength == 1] <- -3
d2$partyCont[d2$demStrength == 2] <- -2
d2$partyCont[d2$partyClose == 1] <- -1
d2$partyCont[d2$partyClose == 3] <- 0
d2$partyCont[d2$partyClose == 2] <- 1
d2$partyCont[d2$repStrength == 2] <- 2
d2$partyCont[d2$repStrength == 1] <- 3
# party factor
d2$party_factor <- NA
d2$party_factor[d2$partyCont < 0] <- 'Democrat'
d2$party_factor[d2$partyCont == 0] <- 'Independent'
d2$party_factor[d2$partyCont > 0] <- 'Republican'
## Order of party variable
d2$party_factor <- factor(d2$party_factor,
levels = c('Democrat', 'Republican','Independent'))
## Contrast codes
d2$DvR <- NA
d2$DvR[d2$party_factor == 'Democrat'] <- -.5
d2$DvR[d2$party_factor == 'Independent'] <- 0
d2$DvR[d2$party_factor == 'Republican'] <- .5
d2$IvDR <- NA
d2$IvDR[d2$party_factor == 'Democrat'] <- .33
d2$IvDR[d2$party_factor == 'Independent'] <- -.67
d2$IvDR[d2$party_factor == 'Republican'] <- .33
## dummy codes
d2$Rep_1[d2$party_factor == 'Democrat'] <- 0
d2$Rep_1[d2$party_factor == 'Republican'] <- 1
d2$Rep_1[d2$party_factor == 'Independent'] <- 0
d2$Ind_1[d2$party_factor == 'Democrat'] <- 0
d2$Ind_1[d2$party_factor == 'Republican'] <- 0
d2$Ind_1[d2$party_factor == 'Independent'] <- 1
d2$Dem_1[d2$party_factor == 'Democrat'] <- 1
d2$Dem_1[d2$party_factor == 'Republican'] <- 0
d2$Dem_1[d2$party_factor == 'Independent'] <- 0
## delete unnecessary columns
d2$party <- NULL
d2$demStrength <- NULL
d2$repStrength <- NULL
d2$partyClose <- NULLLONG merged data
names(d1) == names(d2)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEdm <- rbind.data.frame(d1, d2)
dm$participant <- as.factor(dm$participant)
dm$W1vW2 <- NA
dm$W1vW2[dm$Wave == 1] <- -.5
dm$W1vW2[dm$Wave == 2] <- .5
dm$W1_0 <- NA
dm$W1_0[dm$Wave == 1] <- 0
dm$W1_0[dm$Wave == 2] <- 1
dm$W2_0 <- NA
dm$W2_0[dm$Wave == 1] <- 1
dm$W2_0[dm$Wave == 2] <- 0WIDE merged data
#d1 = x; d2 = y
dw <- merge(d1, d2, by = c("participant"), all.x = T, all.y = T)
dw$party_match <- TRUE
dw$party_match[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Republican'] <- FALSE
dw$party_match[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Democrat']<- FALSE
#make weak leaners
dw$party_factor.y[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Independent']<- 'Democrat'
dw$party_factor.y[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Republican']<- 'Republican'
dw$party_factor.x[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Democrat']<- 'Democrat'
dw$party_factor.x[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Independent']<- 'Republican'
dw$partyCont.y[dw$party_factor.y == 'Democrat' & dw$party_factor.x == 'Independent']<- -1
dw$partyCont.y[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Republican']<- 1
dw$partyCont.x[dw$party_factor.y == 'Independent' & dw$party_factor.x == 'Democrat']<- -1
dw$partyCont.x[dw$party_factor.y == 'Republican' & dw$party_factor.x == 'Independent']<- 1
#get rid of party swaps
dw <- dw[dw$party_match,]
colnames(dw)[colnames(dw) == "vaxxAttitudes.x"] <- "vaxxAttitudes.w1"
colnames(dw)[colnames(dw) == "vaxxAttitudes.y"] <- "vaxxAttitudes.w2"
dw$vaxxAttitudes.c.w1 <- dw$vaxxAttitudes.w1 - mean(dw$vaxxAttitudes.w1, na.rm = T)
colnames(dw)[colnames(dw) == "party_factor.x"] <- "party_factor"
dw$party_factor.y <- NULL
colnames(dw)[colnames(dw) == "partyCont.x"] <- "partyCont.w1"
colnames(dw)[colnames(dw) == "partyCont.y"] <- "partyCont.w2"
colnames(dw)[colnames(dw) == "index_AFexp.x"] <- "index_AFexp.w1"
colnames(dw)[colnames(dw) == "index_AFexp.y"] <- "index_AFexp.w2"
colnames(dw)[colnames(dw) == "index_ANexp.x"] <- "index_ANexp.w1"
colnames(dw)[colnames(dw) == "index_ANexp.y"] <- "index_ANexp.w2"
colnames(dw)[colnames(dw) == "DvR.x"] <- "DvR"
colnames(dw)[colnames(dw) == "IvDR.x"] <- "IvDR"
colnames(dw)[colnames(dw) == "Ind_1.x"] <- "Ind_1"
colnames(dw)[colnames(dw) == "Rep_1.x"] <- "Rep_1"
colnames(dw)[colnames(dw) == "Dem_1.x"] <- "Dem_1"
#get averages
x <- cbind(dw$vaxxAttitudes.w1, dw$vaxxAttitudes.w2)
dw$avgVaxxAttitudes <- rowMeans(x, na.rm = T)
x <- cbind(dw$index_AFexp.w1, dw$index_AFexp.w2)
dw$avg_AFexp <- rowMeans(x, na.rm = T)
x <- cbind(dw$index_ANexp.w1, dw$index_ANexp.w2)
dw$avg_ANexp <- rowMeans(x, na.rm = T)descriptives
sum(table(d1$vaxxAttitudes)) #3051 responses for vaxxAttitudes in wave 1## [1] 3051sum(table(d2$vaxxAttitudes)) #2419 responses for vaxxAttitudes in wave 2## [1] 2419sum(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(d2$ABC_exp)) #2412## [1] 2412sum(table(d2$CBS_exp)) #2413## [1] 2413sum(table(d2$CNN_exp))#2411## [1] 2411sum(table(d2$Fox_exp)) #2414## [1] 2414sum(table(d2$MSNBC_exp)) #2413## [1] 2413sum(table(d2$NBC_exp)) #2412## [1] 2412sum(table(d2$NPR_exp)) #2413## [1] 2413sum(table(d2$NYT_exp)) #2412## [1] 2412sum(table(d2$PBS_exp))#2412## [1] 2412sum(table(d2$USAT_exp))#2413## [1] 2413sum(table(d2$WSJ_exp)) #2413## [1] 2413sum(table(d2$AOL_exp)) #2411## [1] 2411sum(table(d1$party_factor))## [1] 3147sum(table(d2$party_factor))## [1] 2426tapply(d1$index_AFexp, d1$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 5.469034 3.406757 3.935891tapply(d1$index_ANexp, d1$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 108.65344 65.97917 78.10298tapply(d2$index_AFexp, d2$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 5.095026 2.902290 3.173599tapply(d2$index_ANexp, d2$party_factor, mean, na.rm = T)## Democrat Republican Independent
## 102.32919 57.04594 63.54699corr matrix
x <- cbind.data.frame(dw$index_ANexp.w1, dw$index_ANexp.w2, dw$index_AFexp.w1, dw$index_AFexp.w2, dw$avg_ANexp, dw$avg_AFexp, dw$partyCont.w1, dw$partyCont.w2)
corr <- cor(x, use = "complete.obs")
ggcorrplot(corr, type = "lower",
lab = TRUE, title = "correlations")
I. AVG Wave 1 + Wave 2
1. avg_vaxxAttitudes ~ (avg_ANALYTIC + avg_AFFECT) * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * (avg_AFexp + avg_ANexp), data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * (avg_AFexp + avg_ANexp),
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7656 -1.4629 0.2072 1.6467 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.155727 0.063167 -2.465 0.0137 *
## DvR -1.170813 0.142538 -8.214 3.14e-16 ***
## IvDR 0.608711 0.144158 4.223 2.49e-05 ***
## avg_AFexp 0.289147 0.104733 2.761 0.0058 **
## avg_ANexp -0.007961 0.005055 -1.575 0.1154
## DvR:avg_AFexp 0.996666 0.214940 4.637 3.69e-06 ***
## DvR:avg_ANexp -0.043077 0.010306 -4.180 3.00e-05 ***
## IvDR:avg_AFexp -0.216677 0.254294 -0.852 0.3942
## IvDR:avg_ANexp 0.010664 0.012319 0.866 0.3867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.944 on 2997 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.09102, Adjusted R-squared: 0.0886
## F-statistic: 37.51 on 8 and 2997 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) *
(avg_AFexp + avg_ANexp), data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * (avg_AFexp +
## avg_ANexp), data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7656 -1.4629 0.2072 1.6467 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.630554 0.107382 5.872 4.78e-09 ***
## Rep_1 -1.170813 0.142538 -8.214 3.14e-16 ***
## Ind_1 -1.194118 0.165025 -7.236 5.84e-13 ***
## avg_AFexp -0.280689 0.130273 -2.155 0.03127 *
## avg_ANexp 0.017097 0.006229 2.745 0.00609 **
## Rep_1:avg_AFexp 0.996666 0.214940 4.637 3.69e-06 ***
## Rep_1:avg_ANexp -0.043077 0.010306 -4.180 3.00e-05 ***
## Ind_1:avg_AFexp 0.715010 0.264739 2.701 0.00696 **
## Ind_1:avg_ANexp -0.032203 0.012806 -2.515 0.01197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.944 on 2997 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.09102, Adjusted R-squared: 0.0886
## F-statistic: 37.51 on 8 and 2997 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) *
(avg_AFexp + avg_ANexp), data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * (avg_AFexp +
## avg_ANexp), data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7656 -1.4629 0.2072 1.6467 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.540260 0.093734 -5.764 9.06e-09 ***
## Dem_1 1.170813 0.142538 8.214 3.14e-16 ***
## Ind_1 -0.023304 0.156488 -0.149 0.88163
## avg_AFexp 0.715977 0.170963 4.188 2.90e-05 ***
## avg_ANexp -0.025981 0.008211 -3.164 0.00157 **
## Dem_1:avg_AFexp -0.996666 0.214940 -4.637 3.69e-06 ***
## Dem_1:avg_ANexp 0.043077 0.010306 4.180 3.00e-05 ***
## Ind_1:avg_AFexp -0.281656 0.286956 -0.982 0.32641
## Ind_1:avg_ANexp 0.010875 0.013879 0.784 0.43338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.944 on 2997 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.09102, Adjusted R-squared: 0.0886
## F-statistic: 37.51 on 8 and 2997 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) *
(avg_AFexp + avg_ANexp), data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * (avg_AFexp +
## avg_ANexp), data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7656 -1.4629 0.2072 1.6467 3.9363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56356 0.12531 -4.497 7.14e-06 ***
## Rep_1 0.02330 0.15649 0.149 0.88163
## Dem_1 1.19412 0.16502 7.236 5.84e-13 ***
## avg_AFexp 0.43432 0.23047 1.885 0.05959 .
## avg_ANexp -0.01511 0.01119 -1.350 0.17710
## Rep_1:avg_AFexp 0.28166 0.28696 0.982 0.32641
## Rep_1:avg_ANexp -0.01087 0.01388 -0.784 0.43338
## Dem_1:avg_AFexp -0.71501 0.26474 -2.701 0.00696 **
## Dem_1:avg_ANexp 0.03220 0.01281 2.515 0.01197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.944 on 2997 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.09102, Adjusted R-squared: 0.0886
## F-statistic: 37.51 on 8 and 2997 DF, p-value: < 2.2e-162. avg_vaxxAttitudes ~ avg_AFFECT * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * avg_AFexp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * avg_AFexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6358 -1.4584 0.1954 1.6461 3.5314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13498 0.06131 -2.201 0.0278 *
## DvR -0.99894 0.13687 -7.299 3.71e-13 ***
## IvDR 0.59163 0.14107 4.194 2.82e-05 ***
## avg_AFexp 0.12593 0.01207 10.436 < 2e-16 ***
## DvR:avg_AFexp 0.10508 0.02667 3.941 8.31e-05 ***
## IvDR:avg_AFexp 0.00147 0.02797 0.053 0.9581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08515, Adjusted R-squared: 0.08362
## F-statistic: 55.84 on 5 and 3000 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_AFexp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_AFexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6358 -1.4584 0.1954 1.6461 3.5314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55973 0.10452 5.355 9.19e-08 ***
## Rep_1 -0.99894 0.13687 -7.299 3.71e-13 ***
## Ind_1 -1.09110 0.16168 -6.748 1.79e-11 ***
## avg_AFexp 0.07387 0.01688 4.376 1.25e-05 ***
## Rep_1:avg_AFexp 0.10508 0.02667 3.941 8.31e-05 ***
## Ind_1:avg_AFexp 0.05107 0.02982 1.713 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08515, Adjusted R-squared: 0.08362
## F-statistic: 55.84 on 5 and 3000 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_AFexp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_AFexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6358 -1.4584 0.1954 1.6461 3.5314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.43922 0.08837 -4.970 7.06e-07 ***
## Dem_1 0.99894 0.13687 7.299 3.71e-13 ***
## Ind_1 -0.09216 0.15174 -0.607 0.5437
## avg_AFexp 0.17896 0.02064 8.670 < 2e-16 ***
## Dem_1:avg_AFexp -0.10508 0.02667 -3.941 8.31e-05 ***
## Ind_1:avg_AFexp -0.05401 0.03210 -1.683 0.0926 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08515, Adjusted R-squared: 0.08362
## F-statistic: 55.84 on 5 and 3000 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_AFexp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_AFexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6358 -1.4584 0.1954 1.6461 3.5314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.53137 0.12335 -4.308 1.70e-05 ***
## Rep_1 0.09216 0.15174 0.607 0.5437
## Dem_1 1.09110 0.16168 6.748 1.79e-11 ***
## avg_AFexp 0.12494 0.02458 5.083 3.95e-07 ***
## Rep_1:avg_AFexp 0.05401 0.03210 1.683 0.0926 .
## Dem_1:avg_AFexp -0.05107 0.02982 -1.713 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.949 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08515, Adjusted R-squared: 0.08362
## F-statistic: 55.84 on 5 and 3000 DF, p-value: < 2.2e-163. avg_vaxxAttitudes ~ avg_ANALYTIC * party
a. contrast model
model1.cc <- lm(avgVaxxAttitudes ~ (DvR + IvDR) * avg_ANexp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (DvR + IvDR) * avg_ANexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7078 -1.4650 0.2101 1.6444 3.5001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1061776 0.0596888 -1.779 0.075365 .
## DvR -0.9252151 0.1323044 -6.993 3.30e-12 ***
## IvDR 0.5878714 0.1380327 4.259 2.12e-05 ***
## avg_ANexp 0.0059352 0.0005825 10.190 < 2e-16 ***
## DvR:avg_ANexp 0.0043674 0.0012798 3.413 0.000652 ***
## IvDR:avg_ANexp 0.0001111 0.0013553 0.082 0.934694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08322, Adjusted R-squared: 0.08169
## F-statistic: 54.46 on 5 and 3000 DF, p-value: < 2.2e-16plot
b. simple effects Dem
model1.dem <- lm(avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_ANexp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Ind_1) * avg_ANexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7078 -1.4650 0.2101 1.6444 3.5001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5504275 0.1011177 5.443 5.65e-08 ***
## Rep_1 -0.9252151 0.1323044 -6.993 3.30e-12 ***
## Ind_1 -1.0504789 0.1578028 -6.657 3.31e-11 ***
## avg_ANexp 0.0037882 0.0008081 4.688 2.88e-06 ***
## Rep_1:avg_ANexp 0.0043674 0.0012798 3.413 0.000652 ***
## Ind_1:avg_ANexp 0.0020727 0.0014424 1.437 0.150835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08322, Adjusted R-squared: 0.08169
## F-statistic: 54.46 on 5 and 3000 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_ANexp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Dem_1 + Ind_1) * avg_ANexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7078 -1.4650 0.2101 1.6444 3.5001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3747877 0.0853210 -4.393 1.16e-05 ***
## Dem_1 0.9252151 0.1323044 6.993 3.30e-12 ***
## Ind_1 -0.1252638 0.1481776 -0.845 0.397976
## avg_ANexp 0.0081556 0.0009924 8.218 3.03e-16 ***
## Dem_1:avg_ANexp -0.0043674 0.0012798 -3.413 0.000652 ***
## Ind_1:avg_ANexp -0.0022948 0.0015531 -1.478 0.139644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08322, Adjusted R-squared: 0.08169
## F-statistic: 54.46 on 5 and 3000 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_ANexp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avgVaxxAttitudes ~ (Rep_1 + Dem_1) * avg_ANexp,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7078 -1.4650 0.2101 1.6444 3.5001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.500051 0.121148 -4.128 3.77e-05 ***
## Rep_1 0.125264 0.148178 0.845 0.398
## Dem_1 1.050479 0.157803 6.657 3.31e-11 ***
## avg_ANexp 0.005861 0.001195 4.905 9.82e-07 ***
## Rep_1:avg_ANexp 0.002295 0.001553 1.478 0.140
## Dem_1:avg_ANexp -0.002073 0.001442 -1.437 0.151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 3000 degrees of freedom
## (411 observations deleted due to missingness)
## Multiple R-squared: 0.08322, Adjusted R-squared: 0.08169
## F-statistic: 54.46 on 5 and 3000 DF, p-value: < 2.2e-164. avg_ANALYTIC ~ avg_AFFECT * party
a. contrast model
model1.cc <- lm(avg_ANexp ~ (DvR + IvDR) * avg_AFexp, data = dw)
summary(model1.cc)##
## Call:
## lm(formula = avg_ANexp ~ (DvR + IvDR) * avg_AFexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.969 -4.764 1.209 4.694 33.241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.38809 0.24612 -13.766 < 2e-16 ***
## DvR 0.26566 0.54920 0.484 0.628620
## IvDR -1.87633 0.56639 -3.313 0.000935 ***
## avg_AFexp 20.62844 0.04844 425.852 < 2e-16 ***
## DvR:avg_AFexp -0.06815 0.10701 -0.637 0.524278
## IvDR:avg_AFexp 0.22150 0.11229 1.973 0.048636 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.828 on 3003 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9865, Adjusted R-squared: 0.9865
## F-statistic: 4.392e+04 on 5 and 3003 DF, p-value: < 2.2e-16plot
## `avg_ANexp` was not found in model terms. Maybe misspelled?## Warning: Removed 48 row(s) containing missing values (geom_path).
b. simple effects Dem
model1.dem <- lm(avg_ANexp ~ (Rep_1 + Ind_1) * avg_AFexp, data = dw)
summary(model1.dem)##
## Call:
## lm(formula = avg_ANexp ~ (Rep_1 + Ind_1) * avg_AFexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.969 -4.764 1.209 4.694 33.241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.14010 0.41973 -9.864 < 2e-16 ***
## Rep_1 0.26566 0.54920 0.484 0.62862
## Ind_1 2.00916 0.64928 3.094 0.00199 **
## avg_AFexp 20.73561 0.06779 305.900 < 2e-16 ***
## Rep_1:avg_AFexp -0.06815 0.10701 -0.637 0.52428
## Ind_1:avg_AFexp -0.25557 0.11975 -2.134 0.03291 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.828 on 3003 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9865, Adjusted R-squared: 0.9865
## F-statistic: 4.392e+04 on 5 and 3003 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(avg_ANexp ~ (Dem_1 + Ind_1) * avg_AFexp, data = dw)
summary(model1.rep)##
## Call:
## lm(formula = avg_ANexp ~ (Dem_1 + Ind_1) * avg_AFexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.969 -4.764 1.209 4.694 33.241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.87445 0.35418 -10.939 < 2e-16 ***
## Dem_1 -0.26566 0.54920 -0.484 0.62862
## Ind_1 1.74350 0.60897 2.863 0.00422 **
## avg_AFexp 20.66746 0.08281 249.591 < 2e-16 ***
## Dem_1:avg_AFexp 0.06815 0.10701 0.637 0.52428
## Ind_1:avg_AFexp -0.18742 0.12885 -1.455 0.14589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.828 on 3003 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9865, Adjusted R-squared: 0.9865
## F-statistic: 4.392e+04 on 5 and 3003 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(avg_ANexp ~ (Rep_1 + Dem_1) * avg_AFexp, data = dw)
summary(model1.ind)##
## Call:
## lm(formula = avg_ANexp ~ (Rep_1 + Dem_1) * avg_AFexp, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.969 -4.764 1.209 4.694 33.241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.13095 0.49537 -4.302 1.75e-05 ***
## Rep_1 -1.74350 0.60897 -2.863 0.00422 **
## Dem_1 -2.00916 0.64928 -3.094 0.00199 **
## avg_AFexp 20.48004 0.09872 207.454 < 2e-16 ***
## Rep_1:avg_AFexp 0.18742 0.12885 1.455 0.14589
## Dem_1:avg_AFexp 0.25557 0.11975 2.134 0.03291 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.828 on 3003 degrees of freedom
## (408 observations deleted due to missingness)
## Multiple R-squared: 0.9865, Adjusted R-squared: 0.9865
## F-statistic: 4.392e+04 on 5 and 3003 DF, p-value: < 2.2e-16II. WAVE 1
1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp), data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp),
## data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7628 -1.5860 0.2761 1.7980 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.026098 0.065208 -0.400 0.689020
## DvR -1.409247 0.146485 -9.620 < 2e-16 ***
## IvDR 0.576950 0.149324 3.864 0.000114 ***
## index_AFexp 0.296050 0.099508 2.975 0.002952 **
## index_ANexp -0.008724 0.004811 -1.813 0.069868 .
## DvR:index_AFexp 0.779364 0.199567 3.905 9.62e-05 ***
## DvR:index_ANexp -0.031730 0.009577 -3.313 0.000933 ***
## IvDR:index_AFexp -0.248788 0.244621 -1.017 0.309219
## IvDR:index_ANexp 0.012615 0.011873 1.062 0.288107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3033 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09335, Adjusted R-squared: 0.09096
## F-statistic: 39.04 on 8 and 3033 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), data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp +
## index_ANexp), data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7628 -1.5860 0.2761 1.7980 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.868919 0.109142 7.961 2.38e-15 ***
## Rep_1 -1.409247 0.146485 -9.620 < 2e-16 ***
## Ind_1 -1.281573 0.169839 -7.546 5.91e-14 ***
## index_AFexp -0.175732 0.120474 -1.459 0.144759
## index_ANexp 0.011304 0.005751 1.966 0.049440 *
## Rep_1:index_AFexp 0.779364 0.199567 3.905 9.62e-05 ***
## Rep_1:index_ANexp -0.031730 0.009577 -3.313 0.000933 ***
## Ind_1:index_AFexp 0.638470 0.253765 2.516 0.011921 *
## Ind_1:index_ANexp -0.028480 0.012293 -2.317 0.020580 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3033 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09335, Adjusted R-squared: 0.09096
## F-statistic: 39.04 on 8 and 3033 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp + index_ANexp), data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp +
## index_ANexp), data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7628 -1.5860 0.2761 1.7980 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.540328 0.097703 -5.530 3.47e-08 ***
## Dem_1 1.409247 0.146485 9.620 < 2e-16 ***
## Ind_1 0.127674 0.162724 0.785 0.432748
## index_AFexp 0.603632 0.159100 3.794 0.000151 ***
## index_ANexp -0.020427 0.007658 -2.667 0.007684 **
## Dem_1:index_AFexp -0.779364 0.199567 -3.905 9.62e-05 ***
## Dem_1:index_ANexp 0.031730 0.009577 3.313 0.000933 ***
## Ind_1:index_AFexp -0.140894 0.274218 -0.514 0.607427
## Ind_1:index_ANexp 0.003251 0.013292 0.245 0.806817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3033 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09335, Adjusted R-squared: 0.09096
## F-statistic: 39.04 on 8 and 3033 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp + index_ANexp), data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp +
## index_ANexp), data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7628 -1.5860 0.2761 1.7980 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.412654 0.130128 -3.171 0.00153 **
## Rep_1 -0.127674 0.162724 -0.785 0.43275
## Dem_1 1.281573 0.169839 7.546 5.91e-14 ***
## index_AFexp 0.462738 0.223345 2.072 0.03836 *
## index_ANexp -0.017176 0.010864 -1.581 0.11399
## Rep_1:index_AFexp 0.140894 0.274218 0.514 0.60743
## Rep_1:index_ANexp -0.003251 0.013292 -0.245 0.80682
## Dem_1:index_AFexp -0.638470 0.253765 -2.516 0.01192 *
## Dem_1:index_ANexp 0.028480 0.012293 2.317 0.02058 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.058 on 3033 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.09335, Adjusted R-squared: 0.09096
## F-statistic: 39.04 on 8 and 3033 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_ANexp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_ANexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7254 -1.6460 0.2202 1.8104 3.3857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0305811 0.0614206 0.498 0.618593
## DvR -1.1966381 0.1353612 -8.840 < 2e-16 ***
## IvDR 0.5515359 0.1426194 3.867 0.000112 ***
## index_ANexp 0.0055330 0.0005703 9.702 < 2e-16 ***
## DvR:index_ANexp 0.0054088 0.0012363 4.375 1.25e-05 ***
## IvDR:index_ANexp 0.0004985 0.0013394 0.372 0.709800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08713, Adjusted R-squared: 0.08563
## F-statistic: 57.95 on 5 and 3036 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, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7254 -1.6460 0.2202 1.8104 3.3857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8109070 0.1019356 7.955 2.50e-15 ***
## Rep_1 -1.1966381 0.1353612 -8.840 < 2e-16 ***
## Ind_1 -1.1498550 0.1617112 -7.111 1.44e-12 ***
## index_ANexp 0.0029931 0.0007851 3.812 0.00014 ***
## Rep_1:index_ANexp 0.0054088 0.0012363 4.375 1.25e-05 ***
## Ind_1:index_ANexp 0.0022060 0.0014242 1.549 0.12150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08713, Adjusted R-squared: 0.08563
## F-statistic: 57.95 on 5 and 3036 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7254 -1.6460 0.2202 1.8104 3.3857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3857311 0.0890606 -4.331 1.53e-05 ***
## Dem_1 1.1966381 0.1353612 8.840 < 2e-16 ***
## Ind_1 0.0467831 0.1539202 0.304 0.7612
## index_ANexp 0.0084019 0.0009549 8.798 < 2e-16 ***
## Dem_1:index_ANexp -0.0054088 0.0012363 -4.375 1.25e-05 ***
## Ind_1:index_ANexp -0.0032029 0.0015244 -2.101 0.0357 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08713, Adjusted R-squared: 0.08563
## F-statistic: 57.95 on 5 and 3036 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7254 -1.6460 0.2202 1.8104 3.3857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.338948 0.125537 -2.700 0.00697 **
## Rep_1 -0.046783 0.153920 -0.304 0.76119
## Dem_1 1.149855 0.161711 7.111 1.44e-12 ***
## index_ANexp 0.005199 0.001188 4.376 1.25e-05 ***
## Rep_1:index_ANexp 0.003203 0.001524 2.101 0.03572 *
## Dem_1:index_ANexp -0.002206 0.001424 -1.549 0.12150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08713, Adjusted R-squared: 0.08563
## F-statistic: 57.95 on 5 and 3036 DF, p-value: < 2.2e-163. vaxxAttitudes ~ AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_AFexp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6923 -1.6134 0.2362 1.8013 3.4534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002097 0.063167 -0.033 0.973517
## DvR -1.267583 0.140315 -9.034 < 2e-16 ***
## IvDR 0.553138 0.145852 3.792 0.000152 ***
## index_AFexp 0.117744 0.011797 9.981 < 2e-16 ***
## DvR:index_AFexp 0.123673 0.025740 4.805 1.63e-06 ***
## IvDR:index_AFexp 0.008955 0.027584 0.325 0.745465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08932, Adjusted R-squared: 0.08782
## F-statistic: 59.56 on 5 and 3036 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, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6923 -1.6134 0.2362 1.8013 3.4534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81423 0.10572 7.702 1.80e-14 ***
## Rep_1 -1.26758 0.14031 -9.034 < 2e-16 ***
## Ind_1 -1.18693 0.16591 -7.154 1.05e-12 ***
## index_AFexp 0.05886 0.01643 3.583 0.000345 ***
## Rep_1:index_AFexp 0.12367 0.02574 4.805 1.63e-06 ***
## Ind_1:index_AFexp 0.05288 0.02941 1.798 0.072289 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08932, Adjusted R-squared: 0.08782
## F-statistic: 59.56 on 5 and 3036 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6923 -1.6134 0.2362 1.8013 3.4534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.45335 0.09226 -4.914 9.40e-07 ***
## Dem_1 1.26758 0.14031 9.034 < 2e-16 ***
## Ind_1 0.08065 0.15768 0.512 0.6090
## index_AFexp 0.18254 0.01982 9.211 < 2e-16 ***
## Dem_1:index_AFexp -0.12367 0.02574 -4.805 1.63e-06 ***
## Ind_1:index_AFexp -0.07079 0.03143 -2.252 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08932, Adjusted R-squared: 0.08782
## F-statistic: 59.56 on 5 and 3036 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6923 -1.6134 0.2362 1.8013 3.4534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.37270 0.12787 -2.915 0.00359 **
## Rep_1 -0.08065 0.15768 -0.512 0.60903
## Dem_1 1.18693 0.16591 7.154 1.05e-12 ***
## index_AFexp 0.11174 0.02440 4.580 4.83e-06 ***
## Rep_1:index_AFexp 0.07079 0.03143 2.252 0.02437 *
## Dem_1:index_AFexp -0.05288 0.02941 -1.798 0.07229 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3036 degrees of freedom
## (269 observations deleted due to missingness)
## Multiple R-squared: 0.08932, Adjusted R-squared: 0.08782
## F-statistic: 59.56 on 5 and 3036 DF, p-value: < 2.2e-164. ANALYTIC ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_ANexp ~ (DvR + IvDR) * index_AFexp, data = d1)
summary(model1.cc)##
## Call:
## lm(formula = index_ANexp ~ (DvR + IvDR) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.758 -5.611 1.603 5.390 34.878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.80961 0.27020 -14.099 < 2e-16 ***
## DvR 0.59646 0.59998 0.994 0.320234
## IvDR -2.21409 0.62405 -3.548 0.000394 ***
## index_AFexp 20.60045 0.05047 408.182 < 2e-16 ***
## DvR:index_AFexp -0.13940 0.11008 -1.266 0.205491
## IvDR:index_AFexp 0.24723 0.11803 2.095 0.036282 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.822 on 3039 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.985, Adjusted R-squared: 0.985
## F-statistic: 3.993e+04 on 5 and 3039 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, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp, data = d1)
summary(model1.dem)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.758 -5.611 1.603 5.390 34.878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.83849 0.45242 -10.695 < 2e-16 ***
## Rep_1 0.59646 0.59998 0.994 0.320234
## Ind_1 2.51232 0.71002 3.538 0.000409 ***
## index_AFexp 20.75173 0.07029 295.218 < 2e-16 ***
## Rep_1:index_AFexp -0.13940 0.11008 -1.266 0.205491
## Ind_1:index_AFexp -0.31693 0.12586 -2.518 0.011852 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.822 on 3039 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.985, Adjusted R-squared: 0.985
## F-statistic: 3.993e+04 on 5 and 3039 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp, data = d1)
summary(model1.rep)##
## Call:
## lm(formula = index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.758 -5.611 1.603 5.390 34.878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.24203 0.39408 -10.765 < 2e-16 ***
## Dem_1 -0.59646 0.59998 -0.994 0.32023
## Ind_1 1.91586 0.67435 2.841 0.00453 **
## index_AFexp 20.61233 0.08472 243.301 < 2e-16 ***
## Dem_1:index_AFexp 0.13940 0.11008 1.266 0.20549
## Ind_1:index_AFexp -0.17753 0.13446 -1.320 0.18682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.822 on 3039 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.985, Adjusted R-squared: 0.985
## F-statistic: 3.993e+04 on 5 and 3039 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp, data = d1)
summary(model1.ind)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp, data = d1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.758 -5.611 1.603 5.390 34.878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.3262 0.5472 -4.251 2.19e-05 ***
## Rep_1 -1.9159 0.6743 -2.841 0.004526 **
## Dem_1 -2.5123 0.7100 -3.538 0.000409 ***
## index_AFexp 20.4348 0.1044 195.723 < 2e-16 ***
## Rep_1:index_AFexp 0.1775 0.1345 1.320 0.186817
## Dem_1:index_AFexp 0.3169 0.1259 2.518 0.011852 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.822 on 3039 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.985, Adjusted R-squared: 0.985
## F-statistic: 3.993e+04 on 5 and 3039 DF, p-value: < 2.2e-16III. WAVE 2
1. vaxxAttitudes ~ (AFFECT + ANALYTIC) * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp), data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * (index_AFexp + index_ANexp),
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5114 -1.5385 0.1135 1.7013 3.5653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.177235 0.071958 -2.463 0.01385 *
## DvR -0.663006 0.156158 -4.246 2.26e-05 ***
## IvDR 0.478005 0.168860 2.831 0.00468 **
## index_AFexp 0.124070 0.135985 0.912 0.36166
## index_ANexp -0.000544 0.006508 -0.084 0.93338
## DvR:index_AFexp 0.807628 0.248682 3.248 0.00118 **
## DvR:index_ANexp -0.036590 0.011814 -3.097 0.00198 **
## IvDR:index_AFexp 0.082734 0.348621 0.237 0.81243
## IvDR:index_ANexp -0.004936 0.016732 -0.295 0.76799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2397 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05401, Adjusted R-squared: 0.05086
## F-statistic: 17.11 on 8 and 2397 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes ~ party_factor * (index_AFexp + index_ANexp), data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp + index_ANexp), data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * (index_AFexp +
## index_ANexp), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5114 -1.5385 0.1135 1.7013 3.5653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.312010 0.119117 2.619 0.00887 **
## Rep_1 -0.663006 0.156158 -4.246 2.26e-05 ***
## Ind_1 -0.809508 0.191328 -4.231 2.41e-05 ***
## index_AFexp -0.252441 0.155295 -1.626 0.10417
## index_ANexp 0.016122 0.007349 2.194 0.02834 *
## Rep_1:index_AFexp 0.807628 0.248682 3.248 0.00118 **
## Rep_1:index_ANexp -0.036590 0.011814 -3.097 0.00198 **
## Ind_1:index_AFexp 0.321080 0.360821 0.890 0.37363
## Ind_1:index_ANexp -0.013358 0.017294 -0.772 0.43993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2397 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05401, Adjusted R-squared: 0.05086
## F-statistic: 17.11 on 8 and 2397 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp + index_ANexp), data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * (index_AFexp +
## index_ANexp), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5114 -1.5385 0.1135 1.7013 3.5653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.35100 0.10098 -3.476 0.000518 ***
## Dem_1 0.66301 0.15616 4.246 2.26e-05 ***
## Ind_1 -0.14650 0.18059 -0.811 0.417317
## index_AFexp 0.55519 0.19423 2.858 0.004295 **
## index_ANexp -0.02047 0.00925 -2.213 0.027005 *
## Dem_1:index_AFexp -0.80763 0.24868 -3.248 0.001180 **
## Dem_1:index_ANexp 0.03659 0.01181 3.097 0.001976 **
## Ind_1:index_AFexp -0.48655 0.37921 -1.283 0.199599
## Ind_1:index_ANexp 0.02323 0.01818 1.278 0.201501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2397 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05401, Adjusted R-squared: 0.05086
## F-statistic: 17.11 on 8 and 2397 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp + index_ANexp), data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * (index_AFexp +
## index_ANexp), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5114 -1.5385 0.1135 1.7013 3.5653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.497498 0.149725 -3.323 0.000905 ***
## Rep_1 0.146502 0.180593 0.811 0.417317
## Dem_1 0.809508 0.191328 4.231 2.41e-05 ***
## index_AFexp 0.068638 0.325693 0.211 0.833104
## index_ANexp 0.002763 0.015655 0.177 0.859897
## Rep_1:index_AFexp 0.486548 0.379212 1.283 0.199599
## Rep_1:index_ANexp -0.023231 0.018183 -1.278 0.201501
## Dem_1:index_AFexp -0.321080 0.360821 -0.890 0.373632
## Dem_1:index_ANexp 0.013358 0.017294 0.772 0.439927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 2397 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05401, Adjusted R-squared: 0.05086
## F-statistic: 17.11 on 8 and 2397 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_ANexp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.610 -1.575 0.125 1.758 3.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.160534 0.067676 -2.372 0.017766 *
## DvR -0.476138 0.144390 -3.298 0.000989 ***
## IvDR 0.489176 0.160574 3.046 0.002341 **
## index_ANexp 0.005360 0.000701 7.647 2.95e-14 ***
## DvR:index_ANexp 0.001517 0.001422 1.067 0.286228
## IvDR:index_ANexp -0.001024 0.001713 -0.598 0.550138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.04973, Adjusted R-squared: 0.04775
## F-statistic: 25.12 on 5 and 2400 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_ANexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.610 -1.575 0.125 1.758 3.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2389636 0.1104970 2.163 0.030668 *
## Rep_1 -0.4761380 0.1443901 -3.298 0.000989 ***
## Ind_1 -0.7272455 0.1810568 -4.017 6.08e-05 ***
## index_ANexp 0.0042642 0.0008921 4.780 1.86e-06 ***
## Rep_1:index_ANexp 0.0015173 0.0014224 1.067 0.286228
## Ind_1:index_ANexp 0.0017821 0.0017952 0.993 0.320961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.04973, Adjusted R-squared: 0.04775
## F-statistic: 25.12 on 5 and 2400 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.610 -1.575 0.125 1.758 3.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2371744 0.0929458 -2.552 0.010780 *
## Dem_1 0.4761380 0.1443901 3.298 0.000989 ***
## Ind_1 -0.2511074 0.1709120 -1.469 0.141904
## index_ANexp 0.0057814 0.0011079 5.219 1.96e-07 ***
## Dem_1:index_ANexp -0.0015173 0.0014224 -1.067 0.286228
## Ind_1:index_ANexp 0.0002648 0.0019116 0.139 0.889828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.04973, Adjusted R-squared: 0.04775
## F-statistic: 25.12 on 5 and 2400 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_ANexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.610 -1.575 0.125 1.758 3.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4882818 0.1434294 -3.404 0.000674 ***
## Rep_1 0.2511074 0.1709120 1.469 0.141904
## Dem_1 0.7272455 0.1810568 4.017 6.08e-05 ***
## index_ANexp 0.0060462 0.0015578 3.881 0.000107 ***
## Rep_1:index_ANexp -0.0002648 0.0019116 -0.139 0.889828
## Dem_1:index_ANexp -0.0017821 0.0017952 -0.993 0.320961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.04973, Adjusted R-squared: 0.04775
## F-statistic: 25.12 on 5 and 2400 DF, p-value: < 2.2e-163. vaxxAttitudes ~ AFFECT * party
a. contrast model
model1.cc <- lm(vaxxAttitudes ~ (DvR + IvDR) * index_AFexp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = vaxxAttitudes ~ (DvR + IvDR) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6618 -1.5849 0.1269 1.7613 3.5034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.18015 0.06947 -2.593 0.009571 **
## DvR -0.51862 0.14933 -3.473 0.000524 ***
## IvDR 0.48254 0.16406 2.941 0.003301 **
## index_AFexp 0.11329 0.01466 7.728 1.6e-14 ***
## DvR:index_AFexp 0.04273 0.02994 1.427 0.153610
## IvDR:index_AFexp -0.01874 0.03569 -0.525 0.599567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05017, Adjusted R-squared: 0.04819
## F-statistic: 25.35 on 5 and 2400 DF, p-value: < 2.2e-16plot
m1 <- lm(vaxxAttitudes ~ party_factor * index_AFexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Ind_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6618 -1.5849 0.1269 1.7613 3.5034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23840 0.11445 2.083 0.037365 *
## Rep_1 -0.51862 0.14933 -3.473 0.000524 ***
## Ind_1 -0.74185 0.18558 -3.997 6.60e-05 ***
## index_AFexp 0.08574 0.01885 4.549 5.66e-06 ***
## Rep_1:index_AFexp 0.04273 0.02994 1.427 0.153610
## Ind_1:index_AFexp 0.04011 0.03749 1.070 0.284761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05017, Adjusted R-squared: 0.04819
## F-statistic: 25.35 on 5 and 2400 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = vaxxAttitudes ~ (Dem_1 + Ind_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6618 -1.5849 0.1269 1.7613 3.5034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.280221 0.095912 -2.922 0.003514 **
## Dem_1 0.518620 0.149329 3.473 0.000524 ***
## Ind_1 -0.223225 0.174760 -1.277 0.201610
## index_AFexp 0.128469 0.023258 5.524 3.68e-08 ***
## Dem_1:index_AFexp -0.042731 0.029937 -1.427 0.153610
## Ind_1:index_AFexp -0.002623 0.039886 -0.066 0.947573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05017, Adjusted R-squared: 0.04819
## F-statistic: 25.35 on 5 and 2400 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = vaxxAttitudes ~ (Rep_1 + Dem_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6618 -1.5849 0.1269 1.7613 3.5034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.503447 0.146088 -3.446 0.000578 ***
## Rep_1 0.223225 0.174760 1.277 0.201610
## Dem_1 0.741846 0.185584 3.997 6.6e-05 ***
## index_AFexp 0.125846 0.032403 3.884 0.000106 ***
## Rep_1:index_AFexp 0.002623 0.039886 0.066 0.947573
## Dem_1:index_AFexp -0.040108 0.037486 -1.070 0.284761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.06 on 2400 degrees of freedom
## (57 observations deleted due to missingness)
## Multiple R-squared: 0.05017, Adjusted R-squared: 0.04819
## F-statistic: 25.35 on 5 and 2400 DF, p-value: < 2.2e-164. ANALYTIC ~ AFFECT * party
a. contrast model
model1.cc <- lm(index_ANexp ~ (DvR + IvDR) * index_AFexp, data = d2)
summary(model1.cc)##
## Call:
## lm(formula = index_ANexp ~ (DvR + IvDR) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.116 -4.782 1.023 4.519 31.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.38435 0.26241 -12.897 < 2e-16 ***
## DvR 1.05638 0.56383 1.874 0.06111 .
## IvDR -1.83851 0.61984 -2.966 0.00305 **
## index_AFexp 20.84125 0.05539 376.277 < 2e-16 ***
## DvR:index_AFexp -0.12248 0.11309 -1.083 0.27888
## IvDR:index_AFexp 0.20799 0.13487 1.542 0.12316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.783 on 2402 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9875
## F-statistic: 3.807e+04 on 5 and 2402 DF, p-value: < 2.2e-16plot
m1 <- lm(index_ANexp ~ party_factor * index_AFexp, data = d2)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("media analytic thinking") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lm(index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp, data = d2)
summary(model1.dem)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Ind_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.116 -4.782 1.023 4.519 31.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.5193 0.4321 -10.458 < 2e-16 ***
## Rep_1 1.0564 0.5638 1.874 0.061109 .
## Ind_1 2.3667 0.7010 3.376 0.000747 ***
## index_AFexp 20.9711 0.0712 294.558 < 2e-16 ***
## Rep_1:index_AFexp -0.1225 0.1131 -1.083 0.278881
## Ind_1:index_AFexp -0.2692 0.1416 -1.901 0.057438 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.783 on 2402 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9875
## F-statistic: 3.807e+04 on 5 and 2402 DF, p-value: < 2.2e-16c. simple effects Rep
model1.rep <- lm(index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp, data = d2)
summary(model1.rep)##
## Call:
## lm(formula = index_ANexp ~ (Dem_1 + Ind_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.116 -4.782 1.023 4.519 31.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.46287 0.36215 -9.562 <2e-16 ***
## Dem_1 -1.05638 0.56383 -1.874 0.0611 .
## Ind_1 1.31032 0.66021 1.985 0.0473 *
## index_AFexp 20.84864 0.08787 237.279 <2e-16 ***
## Dem_1:index_AFexp 0.12248 0.11309 1.083 0.2789
## Ind_1:index_AFexp -0.14674 0.15070 -0.974 0.3303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.783 on 2402 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9875
## F-statistic: 3.807e+04 on 5 and 2402 DF, p-value: < 2.2e-16d. simple effects Indep
model1.ind <- lm(index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp, data = d2)
summary(model1.ind)##
## Call:
## lm(formula = index_ANexp ~ (Rep_1 + Dem_1) * index_AFexp, data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.116 -4.782 1.023 4.519 31.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1525 0.5520 -3.899 9.91e-05 ***
## Rep_1 -1.3103 0.6602 -1.985 0.047291 *
## Dem_1 -2.3667 0.7010 -3.376 0.000747 ***
## index_AFexp 20.7019 0.1224 169.078 < 2e-16 ***
## Rep_1:index_AFexp 0.1467 0.1507 0.974 0.330292
## Dem_1:index_AFexp 0.2692 0.1416 1.901 0.057438 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.783 on 2402 degrees of freedom
## (55 observations deleted due to missingness)
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9875
## F-statistic: 3.807e+04 on 5 and 2402 DF, p-value: < 2.2e-16IV. LONGITUDINAL MODELS
1. vaxxAttitudes.w2 ~ party * (ANALYTIC.w1 + ANALYTIC.w2) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.775e-01 5.269e-02 3.368 0.00077 ***
## index_ANexp.w1 -8.643e-04 6.609e-04 -1.308 0.19111
## index_ANexp.w2 2.003e-03 6.868e-04 2.917 0.00357 **
## DvR 2.968e-01 1.141e-01 2.602 0.00934 **
## IvDR 2.567e-01 1.228e-01 2.090 0.03671 *
## vaxxAttitudes.c.w1 7.204e-01 1.489e-02 48.398 < 2e-16 ***
## index_ANexp.w1:DvR -1.973e-03 1.458e-03 -1.354 0.17598
## index_ANexp.w1:IvDR -1.061e-03 1.524e-03 -0.696 0.48654
## index_ANexp.w2:DvR 1.031e-03 1.549e-03 0.666 0.50560
## index_ANexp.w2:IvDR 6.892e-05 1.565e-03 0.044 0.96489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model2.dem <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1137476 0.0871526 1.305 0.19198
## index_ANexp.w1 -0.0002277 0.0008545 -0.266 0.78992
## index_ANexp.w2 0.0015106 0.0008737 1.729 0.08394 .
## Ind_1 -0.1082906 0.1403182 -0.772 0.44035
## Rep_1 0.2968434 0.1141037 2.602 0.00934 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Ind_1 0.0000740 0.0015892 0.047 0.96287
## index_ANexp.w1:Rep_1 -0.0019734 0.0014578 -1.354 0.17598
## index_ANexp.w2:Ind_1 0.0004467 0.0016162 0.276 0.78229
## index_ANexp.w2:Rep_1 0.0010312 0.0015487 0.666 0.50560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16c. simple effects for Rep
model2.rep <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4105909 0.0730136 5.623 2.12e-08 ***
## index_ANexp.w1 -0.0022010 0.0011827 -1.861 0.06287 .
## index_ANexp.w2 0.0025418 0.0012792 1.987 0.04704 *
## Ind_1 -0.4051340 0.1303350 -3.108 0.00191 **
## Dem_1 -0.2968434 0.1141037 -2.602 0.00934 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Ind_1 0.0020474 0.0017840 1.148 0.25125
## index_ANexp.w1:Dem_1 0.0019734 0.0014578 1.354 0.17598
## index_ANexp.w2:Ind_1 -0.0005845 0.0018677 -0.313 0.75434
## index_ANexp.w2:Dem_1 -0.0010312 0.0015487 -0.666 0.50560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-16d. simple effects for Indep
model2.ind <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_ANexp.w2) *
## (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0993 -0.8189 0.0449 0.9365 5.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0054570 0.1095614 0.050 0.96028
## index_ANexp.w1 -0.0001537 0.0013408 -0.115 0.90876
## index_ANexp.w2 0.0019573 0.0013612 1.438 0.15059
## Dem_1 0.1082906 0.1403182 0.772 0.44035
## Rep_1 0.4051340 0.1303350 3.108 0.00191 **
## vaxxAttitudes.c.w1 0.7204229 0.0148853 48.398 < 2e-16 ***
## index_ANexp.w1:Dem_1 -0.0000740 0.0015892 -0.047 0.96287
## index_ANexp.w1:Rep_1 -0.0020474 0.0017840 -1.148 0.25125
## index_ANexp.w2:Dem_1 -0.0004467 0.0016162 -0.276 0.78229
## index_ANexp.w2:Rep_1 0.0005845 0.0018677 0.313 0.75434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5477, Adjusted R-squared: 0.5458
## F-statistic: 289 on 9 and 2148 DF, p-value: < 2.2e-162. vaxxAttitudes.w2 ~ party * (AFFECT.w1 + AFFECT.w2) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1794202 0.0543672 3.300 0.000982 ***
## index_AFexp.w1 -0.0154658 0.0137346 -1.126 0.260272
## index_AFexp.w2 0.0384976 0.0144351 2.667 0.007712 **
## DvR 0.2768726 0.1185634 2.335 0.019623 *
## IvDR 0.2720531 0.1259446 2.160 0.030875 *
## vaxxAttitudes.c.w1 0.7204464 0.0149108 48.317 < 2e-16 ***
## index_AFexp.w1:DvR -0.0383700 0.0304108 -1.262 0.207185
## index_AFexp.w1:IvDR -0.0222708 0.0315830 -0.705 0.480792
## index_AFexp.w2:DvR 0.0243595 0.0326008 0.747 0.455020
## index_AFexp.w2:IvDR -0.0009634 0.0328450 -0.029 0.976602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * party_factor + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model2.dem <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.130761 0.090357 1.447 0.1480
## index_AFexp.w1 -0.003630 0.018210 -0.199 0.8420
## index_AFexp.w2 0.026000 0.018740 1.387 0.1655
## Ind_1 -0.133617 0.144220 -0.926 0.3543
## Rep_1 0.276873 0.118563 2.335 0.0196 *
## vaxxAttitudes.c.w1 0.720446 0.014911 48.317 <2e-16 ***
## index_AFexp.w1:Ind_1 0.003086 0.033165 0.093 0.9259
## index_AFexp.w1:Rep_1 -0.038370 0.030411 -1.262 0.2072
## index_AFexp.w2:Ind_1 0.013143 0.034108 0.385 0.7000
## index_AFexp.w2:Rep_1 0.024360 0.032601 0.747 0.4550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16c. simple effects for Rep
model2.rep <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40763 0.07614 5.354 9.53e-08 ***
## index_AFexp.w1 -0.04200 0.02438 -1.722 0.08514 .
## index_AFexp.w2 0.05036 0.02669 1.887 0.05935 .
## Ind_1 -0.41049 0.13399 -3.064 0.00221 **
## Dem_1 -0.27687 0.11856 -2.335 0.01962 *
## vaxxAttitudes.c.w1 0.72045 0.01491 48.317 < 2e-16 ***
## index_AFexp.w1:Ind_1 0.04146 0.03684 1.125 0.26064
## index_AFexp.w1:Dem_1 0.03837 0.03041 1.262 0.20719
## index_AFexp.w2:Ind_1 -0.01122 0.03906 -0.287 0.77402
## index_AFexp.w2:Dem_1 -0.02436 0.03260 -0.747 0.45502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-16d. simple effects for Indep
model2.ind <- lm(vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model2.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_AFexp.w1 + index_AFexp.w2) *
## (Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0617 -0.8126 0.0416 0.9384 5.4207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0028554 0.1120111 -0.025 0.97966
## index_AFexp.w1 -0.0005444 0.0277329 -0.020 0.98434
## index_AFexp.w2 0.0391431 0.0285284 1.372 0.17018
## Dem_1 0.1336168 0.1442204 0.926 0.35430
## Rep_1 0.4104894 0.1339896 3.064 0.00221 **
## vaxxAttitudes.c.w1 0.7204464 0.0149108 48.317 < 2e-16 ***
## index_AFexp.w1:Dem_1 -0.0030858 0.0331647 -0.093 0.92588
## index_AFexp.w1:Rep_1 -0.0414558 0.0368440 -1.125 0.26064
## index_AFexp.w2:Dem_1 -0.0131432 0.0341080 -0.385 0.70002
## index_AFexp.w2:Rep_1 0.0112163 0.0390594 0.287 0.77402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 2148 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5473, Adjusted R-squared: 0.5454
## F-statistic: 288.5 on 9 and 2148 DF, p-value: < 2.2e-163. vaxxAttitudes.w2 ~ party * (ANALYTIC.w2 + ANALYTIC.w1 + AFFECT.w2 + AFFECT.w1) + vaxxAttitudes.w1
a. contrast coded model
model3.cc <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(DvR + IvDR) + vaxxAttitudes.c.w1, data = dw)
summary(model3.cc)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (DvR + IvDR) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.188275 0.056482 3.333 0.000873 ***
## index_ANexp.w1 -0.007988 0.005118 -1.561 0.118770
## index_AFexp.w1 0.150442 0.106175 1.417 0.156649
## index_ANexp.w2 0.011498 0.005679 2.025 0.043032 *
## index_AFexp.w2 -0.201747 0.119350 -1.690 0.091101 .
## DvR 0.198983 0.124391 1.600 0.109823
## IvDR 0.307677 0.129918 2.368 0.017961 *
## vaxxAttitudes.c.w1 0.719638 0.014939 48.172 < 2e-16 ***
## index_ANexp.w1:DvR -0.006047 0.010218 -0.592 0.554002
## index_ANexp.w1:IvDR 0.004676 0.012619 0.371 0.711015
## index_AFexp.w1:DvR 0.078136 0.212917 0.367 0.713673
## index_AFexp.w1:IvDR -0.115727 0.261061 -0.443 0.657597
## index_ANexp.w2:DvR -0.012351 0.011664 -1.059 0.289750
## index_ANexp.w2:IvDR 0.005426 0.013797 0.393 0.694178
## index_AFexp.w2:DvR 0.290564 0.245520 1.183 0.236756
## index_AFexp.w2:IvDR -0.117359 0.289591 -0.405 0.685329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16- plots
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_ANexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
plot_model(m1, type = "pred", terms = c("index_ANexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w2", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 2") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
index_ANexp.w2 + index_AFexp.w2) *
(party_factor) + vaxxAttitudes.c.w1, data = dw)
plot_model(m1, type = "pred", terms = c("index_AFexp.w1", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect wave 1") +
ylab("willingness to obtain the Covid-19 vaccine wave 2") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects for Dem
model3.dem <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Ind_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.dem)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Ind_1 + Rep_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.190317 0.093459 2.036 0.04184 *
## index_ANexp.w1 -0.003421 0.005825 -0.587 0.55708
## index_AFexp.w1 0.073184 0.124092 0.590 0.55542
## index_ANexp.w2 0.019464 0.006693 2.908 0.00367 **
## index_AFexp.w2 -0.385757 0.143278 -2.692 0.00715 **
## Ind_1 -0.208186 0.148609 -1.401 0.16139
## Rep_1 0.198983 0.124391 1.600 0.10982
## vaxxAttitudes.c.w1 0.719638 0.014939 48.172 < 2e-16 ***
## index_ANexp.w1:Ind_1 -0.007699 0.012925 -0.596 0.55145
## index_ANexp.w1:Rep_1 -0.006047 0.010218 -0.592 0.55400
## index_AFexp.w1:Ind_1 0.154795 0.268775 0.576 0.56473
## index_AFexp.w1:Rep_1 0.078136 0.212917 0.367 0.71367
## index_ANexp.w2:Ind_1 -0.011601 0.014179 -0.818 0.41332
## index_ANexp.w2:Rep_1 -0.012351 0.011664 -1.059 0.28975
## index_AFexp.w2:Ind_1 0.262641 0.298782 0.879 0.37948
## index_AFexp.w2:Rep_1 0.290564 0.245520 1.183 0.23676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16c. simple effects for Rep
model3.rep <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Ind_1 + Dem_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.rep)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Ind_1 + Dem_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3893005 0.0813476 4.786 1.82e-06 ***
## index_ANexp.w1 -0.0094684 0.0083951 -1.128 0.25951
## index_AFexp.w1 0.1513201 0.1730095 0.875 0.38187
## index_ANexp.w2 0.0071131 0.0095518 0.745 0.45654
## index_AFexp.w2 -0.0951931 0.1994256 -0.477 0.63317
## Ind_1 -0.4071687 0.1393172 -2.923 0.00351 **
## Dem_1 -0.1989831 0.1243914 -1.600 0.10982
## vaxxAttitudes.c.w1 0.7196384 0.0149388 48.172 < 2e-16 ***
## index_ANexp.w1:Ind_1 -0.0016520 0.0142687 -0.116 0.90784
## index_ANexp.w1:Dem_1 0.0060474 0.0102175 0.592 0.55400
## index_AFexp.w1:Ind_1 0.0766592 0.2945035 0.260 0.79466
## index_AFexp.w1:Dem_1 -0.0781357 0.2129174 -0.367 0.71367
## index_ANexp.w2:Ind_1 0.0007499 0.0157393 0.048 0.96200
## index_ANexp.w2:Dem_1 0.0123514 0.0116640 1.059 0.28975
## index_AFexp.w2:Ind_1 -0.0279231 0.3295373 -0.085 0.93248
## index_AFexp.w2:Dem_1 -0.2905640 0.2455195 -1.183 0.23676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16d. simple effects for Indep
model3.ind <- lm(vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 + index_ANexp.w2 + index_AFexp.w2) *
(Dem_1 + Rep_1) + vaxxAttitudes.c.w1, data = dw)
summary(model3.ind)##
## Call:
## lm(formula = vaxxAttitudes.w2 ~ (index_ANexp.w1 + index_AFexp.w1 +
## index_ANexp.w2 + index_AFexp.w2) * (Dem_1 + Rep_1) + vaxxAttitudes.c.w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2807 -0.8117 0.0350 0.9206 5.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0178683 0.1150875 -0.155 0.87663
## index_ANexp.w1 -0.0111204 0.0115356 -0.964 0.33515
## index_AFexp.w1 0.2279793 0.2383603 0.956 0.33895
## index_ANexp.w2 0.0078630 0.0124998 0.629 0.52938
## index_AFexp.w2 -0.1231163 0.2621772 -0.470 0.63869
## Dem_1 0.2081856 0.1486093 1.401 0.16139
## Rep_1 0.4071687 0.1393172 2.923 0.00351 **
## vaxxAttitudes.c.w1 0.7196384 0.0149388 48.172 < 2e-16 ***
## index_ANexp.w1:Dem_1 0.0076995 0.0129253 0.596 0.55145
## index_ANexp.w1:Rep_1 0.0016520 0.0142687 0.116 0.90784
## index_AFexp.w1:Dem_1 -0.1547950 0.2687755 -0.576 0.56473
## index_AFexp.w1:Rep_1 -0.0766592 0.2945035 -0.260 0.79466
## index_ANexp.w2:Dem_1 0.0116015 0.0141789 0.818 0.41332
## index_ANexp.w2:Rep_1 -0.0007499 0.0157393 -0.048 0.96200
## index_AFexp.w2:Dem_1 -0.2626409 0.2987825 -0.879 0.37948
## index_AFexp.w2:Rep_1 0.0279231 0.3295373 0.085 0.93248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 2142 degrees of freedom
## (1259 observations deleted due to missingness)
## Multiple R-squared: 0.5499, Adjusted R-squared: 0.5468
## F-statistic: 174.5 on 15 and 2142 DF, p-value: < 2.2e-16V. long by W1 + W2 models
1. vaxxAttitudes ~ W1vW2 + ANALYTIC * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) *
(index_ANexp) +
(1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * (index_ANexp) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.121e-03 4.934e-02 4.721e+03 0.185 0.853332
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## DvR -8.157e-01 1.037e-01 5.201e+03 -7.865 4.47e-15 ***
## IvDR 3.912e-01 1.033e-01 5.413e+03 3.785 0.000155 ***
## index_ANexp 4.244e-03 4.267e-04 5.410e+03 9.947 < 2e-16 ***
## DvR:index_ANexp 3.772e-03 8.779e-04 5.281e+03 4.297 1.76e-05 ***
## IvDR:index_ANexp 9.206e-04 9.491e-04 5.060e+03 0.970 0.332096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AN DR:_AN
## W1vW2 0.006
## DvR -0.045 -0.017
## IvDR -0.237 -0.024 -0.059
## index_ANexp -0.667 0.091 -0.009 0.168
## DvR:ndx_ANx 0.011 0.031 -0.713 0.018 0.159
## IvDR:ndx_AN 0.159 -0.007 0.022 -0.688 -0.278 0.092- plot
m1 <- lmer(vaxxAttitudes ~ W1vW2 + index_ANexp * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Rep_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Rep_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.461e-01 7.698e-02 5.301e+03 7.093 1.48e-12 ***
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 2.662e-03 5.600e-04 5.303e+03 4.754 2.05e-06 ***
## Rep_1 -8.157e-01 1.037e-01 5.201e+03 -7.865 4.47e-15 ***
## Ind_1 -7.991e-01 1.183e-01 5.439e+03 -6.753 1.60e-11 ***
## index_ANexp:Rep_1 3.772e-03 8.779e-04 5.281e+03 4.297 1.76e-05 ***
## index_ANexp:Ind_1 9.654e-04 1.008e-03 5.087e+03 0.957 0.338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Rep_1 Ind_1 i_AN:R
## W1vW2 0.004
## index_ANexp -0.774 0.041
## Rep_1 -0.729 -0.017 0.564
## Ind_1 -0.608 0.014 0.484 0.490
## indx_AN:R_1 0.495 0.031 -0.611 -0.713 -0.328
## indx_AN:I_1 0.420 0.020 -0.525 -0.331 -0.717 0.349c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Dem_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Dem_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.696e-01 7.106e-02 4.697e+03 -3.795 0.00015 ***
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 6.434e-03 6.954e-04 5.389e+03 9.253 < 2e-16 ***
## Dem_1 8.157e-01 1.037e-01 5.201e+03 7.865 4.47e-15 ***
## Ind_1 1.664e-02 1.129e-01 5.441e+03 0.147 0.88278
## index_ANexp:Dem_1 -3.772e-03 8.779e-04 5.281e+03 -4.297 1.76e-05 ***
## index_ANexp:Ind_1 -2.806e-03 1.082e-03 5.120e+03 -2.595 0.00949 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Dem_1 Ind_1 i_AN:D
## W1vW2 -0.020
## index_ANexp -0.649 0.073
## Dem_1 -0.670 0.017 0.446
## Ind_1 -0.557 0.030 0.384 0.406
## indx_AN:D_1 0.504 -0.031 -0.770 -0.713 -0.311
## indx_AN:I_1 0.383 -0.007 -0.609 -0.270 -0.670 0.486d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 +
index_ANexp *
(Dem_1 + Rep_1) +
(1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_ANexp * (Dem_1 + Rep_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21879.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07085 -0.45918 0.03003 0.47244 2.99335
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.035 1.742
## Residual 1.241 1.114
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.530e-01 9.408e-02 5.422e+03 -2.689 0.00718 **
## W1vW2 -2.754e-01 3.274e-02 2.459e+03 -8.411 < 2e-16 ***
## index_ANexp 3.628e-03 8.585e-04 5.148e+03 4.225 2.43e-05 ***
## Dem_1 7.991e-01 1.183e-01 5.439e+03 6.753 1.60e-11 ***
## Rep_1 -1.664e-02 1.129e-01 5.441e+03 -0.147 0.88278
## index_ANexp:Dem_1 -9.654e-04 1.008e-03 5.087e+03 -0.957 0.33846
## index_ANexp:Rep_1 2.806e-03 1.082e-03 5.120e+03 2.595 0.00949 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AN Dem_1 Rep_1 i_AN:D
## W1vW2 0.021
## index_ANexp -0.672 0.051
## Dem_1 -0.760 -0.014 0.527
## Rep_1 -0.779 -0.030 0.533 0.598
## indx_AN:D_1 0.558 -0.020 -0.832 -0.717 -0.448
## indx_AN:R_1 0.514 0.007 -0.766 -0.402 -0.670 0.6492. vaxxAttitudes ~ W1vW2 + AFFECT * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * index_AFexp + (1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * index_AFexp + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.963e-03 5.051e-02 4.766e+03 -0.158 0.874740
## W1vW2 -2.687e-01 3.284e-02 2.467e+03 -8.184 4.35e-16 ***
## DvR -8.463e-01 1.070e-01 5.227e+03 -7.906 3.21e-15 ***
## IvDR 3.919e-01 1.054e-01 5.408e+03 3.717 0.000204 ***
## index_AFexp 8.873e-02 8.886e-03 5.416e+03 9.986 < 2e-16 ***
## DvR:index_AFexp 8.215e-02 1.840e-02 5.296e+03 4.464 8.21e-06 ***
## IvDR:index_AFexp 1.814e-02 1.965e-02 5.077e+03 0.923 0.355922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AF DR:_AF
## W1vW2 -0.009
## DvR -0.052 -0.015
## IvDR -0.225 -0.028 -0.064
## index_AFexp -0.686 0.108 -0.004 0.159
## DvR:ndx_AFx 0.014 0.032 -0.733 0.022 0.149
## IvDR:ndx_AF 0.151 -0.002 0.025 -0.702 -0.264 0.085- plot
m1 <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * party_factor + (1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Rep_1 + Ind_1) + (1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Rep_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.54450 0.07975 5312.96352 6.828 9.59e-12 ***
## W1vW2 -0.26875 0.03284 2467.22199 -8.184 4.35e-16 ***
## index_AFexp 0.05364 0.01186 5339.40804 4.524 6.21e-06 ***
## Rep_1 -0.84629 0.10704 5227.49812 -7.906 3.21e-15 ***
## Ind_1 -0.81503 0.12128 5438.38387 -6.720 2.00e-11 ***
## index_AFexp:Rep_1 0.08215 0.01840 5295.62226 4.464 8.21e-06 ***
## index_AFexp:Ind_1 0.02294 0.02097 5116.70536 1.094 0.274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Rep_1 Ind_1 i_AF:R
## W1vW2 -0.008
## index_AFexp -0.791 0.055
## Rep_1 -0.732 -0.015 0.579
## Ind_1 -0.616 0.017 0.500 0.497
## indx_AF:R_1 0.510 0.032 -0.618 -0.733 -0.343
## indx_AF:I_1 0.437 0.016 -0.536 -0.345 -0.732 0.359c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Ind_1) + (1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Ind_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.30178 0.07289 4773.41683 -4.140 3.53e-05 ***
## W1vW2 -0.26875 0.03284 2467.22199 -8.184 4.35e-16 ***
## index_AFexp 0.13579 0.01448 5386.99965 9.376 < 2e-16 ***
## Dem_1 0.84629 0.10704 5227.49810 7.906 3.21e-15 ***
## Ind_1 0.03126 0.11513 5438.90082 0.272 0.78601
## index_AFexp:Dem_1 -0.08215 0.01840 5295.62228 -4.464 8.21e-06 ***
## index_AFexp:Ind_1 -0.05922 0.02240 5126.66594 -2.644 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Dem_1 Ind_1 i_AF:D
## W1vW2 -0.031
## index_AFexp -0.671 0.086
## Dem_1 -0.667 0.015 0.457
## Ind_1 -0.561 0.032 0.398 0.406
## indx_AF:D_1 0.518 -0.032 -0.765 -0.733 -0.321
## indx_AF:I_1 0.399 -0.011 -0.612 -0.280 -0.686 0.486d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Rep_1) + (1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + index_AFexp * (Dem_1 + Rep_1) + (1 |
## participant)
## Data: dm
##
## REML criterion at convergence: 21860.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07554 -0.46461 0.02802 0.47296 2.97593
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.027 1.740
## Residual 1.243 1.115
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.27053 0.09564 5425.88907 -2.829 0.00469 **
## W1vW2 -0.26875 0.03284 2467.22172 -8.184 4.35e-16 ***
## index_AFexp 0.07658 0.01772 5162.86573 4.321 1.59e-05 ***
## Dem_1 0.81503 0.12128 5438.38387 6.720 2.00e-11 ***
## Rep_1 -0.03126 0.11513 5438.90081 -0.272 0.78601
## index_AFexp:Dem_1 -0.02294 0.02097 5116.70538 -1.094 0.27421
## index_AFexp:Rep_1 0.05922 0.02240 5126.66595 2.644 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF Dem_1 Rep_1 i_AF:D
## W1vW2 0.015
## index_AFexp -0.685 0.056
## Dem_1 -0.754 -0.017 0.532
## Rep_1 -0.776 -0.032 0.541 0.591
## indx_AF:D_1 0.565 -0.016 -0.825 -0.732 -0.451
## indx_AF:R_1 0.521 0.011 -0.763 -0.404 -0.686 0.6423. vaxxAttitudes ~ W1vW2 + (AFFECT + ANALYTIC) * party + (1 | participant)
a. contrast model
model1.cc <- lmer(vaxxAttitudes ~ W1vW2 + (DvR + IvDR) *
(index_AFexp + index_ANexp) +
(1 | participant), data = dm)
summary(model1.cc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (DvR + IvDR) * (index_AFexp + index_ANexp) +
## (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.021e-03 5.192e-02 4.813e+03 0.020 0.9843
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## DvR -8.977e-01 1.109e-01 5.272e+03 -8.093 7.19e-16 ***
## IvDR 4.195e-01 1.076e-01 5.395e+03 3.897 9.86e-05 ***
## index_AFexp 7.303e-02 7.536e-02 5.313e+03 0.969 0.3326
## index_ANexp 7.066e-04 3.625e-03 5.303e+03 0.195 0.8455
## DvR:index_AFexp 2.940e-01 1.429e-01 5.111e+03 2.057 0.0397 *
## DvR:index_ANexp -1.013e-02 6.822e-03 5.097e+03 -1.485 0.1377
## IvDR:index_AFexp -1.348e-01 1.753e-01 4.906e+03 -0.769 0.4420
## IvDR:index_ANexp 7.373e-03 8.479e-03 4.894e+03 0.870 0.3846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 DvR IvDR ind_AF ind_AN DR:_AF DR:_AN IDR:_AF
## W1vW2 -0.041
## DvR -0.054 -0.013
## IvDR -0.209 -0.036 -0.068
## index_AFexp -0.298 0.134 -0.018 0.058
## index_ANexp 0.221 -0.121 0.019 -0.042 -0.993
## DvR:ndx_AFx -0.021 0.025 -0.347 0.002 0.194 -0.197
## DvR:ndx_ANx 0.024 -0.022 0.258 0.001 -0.197 0.202 -0.992
## IvDR:ndx_AF 0.047 0.014 0.001 -0.270 -0.411 0.417 0.092 -0.094
## IvDR:ndx_AN -0.032 -0.014 0.002 0.194 0.414 -0.423 -0.093 0.097 -0.994- plots
m1 <- lmer(vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_ANexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media analytic thinking ") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 350) +
theme_minimal()+
labs(color ='partisan identity') 
m1 <- lmer(vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * party_factor +
(1 | participant), data = dm)
plot_model(m1, type = "pred", terms = c("index_AFexp", "party_factor"),
color = c("blue", "red", "purple")) +
ggtitle("") +
xlab("media affect") +
ylab("willingness to obtain the Covid-19 vaccine") +
xlim(0, 20) +
theme_minimal()+
labs(color ='partisan identity') 
b. simple effects Dem
model1.dem <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Rep_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.dem)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Rep_1 +
## Ind_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.883e-01 8.270e-02 5.310e+03 7.114 1.28e-12 ***
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp -1.185e-01 8.772e-02 5.135e+03 -1.350 0.1769
## index_ANexp 8.204e-03 4.144e-03 5.082e+03 1.980 0.0478 *
## Rep_1 -8.977e-01 1.109e-01 5.272e+03 -8.093 7.19e-16 ***
## Ind_1 -8.684e-01 1.244e-01 5.435e+03 -6.981 3.28e-12 ***
## index_AFexp:Rep_1 2.940e-01 1.429e-01 5.111e+03 2.057 0.0397 *
## index_AFexp:Ind_1 2.818e-01 1.832e-01 4.960e+03 1.538 0.1240
## index_ANexp:Rep_1 -1.013e-02 6.822e-03 5.097e+03 -1.485 0.1377
## index_ANexp:Ind_1 -1.244e-02 8.827e-03 4.936e+03 -1.409 0.1589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Rep_1 Ind_1 i_AF:R i_AF:I i_AN:R
## W1vW2 -0.033
## index_AFexp -0.367 0.104
## index_ANexp 0.266 -0.097 -0.991
## Rep_1 -0.734 -0.013 0.268 -0.194
## Ind_1 -0.624 0.026 0.232 -0.168 0.504
## indx_AF:R_1 0.221 0.025 -0.587 0.581 -0.347 -0.157
## indx_AF:I_1 0.170 -0.004 -0.451 0.446 -0.137 -0.286 0.302
## indx_AN:R_1 -0.158 -0.022 0.576 -0.581 0.258 0.114 -0.992 -0.296
## indx_AN:I_1 -0.120 0.005 0.438 -0.441 0.098 0.205 -0.294 -0.993 0.293c. simple effects Rep
model1.rep <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Dem_1 + Ind_1) +
(1 | participant), data = dm)
summary(model1.rep)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Dem_1 +
## Ind_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.094e-01 7.541e-02 4.942e+03 -4.103 4.14e-05 ***
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp 1.755e-01 1.157e-01 5.248e+03 1.517 0.1294
## index_ANexp -1.925e-03 5.557e-03 5.255e+03 -0.346 0.7291
## Dem_1 8.977e-01 1.109e-01 5.272e+03 8.093 7.19e-16 ***
## Ind_1 2.936e-02 1.177e-01 5.427e+03 0.249 0.8031
## index_AFexp:Dem_1 -2.940e-01 1.429e-01 5.111e+03 -2.057 0.0397 *
## index_AFexp:Ind_1 -1.219e-02 1.953e-01 4.918e+03 -0.062 0.9502
## index_ANexp:Dem_1 1.013e-02 6.822e-03 5.097e+03 1.485 0.1377
## index_ANexp:Ind_1 -2.309e-03 9.441e-03 4.914e+03 -0.245 0.8068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Dem_1 Ind_1 i_AF:D i_AF:I i_AN:D
## W1vW2 -0.055
## index_AFexp -0.337 0.109
## index_ANexp 0.258 -0.099 -0.992
## Dem_1 -0.667 0.013 0.226 -0.172
## Ind_1 -0.568 0.039 0.194 -0.146 0.409
## indx_AF:D_1 0.268 -0.025 -0.790 0.784 -0.347 -0.161
## indx_AF:I_1 0.183 -0.022 -0.548 0.544 -0.126 -0.281 0.448
## indx_AN:D_1 -0.207 0.022 0.788 -0.795 0.258 0.123 -0.992 -0.448
## indx_AN:I_1 -0.138 0.020 0.540 -0.545 0.095 0.205 -0.442 -0.993 0.448d. simple effects Indep
model1.ind <- lmer(vaxxAttitudes ~ W1vW2 +
(index_AFexp + index_ANexp) *
(Dem_1 + Rep_1) +
(1 | participant), data = dm)
summary(model1.ind)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: vaxxAttitudes ~ W1vW2 + (index_AFexp + index_ANexp) * (Dem_1 +
## Rep_1) + (1 | participant)
## Data: dm
##
## REML criterion at convergence: 21881.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0352 -0.4588 0.0287 0.4704 2.9707
##
## Random effects:
## Groups Name Variance Std.Dev.
## participant (Intercept) 3.019 1.737
## Residual 1.246 1.116
## Number of obs: 5448, groups: participant, 3244
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.800e-01 9.728e-02 5.426e+03 -2.879 0.00401 **
## W1vW2 -2.730e-01 3.320e-02 2.506e+03 -8.223 3.15e-16 ***
## index_AFexp 1.634e-01 1.636e-01 5.036e+03 0.998 0.31811
## index_ANexp -4.233e-03 7.926e-03 5.023e+03 -0.534 0.59331
## Dem_1 8.684e-01 1.244e-01 5.435e+03 6.981 3.28e-12 ***
## Rep_1 -2.936e-02 1.177e-01 5.427e+03 -0.249 0.80306
## index_AFexp:Dem_1 -2.818e-01 1.832e-01 4.960e+03 -1.538 0.12400
## index_AFexp:Rep_1 1.219e-02 1.953e-01 4.918e+03 0.062 0.95023
## index_ANexp:Dem_1 1.244e-02 8.827e-03 4.936e+03 1.409 0.15890
## index_ANexp:Rep_1 2.309e-03 9.441e-03 4.914e+03 0.245 0.80682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) W1vW2 ind_AF ind_AN Dem_1 Rep_1 i_AF:D i_AF:R i_AN:D
## W1vW2 0.005
## index_AFexp -0.255 0.052
## index_ANexp 0.183 -0.046 -0.994
## Dem_1 -0.748 -0.026 0.195 -0.140
## Rep_1 -0.770 -0.039 0.198 -0.141 0.581
## indx_AF:D_1 0.221 0.004 -0.878 0.873 -0.286 -0.173
## indx_AF:R_1 0.198 0.022 -0.806 0.802 -0.153 -0.281 0.717
## indx_AN:D_1 -0.159 -0.005 0.878 -0.883 0.205 0.124 -0.993 -0.716
## indx_AN:R_1 -0.140 -0.020 0.804 -0.809 0.109 0.205 -0.715 -0.993 0.723