5_10_meeting
Dani Grant
5/9/2022
data prep
import libraries and data sets
library(psych)
library(lme4)## Loading required package: Matrixlibrary(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(ggplot2)##
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## summary.parameters_skewness datawizard## Learn more about sjPlot with 'browseVignettes("sjPlot")'.library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.3 v purrr 0.3.4
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library(parallel)
library(minqa)
library(dfoptim)
library(ggcorrplot)
#import wave 1 survey data
d1.usa <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/Covid-19_NSF_RAPID_US_Cleaned1.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import wave 2 survey data
d2.usa <- 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 wave 3 survey data
d3.usa <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/wave3_raw.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)
#import LIWC csv USA
liwc <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/media polarization/LIWC_w1w2_Dec_2021.csv", header = T, na.strings = c("", " ", NA), stringsAsFactors = F)LIWC set up
wave 1
#move over measures of interest
w1 <- data.frame(liwc[liwc$Wave == 1,])
w1 <- w1[,c("mediaOutlet", "analytic")]
# create wide data set for wave 1
w1w = w1 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
### calculate averages for each ratings
#analytic
analytic <- data.frame(w1w[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w1 <- data.frame(w1w$mediaOutlet)
colnames(w1)[colnames(w1)=="w1w.mediaOutlet"] <- "mediaOutlet"
w1$analytic <- AN
w1$analytic.s <- scale(AN)
w1$analytic.s.c <- w1$analytic - mean(w1$analytic)wave 2
#move over measures of interest
w2 <- data.frame(liwc[liwc$Wave == 2,])
w2 <- w2[,c("mediaOutlet", "analytic")]
# create wide data set for wave 2
w2w = w2 %>%
group_by(mediaOutlet) %>%
mutate(Visit = 1:n()) %>%
gather("analytic",
key = variable,
value = number) %>%
unite(combi, variable, Visit) %>%
spread(combi, number)
### calculate averages for wave 2 ratings
#analytic
analytic <- data.frame(w2w[paste0("analytic_",1:21)])
AN <- apply(analytic, MARGIN = 1, FUN = mean, na.rm = T)
#add them all to new data.frame
w2 <- data.frame(w2w$mediaOutlet)
colnames(w2)[colnames(w2)=="w2w.mediaOutlet"] <- "mediaOutlet"
w2$analytic <- AN
w2$analytic.s <- scale(AN)
w2$analytic.s.c <- w2$analytic - mean(w2$analytic)data set up
wave 1
d1 <- d1.usa[,c("s3", "party", "demStrength", "repStrength", "partyClose",
"vaxxAttitudes", "race", "area",
"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"
colnames(d1)[colnames(d1) == "vaxxAttitudes"] <- "vaxxAttitudes_w1"
#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$AOL_exp <- d1$AOL_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
x <- cbind(d1$ABC_exp,
d1$CBS_exp,
d1$CNN_exp,
d1$Fox_exp,
d1$MSNBC_exp,
d1$NBC_exp,
d1$NPR_exp,
d1$NYT_exp,
d1$PBS_exp,
d1$USAT_exp,
d1$WSJ_exp,
d1$AOL_exp)
d1$sum.media.exp_w1 <- rowSums(x, na.rm = T)
# create stand alone scores
## analytic thinking
d1$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$mediaOutlet == "AOL"]
#individual media analytic thinking
d1$ABC_ANexp <- d1$ABC_AN * d1$ABC_exp
d1$CBS_ANexp <- d1$CBS_AN * d1$CBS_exp
d1$CNN_ANexp <- d1$CNN_AN * d1$CNN_exp
d1$Fox_ANexp <- d1$Fox_AN * d1$Fox_exp
d1$MSNBC_ANexp <- d1$MSNBC_AN * d1$MSNBC_exp
d1$NBC_ANexp <- d1$NBC_AN * d1$NBC_exp
d1$NPR_ANexp <- d1$NPR_AN * d1$NPR_exp
d1$NYT_ANexp <- d1$NYT_AN * d1$NYT_exp
d1$PBS_ANexp <- d1$PBS_AN * d1$PBS_exp
d1$USAT_ANexp <- d1$USAT_AN * d1$USAT_exp
d1$WSJ_ANexp <- d1$WSJ_AN * d1$WSJ_exp
d1$AOL_ANexp <- d1$AOL_AN * d1$AOL_exp
x <- cbind(d1$ABC_ANexp,
d1$CBS_ANexp,
d1$CNN_ANexp,
d1$Fox_ANexp,
d1$MSNBC_ANexp,
d1$NBC_ANexp,
d1$NPR_ANexp,
d1$NYT_ANexp,
d1$PBS_ANexp,
d1$USAT_ANexp,
d1$WSJ_ANexp,
d1$AOL_ANexp)
d1$index_ANexp_w1 <- rowMeans(x, na.rm = T)
# making party ID
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
#race coding
d1$race_bw <- NA
d1$race_bw[d1$race == 1] <- "other"
d1$race_bw[d1$race == 2] <- "black"
d1$race_bw[d1$race == 3] <- "other"
d1$race_bw[d1$race == 4] <- "other"
d1$race_bw[d1$race == 5] <- "other"
d1$race_bw[d1$race == 6] <- "white"
#c1: Black = -0.5, White = +.5; all other ethnicity = 0;
d1$bVw <- 0 #other
d1$bVw[d1$race == 6] <- .5 #white
d1$bVw[d1$race == 2] <- -.5 #black
#c2: Black = -.5, White = -.5, all other ethnicity = 1
d1$bwVo <- 1 #other
d1$bwVo[d1$race == 6] <- -.5 #white
d1$bwVo[d1$race == 2] <- -.5 #black
# 1 = Urban
# 2 = Suburban
# 3 = Rural
d1$area_factor <- NA
d1$area_factor[d1$area == 1] <- "other"
d1$area_factor[d1$area == 2] <- "other"
d1$area_factor[d1$area == 3] <- "rural"
d1$ruralVother <- NA
d1$ruralVother[d1$area == 3] <- -1/2
d1$ruralVother[d1$area == 1] <- 1/2
d1$ruralVother[d1$area == 2] <- 1/2wave 2
d2 <- d2.usa[,c("s3", "vaxxAttitudes",
"mediaExposure_1", "mediaExposure_2",
"mediaExposure_4", "mediaExposure_5",
"mediaExposure_6", "mediaExposure_7",
"mediaExposure_10", "mediaExposure_11",
"mediaExposure_12", "mediaExposure_13",
"mediaExposure_14", "mediaExposure_15")]
colnames(d2)[colnames(d2) == "s3"] <- "participant"
colnames(d2)[colnames(d2) == "vaxxAttitudes"] <- "vaxxAttitudes_w2"
#rename exposure
colnames(d2)[colnames(d2)=="mediaExposure_1"] <- "NYT_exp"
colnames(d2)[colnames(d2)=="mediaExposure_2"] <- "WSJ_exp"
colnames(d2)[colnames(d2)=="mediaExposure_4"] <- "USAT_exp"
colnames(d2)[colnames(d2)=="mediaExposure_5"] <- "Fox_exp"
colnames(d2)[colnames(d2)=="mediaExposure_6"] <- "CNN_exp"
colnames(d2)[colnames(d2)=="mediaExposure_7"] <- "MSNBC_exp"
colnames(d2)[colnames(d2)=="mediaExposure_10"] <-"AOL_exp"
colnames(d2)[colnames(d2)=="mediaExposure_11"] <-"NPR_exp"
colnames(d2)[colnames(d2)=="mediaExposure_12"] <-"ABC_exp"
colnames(d2)[colnames(d2)=="mediaExposure_13"] <-"NBC_exp"
colnames(d2)[colnames(d2)=="mediaExposure_14"] <-"CBS_exp"
colnames(d2)[colnames(d2)=="mediaExposure_15"] <-"PBS_exp"
#change to 0-4 rating
d2$ABC_exp <- d2$ABC_exp - 1
d2$AOL_exp <- d2$AOL_exp - 1
d2$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$WSJ_exp - 1
x <- cbind(d2$ABC_exp,
d2$CBS_exp,
d2$CNN_exp,
d2$Fox_exp,
d2$MSNBC_exp,
d2$NBC_exp,
d2$NPR_exp,
d2$NYT_exp,
d2$PBS_exp,
d2$USAT_exp,
d2$WSJ_exp,
d2$AOL_exp)
d2$sum.media.exp_w2 <- rowSums(x, na.rm = T)
## 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 analytic thinking
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$index_ANexp_w2 <- rowMeans(x, na.rm = T)wave 3
d3 <- d3.usa[,c("s3",
"vaxx_type", "boost_type", "mp_dose", "other_vaxx",
"media_exposure_1", "media_exposure_2",
"media_exposure_4", "media_exposure_5",
"media_exposure_6", "media_exposure_7",
"media_exposure_10", "media_exposure_11",
"media_exposure_12", "media_exposure_13",
"media_exposure_14", "media_exposure_15")]
colnames(d3)[colnames(d3) == "s3"] <- "participant"
#rename exposure
colnames(d3)[colnames(d3)=="media_exposure_1"] <- "NYT_exp"
colnames(d3)[colnames(d3)=="media_exposure_2"] <- "WSJ_exp"
colnames(d3)[colnames(d3)=="media_exposure_4"] <- "USAT_exp"
colnames(d3)[colnames(d3)=="media_exposure_5"] <- "Fox_exp"
colnames(d3)[colnames(d3)=="media_exposure_6"] <- "CNN_exp"
colnames(d3)[colnames(d3)=="media_exposure_7"] <- "MSNBC_exp"
colnames(d3)[colnames(d3)=="media_exposure_10"] <-"AOL_exp"
colnames(d3)[colnames(d3)=="media_exposure_11"] <-"NPR_exp"
colnames(d3)[colnames(d3)=="media_exposure_12"] <-"ABC_exp"
colnames(d3)[colnames(d3)=="media_exposure_13"] <-"NBC_exp"
colnames(d3)[colnames(d3)=="media_exposure_14"] <-"CBS_exp"
colnames(d3)[colnames(d3)=="media_exposure_15"] <-"PBS_exp"
#change to 0-4 rating
d3$ABC_exp <- 2/3*(d3$ABC_exp - 1)
d3$AOL_exp <- 2/3*(d3$AOL_exp - 1)
d3$CBS_exp <- 2/3*(d3$CBS_exp - 1)
d3$CNN_exp <- 2/3*(d3$CNN_exp - 1)
d3$Fox_exp <- 2/3*(d3$Fox_exp - 1)
d3$MSNBC_exp <- 2/3*(d3$MSNBC_exp - 1)
d3$NBC_exp <- 2/3*(d3$NBC_exp - 1)
d3$NPR_exp <- 2/3*(d3$NPR_exp - 1)
d3$NYT_exp <- 2/3*(d3$NYT_exp - 1)
d3$PBS_exp <- 2/3*(d3$PBS_exp - 1)
d3$USAT_exp <- 2/3*(d3$USAT_exp - 1)
d3$WSJ_exp <- 2/3*(d3$WSJ_exp - 1)
x <- cbind(d3$ABC_exp,
d3$CBS_exp,
d3$CNN_exp,
d3$Fox_exp,
d3$MSNBC_exp,
d3$NBC_exp,
d3$NPR_exp,
d3$NYT_exp,
d3$PBS_exp,
d3$USAT_exp,
d3$WSJ_exp,
d3$AOL_exp)
d3$sum.media.exp_w3 <- rowSums(x, na.rm = T)
## analytic thinking
d3$ABC_AN <- w2$analytic[w2$mediaOutlet == "ABC"]
d3$CBS_AN <- w2$analytic[w2$mediaOutlet == "CBS"]
d3$CNN_AN <- w2$analytic[w2$mediaOutlet == "CNN"]
d3$Fox_AN <- w2$analytic[w2$mediaOutlet == "Fox"]
d3$MSNBC_AN <- w2$analytic[w2$mediaOutlet == "MSNBC"]
d3$NBC_AN <- w2$analytic[w2$mediaOutlet == "NBC"]
d3$NPR_AN <- w2$analytic[w2$mediaOutlet == "NPR"]
d3$NYT_AN <- w2$analytic[w2$mediaOutlet == "NYT"]
d3$PBS_AN <- w2$analytic[w2$mediaOutlet == "PBS"]
d3$USAT_AN <- w2$analytic[w2$mediaOutlet == "USAToday"]
d3$WSJ_AN <- w2$analytic[w2$mediaOutlet == "WSJ"]
d3$AOL_AN <- w2$analytic[w2$mediaOutlet == "AOL"]
# analytic thinking
d3$ABC_ANexp <- d3$ABC_AN * d3$ABC_exp
d3$CBS_ANexp <- d3$CBS_AN * d3$CBS_exp
d3$CNN_ANexp <- d3$CNN_AN * d3$CNN_exp
d3$Fox_ANexp <- d3$Fox_AN * d3$Fox_exp
d3$MSNBC_ANexp <- d3$MSNBC_AN * d3$MSNBC_exp
d3$NBC_ANexp <- d3$NBC_AN * d3$NBC_exp
d3$NPR_ANexp <- d3$NPR_AN * d3$NPR_exp
d3$NYT_ANexp <- d3$NYT_AN * d3$NYT_exp
d3$PBS_ANexp <- d3$PBS_AN * d3$PBS_exp
d3$USAT_ANexp <- d3$USAT_AN * d3$USAT_exp
d3$WSJ_ANexp <- d3$WSJ_AN * d3$WSJ_exp
d3$AOL_ANexp <- d3$AOL_AN * d3$AOL_exp
x <- cbind(d3$ABC_ANexp,
d3$CBS_ANexp,
d3$CNN_ANexp,
d3$Fox_ANexp,
d3$MSNBC_ANexp,
d3$NBC_ANexp,
d3$NPR_ANexp,
d3$NYT_ANexp,
d3$PBS_ANexp,
d3$USAT_ANexp,
d3$WSJ_ANexp,
d3$AOL_ANexp)
d3$index_ANexp_w3 <- rowMeans(x, na.rm = T)
# vaccine behavior scale
d3$vaxxBehavior <- NA
d3$vaxxBehavior[d3$vaxx_type == 1] <- 0 #not vaxxed
d3$vaxxBehavior[d3$mp_dose == 1] <- 1 #partial vaxxed with M or P
d3$vaxxBehavior[d3$other_vaxx == 4] <- 1 #partial vaxxed with other
d3$vaxxBehavior[d3$vaxx_type == 2] <- 2 #fully vaxxed with J&J
d3$vaxxBehavior[d3$mp_dose == 2] <- 2 #fully vaxxed with M or P
d3$vaxxBehavior[d3$other_vaxx == 4] <- 2 #fully vaxxed with other
d3$vaxxBehavior[d3$boost_type != 1] <- 3 #vaxxed + boostedwide merged data
d.1 <- d1[
c("participant",
"vaxxAttitudes_w1",
"party_factor",
"DvR",
"IvDR",
"Rep_1",
"Ind_1",
"Dem_1",
"race_bw",
"bVw",
"bwVo",
"area_factor",
"ruralVother",
"index_ANexp_w1",
"sum.media.exp_w1")]
d.2 <- d2[
c("participant",
"vaxxAttitudes_w2",
"index_ANexp_w2",
"sum.media.exp_w2")]
d.3 <- d3[
c("participant",
"vaxxBehavior",
"index_ANexp_w3",
"sum.media.exp_w3")]
dw <- merge(d.1, d.2, by = c("participant"), all.x = T, all.y = T)
dw <- merge(dw, d.3, by = c("participant"), all.x = T, all.y = T)
dw <- dw[,c("participant",
"party_factor", "DvR", "IvDR",
"Rep_1", "Dem_1", "Ind_1",
"race_bw", "bVw", "bwVo",
"area_factor", "ruralVother",
"index_ANexp_w1", "index_ANexp_w2", "index_ANexp_w3",
"sum.media.exp_w1", "sum.media.exp_w2", "sum.media.exp_w3",
"vaxxAttitudes_w1", "vaxxAttitudes_w2", "vaxxBehavior")]
dw$avgVaxxAttitudes <- (dw$vaxxAttitudes_w1 + dw$vaxxAttitudes_w2)/2
dw$avgANexp <- (dw$index_ANexp_w1 + dw$index_ANexp_w2)/2
# mean center variables
dw$vaxxAttitudes_w1.c <- dw$vaxxAttitudes_w1 - mean(dw$vaxxAttitudes_w1, na.rm = T)
dw$vaxxAttitudes_w2.c <- dw$vaxxAttitudes_w2 - mean(dw$vaxxAttitudes_w2, na.rm = T)
dw$avgVaxxAttitudes.c <- dw$avgVaxxAttitudes - mean(dw$avgVaxxAttitudes, na.rm = T)
dw$avgANexp.c <- dw$avgANexp - mean(dw$avgANexp, na.rm = T)
dw$index_ANexp_w1.c <- dw$index_ANexp_w1 - mean(dw$index_ANexp_w1, na.rm = T)
dw$index_ANexp_w2.c <- dw$index_ANexp_w2 - mean(dw$index_ANexp_w2, na.rm = T)
dw$index_ANexp_w3.c <- dw$index_ANexp_w3 - mean(dw$index_ANexp_w3, na.rm = T)descriptives
- w1 LIWC media ratings
## mediaOutlet analytic
## 1 ABC 79.108
## 2 AOL 95.420
## 3 CBS 78.760
## 4 CNN 73.610
## 5 Fox 60.430
## 6 MSNBC 70.520
## 7 NBC 80.545
## 8 NPR 71.830
## 9 NYT 93.494
## 10 PBS 78.860
## 11 USAToday 91.280
## 12 WSJ 96.598
- w2 LIWC media ratings
## mediaOutlet analytic
## 1 ABC 75.978
## 2 AOL 95.313
## 3 CBS 84.055
## 4 CNN 64.720
## 5 Fox 61.095
## 6 MSNBC 69.560
## 7 NBC 78.120
## 8 NPR 70.815
## 9 NYT 93.364
## 10 PBS 77.060
## 11 USAToday 91.855
## 12 WSJ 96.432
correlations
- LIWC ratings
##
## Pearson's product-moment correlation
##
## data: w1$analytic and w2$analytic
## t = 11.662, df = 10, p-value = 3.821e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8770272 0.9904423
## sample estimates:
## cor
## 0.965145- media index scores
- vaxx attitudes/behavior
means & SDs
- across partyID
## M SD Min Max
## vaxxAttitudes_w1 0.60 2.15 -3 3
## vaxxAttitudes_w2 0.35 2.11 -3 3
## vaxxBehavior 2.22 1.18 0 3- by partyID
##
## Democrat Republican Independent
## 1534 1237 674##
## Democrat Republican Independent
## 0.445 0.359 0.196##
## Descriptive statistics by group
## party_factor: Democrat
## vars n mean sd median trimmed mad min max range skew
## vaxxAttitudes_w1 1 1498 1.16 1.91 2 1.42 1.48 -3 3 6 -0.81
## vaxxAttitudes_w2 2 1114 0.73 1.91 1 0.91 1.48 -3 3 6 -0.51
## vaxxBehavior 3 476 2.59 0.88 3 2.84 0.00 0 3 3 -2.21
## kurtosis se
## vaxxAttitudes_w1 -0.41 0.05
## vaxxAttitudes_w2 -0.70 0.06
## vaxxBehavior 3.61 0.04
## ------------------------------------------------------------
## party_factor: Republican
## vars n mean sd median trimmed mad min max range skew
## vaxxAttitudes_w1 1 1197 0.17 2.31 0 0.21 2.97 -3 3 6 -0.19
## vaxxAttitudes_w2 2 958 0.15 2.26 0 0.18 2.97 -3 3 6 -0.18
## vaxxBehavior 3 402 1.87 1.30 2 1.97 1.48 0 3 3 -0.59
## kurtosis se
## vaxxAttitudes_w1 -1.47 0.07
## vaxxAttitudes_w2 -1.39 0.07
## vaxxBehavior -1.43 0.07
## ------------------------------------------------------------
## party_factor: Independent
## vars n mean sd median trimmed mad min max range skew
## vaxxAttitudes_w1 1 641 0.11 2.11 0 0.13 2.97 -3 3 6 -0.14
## vaxxAttitudes_w2 2 433 -0.08 2.06 0 -0.10 2.97 -3 3 6 -0.01
## vaxxBehavior 3 175 2.06 1.27 3 2.19 0.00 0 3 3 -0.86
## kurtosis se
## vaxxAttitudes_w1 -1.21 0.08
## vaxxAttitudes_w2 -1.08 0.10
## vaxxBehavior -1.06 0.10Wave 1 (Aug 2020)
1. vaxxAttitudes ~ ANALYTIC + party
a. contrast model
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ DvR + IvDR + index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1950 -1.6670 0.1858 1.8062 3.3141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0350259 0.0577811 0.606 0.544
## DvR -0.7605102 0.0829941 -9.163 < 2e-16 ***
## IvDR 0.5210279 0.0908982 5.732 1.08e-08 ***
## index_ANexp_w1 0.0052624 0.0005153 10.213 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3332 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.08295, Adjusted R-squared: 0.08212
## F-statistic: 100.5 on 3 and 3332 DF, p-value: < 2.2e-16plot
b. simple effects Dem
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ Rep_1 + Ind_1 + index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1950 -1.6670 0.1858 1.8062 3.3141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5872202 0.0771280 7.614 3.45e-14 ***
## Rep_1 -0.7605102 0.0829941 -9.163 < 2e-16 ***
## Ind_1 -0.9012830 0.0985170 -9.149 < 2e-16 ***
## index_ANexp_w1 0.0052624 0.0005153 10.213 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3332 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.08295, Adjusted R-squared: 0.08212
## F-statistic: 100.5 on 3 and 3332 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ Dem_1 + Ind_1 + index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1950 -1.6670 0.1858 1.8062 3.3141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1732900 0.0685448 -2.528 0.0115 *
## Dem_1 0.7605102 0.0829941 9.163 <2e-16 ***
## Ind_1 -0.1407728 0.1013083 -1.390 0.1648
## index_ANexp_w1 0.0052624 0.0005153 10.213 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3332 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.08295, Adjusted R-squared: 0.08212
## F-statistic: 100.5 on 3 and 3332 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ Rep_1 + Dem_1 + index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1950 -1.6670 0.1858 1.8062 3.3141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3140628 0.0913277 -3.439 0.000591 ***
## Rep_1 0.1407728 0.1013083 1.390 0.164759
## Dem_1 0.9012830 0.0985170 9.149 < 2e-16 ***
## index_ANexp_w1 0.0052624 0.0005153 10.213 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.064 on 3332 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.08295, Adjusted R-squared: 0.08212
## F-statistic: 100.5 on 3 and 3332 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (DvR + IvDR) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7434 -1.5968 0.2297 1.8072 3.4032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0430505 0.0586318 0.734 0.463
## DvR -1.2623716 0.1282334 -9.844 < 2e-16 ***
## IvDR 0.5603220 0.1368667 4.094 4.34e-05 ***
## index_ANexp_w1 0.0056720 0.0005404 10.496 < 2e-16 ***
## DvR:index_ANexp_w1 0.0060268 0.0011752 5.128 3.09e-07 ***
## IvDR:index_ANexp_w1 0.0002692 0.0012664 0.213 0.832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 3330 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.09015, Adjusted R-squared: 0.08878
## F-statistic: 65.99 on 5 and 3330 DF, p-value: < 2.2e-16plot
b. simple effects Dem
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (Rep_1 + Ind_1) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7434 -1.5968 0.2297 1.8072 3.4032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.859143 0.096951 8.862 < 2e-16 ***
## Rep_1 -1.262372 0.128233 -9.844 < 2e-16 ***
## Ind_1 -1.191508 0.154987 -7.688 1.96e-14 ***
## index_ANexp_w1 0.002747 0.000750 3.663 0.000253 ***
## Rep_1:index_ANexp_w1 0.006027 0.001175 5.128 3.09e-07 ***
## Ind_1:index_ANexp_w1 0.002744 0.001349 2.034 0.042066 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 3330 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.09015, Adjusted R-squared: 0.08878
## F-statistic: 65.99 on 5 and 3330 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (Dem_1 + Ind_1) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7434 -1.5968 0.2297 1.8072 3.4032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4032290 0.0839304 -4.804 1.62e-06 ***
## Dem_1 1.2623716 0.1282334 9.844 < 2e-16 ***
## Ind_1 0.0708638 0.1471932 0.481 0.6302
## index_ANexp_w1 0.0087743 0.0009049 9.697 < 2e-16 ***
## Dem_1:index_ANexp_w1 -0.0060268 0.0011752 -5.128 3.09e-07 ***
## Ind_1:index_ANexp_w1 -0.0032827 0.0014412 -2.278 0.0228 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 3330 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.09015, Adjusted R-squared: 0.08878
## F-statistic: 65.99 on 5 and 3330 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (Rep_1 + Dem_1) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7434 -1.5968 0.2297 1.8072 3.4032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.332365 0.120920 -2.749 0.00602 **
## Rep_1 -0.070864 0.147193 -0.481 0.63024
## Dem_1 1.191508 0.154987 7.688 1.96e-14 ***
## index_ANexp_w1 0.005492 0.001122 4.895 1.03e-06 ***
## Rep_1:index_ANexp_w1 0.003283 0.001441 2.278 0.02281 *
## Dem_1:index_ANexp_w1 -0.002744 0.001349 -2.034 0.04207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.057 on 3330 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.09015, Adjusted R-squared: 0.08878
## F-statistic: 65.99 on 5 and 3330 DF, p-value: < 2.2e-16Wave 2 (Nov 2020)
1. vaxxAttitudes ~ ANALYTIC + party
a. contrast model
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ DvR + IvDR + index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6077 -1.4822 0.0818 1.7580 3.4297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1111178 0.0647065 -1.717 0.086056 .
## DvR -0.3596209 0.0950270 -3.784 0.000158 ***
## IvDR 0.4755160 0.1085504 4.381 1.23e-05 ***
## index_ANexp_w2 0.0049401 0.0006166 8.012 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2494 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05007, Adjusted R-squared: 0.04893
## F-statistic: 43.82 on 3 and 2494 DF, p-value: < 2.2e-16plot
## Warning: Removed 36 row(s) containing missing values (geom_path).
b. simple effects Dem
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ Rep_1 + Ind_1 + index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6077 -1.4822 0.0818 1.7580 3.4297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2256130 0.0880724 2.562 0.010475 *
## Rep_1 -0.3596209 0.0950270 -3.784 0.000158 ***
## Ind_1 -0.6553265 0.1177255 -5.567 2.88e-08 ***
## index_ANexp_w2 0.0049401 0.0006166 8.012 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2494 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05007, Adjusted R-squared: 0.04893
## F-statistic: 43.82 on 3 and 2494 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ Dem_1 + Ind_1 + index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6077 -1.4822 0.0818 1.7580 3.4297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1340079 0.0744488 -1.800 0.071981 .
## Dem_1 0.3596209 0.0950270 3.784 0.000158 ***
## Ind_1 -0.2957055 0.1192567 -2.480 0.013220 *
## index_ANexp_w2 0.0049401 0.0006166 8.012 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2494 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05007, Adjusted R-squared: 0.04893
## F-statistic: 43.82 on 3 and 2494 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ Rep_1 + Dem_1 + index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6077 -1.4822 0.0818 1.7580 3.4297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4297135 0.1080046 -3.979 7.13e-05 ***
## Rep_1 0.2957055 0.1192567 2.480 0.0132 *
## Dem_1 0.6553265 0.1177255 5.567 2.88e-08 ***
## index_ANexp_w2 0.0049401 0.0006166 8.012 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2494 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05007, Adjusted R-squared: 0.04893
## F-statistic: 43.82 on 3 and 2494 DF, p-value: < 2.2e-162. vaxxAttitudes ~ ANALYTIC * party
a. contrast model
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (DvR + IvDR) * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5128 -1.4390 0.0884 1.7604 3.5610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1289291 0.0661540 -1.949 0.05142 .
## DvR -0.4642664 0.1410609 -3.291 0.00101 **
## IvDR 0.6448787 0.1570213 4.107 4.14e-05 ***
## index_ANexp_w2 0.0053366 0.0006604 8.080 9.96e-16 ***
## DvR:index_ANexp_w2 0.0011434 0.0014130 0.809 0.41850
## IvDR:index_ANexp_w2 -0.0021478 0.0015643 -1.373 0.16988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2492 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05114, Adjusted R-squared: 0.04923
## F-statistic: 26.86 on 5 and 2492 DF, p-value: < 2.2e-16plot
b. simple effects Dem
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (Rep_1 + Ind_1) * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5128 -1.4390 0.0884 1.7604 3.5610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3160141 0.1086072 2.910 0.00365 **
## Rep_1 -0.4642664 0.1410609 -3.291 0.00101 **
## Ind_1 -0.8770119 0.1774167 -4.943 8.19e-07 ***
## index_ANexp_w2 0.0040562 0.0008754 4.633 3.78e-06 ***
## Rep_1:index_ANexp_w2 0.0011434 0.0014130 0.809 0.41850
## Ind_1:index_ANexp_w2 0.0027194 0.0016475 1.651 0.09894 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2492 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05114, Adjusted R-squared: 0.04923
## F-statistic: 26.86 on 5 and 2492 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (Dem_1 + Ind_1) * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5128 -1.4390 0.0884 1.7604 3.5610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.148252 0.090015 -1.647 0.09969 .
## Dem_1 0.464266 0.141061 3.291 0.00101 **
## Ind_1 -0.412745 0.166685 -2.476 0.01334 *
## index_ANexp_w2 0.005200 0.001109 4.688 2.91e-06 ***
## Dem_1:index_ANexp_w2 -0.001143 0.001413 -0.809 0.41850
## Ind_1:index_ANexp_w2 0.001576 0.001783 0.884 0.37673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2492 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05114, Adjusted R-squared: 0.04923
## F-statistic: 26.86 on 5 and 2492 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (Rep_1 + Dem_1) * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5128 -1.4390 0.0884 1.7604 3.5610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.560998 0.140290 -3.999 6.55e-05 ***
## Rep_1 0.412745 0.166685 2.476 0.0133 *
## Dem_1 0.877012 0.177417 4.943 8.19e-07 ***
## index_ANexp_w2 0.006776 0.001396 4.855 1.28e-06 ***
## Rep_1:index_ANexp_w2 -0.001576 0.001783 -0.884 0.3767
## Dem_1:index_ANexp_w2 -0.002719 0.001648 -1.651 0.0989 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.049 on 2492 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.05114, Adjusted R-squared: 0.04923
## F-statistic: 26.86 on 5 and 2492 DF, p-value: < 2.2e-16Wave 3 (March 2022)
1. vaxxBehavior ~ ANALYTIC + party
a. contrast model
##
## Call:
## lm(formula = vaxxBehavior ~ (DvR + IvDR) + index_ANexp_w3, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0875 -0.3853 0.3471 0.7290 1.2970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9475600 0.0518457 37.565 < 2e-16 ***
## DvR -0.5680482 0.0792272 -7.170 1.44e-12 ***
## IvDR 0.1195534 0.0928706 1.287 0.198
## index_ANexp_w3 0.0034163 0.0005139 6.647 4.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1019 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1188
## F-statistic: 46.92 on 3 and 1019 DF, p-value: < 2.2e-16- plot
## Warning: Removed 36 row(s) containing missing values (geom_path).
b. simple effects Dem
##
## Call:
## lm(formula = vaxxBehavior ~ (Rep_1 + Ind_1) + index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0875 -0.3853 0.3471 0.7290 1.2970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2710367 0.0710720 31.954 < 2e-16 ***
## Rep_1 -0.5680482 0.0792272 -7.170 1.44e-12 ***
## Ind_1 -0.4035775 0.1004236 -4.019 6.28e-05 ***
## index_ANexp_w3 0.0034163 0.0005139 6.647 4.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1019 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1188
## F-statistic: 46.92 on 3 and 1019 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxBehavior ~ (Dem_1 + Ind_1) + index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0875 -0.3853 0.3471 0.7290 1.2970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7029885 0.0614216 27.726 < 2e-16 ***
## Dem_1 0.5680482 0.0792272 7.170 1.44e-12 ***
## Ind_1 0.1644707 0.1015061 1.620 0.105
## index_ANexp_w3 0.0034163 0.0005139 6.647 4.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1019 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1188
## F-statistic: 46.92 on 3 and 1019 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxBehavior ~ (Rep_1 + Dem_1) + index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0875 -0.3853 0.3471 0.7290 1.2970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8674592 0.0902916 20.683 < 2e-16 ***
## Rep_1 -0.1644707 0.1015061 -1.620 0.105
## Dem_1 0.4035775 0.1004236 4.019 6.28e-05 ***
## index_ANexp_w3 0.0034163 0.0005139 6.647 4.86e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 1019 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1188
## F-statistic: 46.92 on 3 and 1019 DF, p-value: < 2.2e-162. vaxxBehavior ~ ANALYTIC * party
a. contrast model
##
## Call:
## lm(formula = vaxxBehavior ~ (DvR + IvDR) * index_ANexp_w3, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2238 -0.4878 0.3781 0.5716 1.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9468528 0.0527960 36.875 < 2e-16 ***
## DvR -0.8249592 0.1119466 -7.369 3.55e-13 ***
## IvDR 0.1985083 0.1257554 1.579 0.11476
## index_ANexp_w3 0.0038596 0.0005589 6.906 8.77e-12 ***
## DvR:index_ANexp_w3 0.0036566 0.0011586 3.156 0.00165 **
## IvDR:index_ANexp_w3 -0.0006433 0.0013492 -0.477 0.63362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.095 on 1017 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1304, Adjusted R-squared: 0.1261
## F-statistic: 30.51 on 5 and 1017 DF, p-value: < 2.2e-16- plot
b. simple effects Dem
##
## Call:
## lm(formula = vaxxBehavior ~ (Rep_1 + Ind_1) * index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2238 -0.4878 0.3781 0.5716 1.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4248401 0.0859067 28.226 < 2e-16 ***
## Rep_1 -0.8249592 0.1119466 -7.369 3.55e-13 ***
## Ind_1 -0.6109879 0.1416382 -4.314 1.76e-05 ***
## index_ANexp_w3 0.0018190 0.0007195 2.528 0.01161 *
## Rep_1:index_ANexp_w3 0.0036566 0.0011586 3.156 0.00165 **
## Ind_1:index_ANexp_w3 0.0024715 0.0014150 1.747 0.08100 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.095 on 1017 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1304, Adjusted R-squared: 0.1261
## F-statistic: 30.51 on 5 and 1017 DF, p-value: < 2.2e-16c. simple effects Rep
##
## Call:
## lm(formula = vaxxBehavior ~ (Dem_1 + Ind_1) * index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2238 -0.4878 0.3781 0.5716 1.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5998810 0.0717779 22.289 < 2e-16 ***
## Dem_1 0.8249592 0.1119466 7.369 3.55e-13 ***
## Ind_1 0.2139713 0.1335421 1.602 0.10941
## index_ANexp_w3 0.0054756 0.0009082 6.029 2.30e-09 ***
## Dem_1:index_ANexp_w3 -0.0036566 0.0011586 -3.156 0.00165 **
## Ind_1:index_ANexp_w3 -0.0011850 0.0015197 -0.780 0.43571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.095 on 1017 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1304, Adjusted R-squared: 0.1261
## F-statistic: 30.51 on 5 and 1017 DF, p-value: < 2.2e-16d. simple effects Indep
##
## Call:
## lm(formula = vaxxBehavior ~ (Rep_1 + Dem_1) * index_ANexp_w3,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2238 -0.4878 0.3781 0.5716 1.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.813852 0.112612 16.107 < 2e-16 ***
## Rep_1 -0.213971 0.133542 -1.602 0.109405
## Dem_1 0.610988 0.141638 4.314 1.76e-05 ***
## index_ANexp_w3 0.004291 0.001218 3.521 0.000449 ***
## Rep_1:index_ANexp_w3 0.001185 0.001520 0.780 0.435707
## Dem_1:index_ANexp_w3 -0.002472 0.001415 -1.747 0.081005 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.095 on 1017 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1304, Adjusted R-squared: 0.1261
## F-statistic: 30.51 on 5 and 1017 DF, p-value: < 2.2e-16longitudinal: vaxx attitudes wave 2
2. controlling model
a. contrast coded model
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 + (DvR + IvDR) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3922 -0.8126 0.0527 0.9417 5.3563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2243326 0.0487478 4.602 4.4e-06 ***
## index_ANexp_w2 0.0018731 0.0005873 3.190 0.00144 **
## DvR 0.1976645 0.0676764 2.921 0.00352 **
## IvDR 0.1634885 0.0762188 2.145 0.03205 *
## index_ANexp_w1 -0.0009377 0.0005667 -1.655 0.09808 .
## vaxxAttitudes_w1.c 0.7133174 0.0139803 51.023 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2475 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.5374, Adjusted R-squared: 0.5365
## F-statistic: 575.1 on 5 and 2475 DF, p-value: < 2.2e-16- plot
## Warning: Removed 36 row(s) containing missing values (geom_path).
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 + (Rep_1 + Ind_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3922 -0.8126 0.0527 0.9417 5.3563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1794515 0.0647671 2.771 0.00564 **
## index_ANexp_w2 0.0018731 0.0005873 3.190 0.00144 **
## Rep_1 0.1976645 0.0676764 2.921 0.00352 **
## Ind_1 -0.0646563 0.0832405 -0.777 0.43739
## index_ANexp_w1 -0.0009377 0.0005667 -1.655 0.09808 .
## vaxxAttitudes_w1.c 0.7133174 0.0139803 51.023 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2475 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.5374, Adjusted R-squared: 0.5365
## F-statistic: 575.1 on 5 and 2475 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 + (Dem_1 + Ind_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3922 -0.8126 0.0527 0.9417 5.3563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3771160 0.0550935 6.845 9.61e-12 ***
## index_ANexp_w2 0.0018731 0.0005873 3.190 0.00144 **
## Dem_1 -0.1976645 0.0676764 -2.921 0.00352 **
## Ind_1 -0.2623208 0.0835444 -3.140 0.00171 **
## index_ANexp_w1 -0.0009377 0.0005667 -1.655 0.09808 .
## vaxxAttitudes_w1.c 0.7133174 0.0139803 51.023 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2475 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.5374, Adjusted R-squared: 0.5365
## F-statistic: 575.1 on 5 and 2475 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 + (Dem_1 + Rep_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3922 -0.8126 0.0527 0.9417 5.3563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1147953 0.0779642 1.472 0.14104
## index_ANexp_w2 0.0018731 0.0005873 3.190 0.00144 **
## Dem_1 0.0646563 0.0832405 0.777 0.43739
## Rep_1 0.2623208 0.0835444 3.140 0.00171 **
## index_ANexp_w1 -0.0009377 0.0005667 -1.655 0.09808 .
## vaxxAttitudes_w1.c 0.7133174 0.0139803 51.023 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2475 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.5374, Adjusted R-squared: 0.5365
## F-statistic: 575.1 on 5 and 2475 DF, p-value: < 2.2e-162. interacting model
a. contrast coded model
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * (DvR + IvDR) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2202 -0.8507 0.0294 0.9298 5.3813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2094864 0.0498096 4.206 2.69e-05 ***
## index_ANexp_w2 0.0018826 0.0006132 3.070 0.00216 **
## DvR 0.2878132 0.1002033 2.872 0.00411 **
## IvDR 0.2511148 0.1104203 2.274 0.02304 *
## index_ANexp_w1 -0.0008771 0.0005677 -1.545 0.12247
## vaxxAttitudes_w1.c 0.7138195 0.0139922 51.016 < 2e-16 ***
## index_ANexp_w2:DvR -0.0013902 0.0009974 -1.394 0.16348
## index_ANexp_w2:IvDR -0.0014178 0.0010961 -1.294 0.19593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2473 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.538, Adjusted R-squared: 0.5367
## F-statistic: 411.5 on 7 and 2473 DF, p-value: < 2.2e-16- plot
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * (Rep_1 + Ind_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2202 -0.8507 0.0294 0.9298 5.3813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1484477 0.0790730 1.877 0.06059 .
## index_ANexp_w2 0.0021098 0.0007181 2.938 0.00334 **
## Rep_1 0.2878132 0.1002033 2.872 0.00411 **
## Ind_1 -0.1072082 0.1253468 -0.855 0.39247
## index_ANexp_w1 -0.0008771 0.0005677 -1.545 0.12247
## vaxxAttitudes_w1.c 0.7138195 0.0139922 51.016 < 2e-16 ***
## index_ANexp_w2:Rep_1 -0.0013902 0.0009974 -1.394 0.16348
## index_ANexp_w2:Ind_1 0.0007227 0.0011530 0.627 0.53085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2473 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.538, Adjusted R-squared: 0.5367
## F-statistic: 411.5 on 7 and 2473 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * (Dem_1 + Ind_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2202 -0.8507 0.0294 0.9298 5.3813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4362609 0.0652918 6.682 2.91e-11 ***
## index_ANexp_w2 0.0007196 0.0009008 0.799 0.424472
## Dem_1 -0.2878132 0.1002033 -2.872 0.004110 **
## Ind_1 -0.3950214 0.1170207 -3.376 0.000748 ***
## index_ANexp_w1 -0.0008771 0.0005677 -1.545 0.122472
## vaxxAttitudes_w1.c 0.7138195 0.0139922 51.016 < 2e-16 ***
## index_ANexp_w2:Dem_1 0.0013902 0.0009974 1.394 0.163485
## index_ANexp_w2:Ind_1 0.0021129 0.0012532 1.686 0.091918 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2473 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.538, Adjusted R-squared: 0.5367
## F-statistic: 411.5 on 7 and 2473 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ index_ANexp_w2 * (Dem_1 + Rep_1) +
## index_ANexp_w1 + vaxxAttitudes_w1.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2202 -0.8507 0.0294 0.9298 5.3813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0412394 0.1005424 0.410 0.681717
## index_ANexp_w2 0.0028325 0.0010498 2.698 0.007019 **
## Dem_1 0.1072082 0.1253468 0.855 0.392472
## Rep_1 0.3950214 0.1170207 3.376 0.000748 ***
## index_ANexp_w1 -0.0008771 0.0005677 -1.545 0.122472
## vaxxAttitudes_w1.c 0.7138195 0.0139922 51.016 < 2e-16 ***
## index_ANexp_w2:Dem_1 -0.0007227 0.0011530 -0.627 0.530854
## index_ANexp_w2:Rep_1 -0.0021129 0.0012532 -1.686 0.091918 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.429 on 2473 degrees of freedom
## (1372 observations deleted due to missingness)
## Multiple R-squared: 0.538, Adjusted R-squared: 0.5367
## F-statistic: 411.5 on 7 and 2473 DF, p-value: < 2.2e-16longitudinal: vaxx behavior
1. controlling model
a. contrast model
##
## Call:
## lm(formula = vaxxBehavior ~ DvR + IvDR + index_ANexp_w1 + index_ANexp_w2 +
## index_ANexp_w3 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3390 -0.4288 0.1788 0.6060 2.0212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0369290 0.0546233 37.290 < 2e-16 ***
## DvR -0.3432519 0.0753440 -4.556 5.92e-06 ***
## IvDR 0.0166757 0.0858511 0.194 0.84603
## index_ANexp_w1 -0.0000276 0.0006551 -0.042 0.96641
## index_ANexp_w2 -0.0005178 0.0007127 -0.727 0.46771
## index_ANexp_w3 0.0026544 0.0006660 3.985 7.28e-05 ***
## vaxxAttitudes_w1.c 0.1939377 0.0231595 8.374 < 2e-16 ***
## vaxxAttitudes_w2.c 0.0711004 0.0232100 3.063 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.964 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3155, Adjusted R-squared: 0.3103
## F-statistic: 60.45 on 7 and 918 DF, p-value: < 2.2e-16- plot
## Warning: Removed 36 row(s) containing missing values (geom_path).
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxBehavior ~ Rep_1 + Ind_1 + index_ANexp_w1 +
## index_ANexp_w2 + index_ANexp_w3 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3390 -0.4288 0.1788 0.6060 2.0212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2140580 0.0722644 30.638 < 2e-16 ***
## Rep_1 -0.3432519 0.0753440 -4.556 5.92e-06 ***
## Ind_1 -0.1883017 0.0932665 -2.019 0.04378 *
## index_ANexp_w1 -0.0000276 0.0006551 -0.042 0.96641
## index_ANexp_w2 -0.0005178 0.0007127 -0.727 0.46771
## index_ANexp_w3 0.0026544 0.0006660 3.985 7.28e-05 ***
## vaxxAttitudes_w1.c 0.1939377 0.0231595 8.374 < 2e-16 ***
## vaxxAttitudes_w2.c 0.0711004 0.0232100 3.063 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.964 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3155, Adjusted R-squared: 0.3103
## F-statistic: 60.45 on 7 and 918 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxBehavior ~ Dem_1 + Ind_1 + index_ANexp_w1 +
## index_ANexp_w2 + index_ANexp_w3 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3390 -0.4288 0.1788 0.6060 2.0212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8708060 0.0609024 30.718 < 2e-16 ***
## Dem_1 0.3432519 0.0753440 4.556 5.92e-06 ***
## Ind_1 0.1549503 0.0942366 1.644 0.10046
## index_ANexp_w1 -0.0000276 0.0006551 -0.042 0.96641
## index_ANexp_w2 -0.0005178 0.0007127 -0.727 0.46771
## index_ANexp_w3 0.0026544 0.0006660 3.985 7.28e-05 ***
## vaxxAttitudes_w1.c 0.1939377 0.0231595 8.374 < 2e-16 ***
## vaxxAttitudes_w2.c 0.0711004 0.0232100 3.063 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.964 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3155, Adjusted R-squared: 0.3103
## F-statistic: 60.45 on 7 and 918 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxBehavior ~ Dem_1 + Rep_1 + index_ANexp_w1 +
## index_ANexp_w2 + index_ANexp_w3 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3390 -0.4288 0.1788 0.6060 2.0212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0257563 0.0882864 22.945 < 2e-16 ***
## Dem_1 0.1883017 0.0932665 2.019 0.04378 *
## Rep_1 -0.1549503 0.0942366 -1.644 0.10046
## index_ANexp_w1 -0.0000276 0.0006551 -0.042 0.96641
## index_ANexp_w2 -0.0005178 0.0007127 -0.727 0.46771
## index_ANexp_w3 0.0026544 0.0006660 3.985 7.28e-05 ***
## vaxxAttitudes_w1.c 0.1939377 0.0231595 8.374 < 2e-16 ***
## vaxxAttitudes_w2.c 0.0711004 0.0232100 3.063 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.964 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3155, Adjusted R-squared: 0.3103
## F-statistic: 60.45 on 7 and 918 DF, p-value: < 2.2e-162. interacting model
a. contrast coded model
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (DvR + IvDR) + index_ANexp_w1 +
## index_ANexp_w2 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.4229 0.1671 0.5958 2.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0432455 0.0560006 36.486 < 2e-16 ***
## index_ANexp_w3 0.0028999 0.0006972 4.159 3.49e-05 ***
## DvR -0.4814893 0.1083585 -4.443 9.93e-06 ***
## IvDR 0.0254973 0.1171618 0.218 0.82777
## index_ANexp_w1 -0.0001013 0.0006606 -0.153 0.87810
## index_ANexp_w2 -0.0005659 0.0007135 -0.793 0.42791
## vaxxAttitudes_w1.c 0.1894784 0.0232998 8.132 1.36e-15 ***
## vaxxAttitudes_w2.c 0.0730067 0.0232198 3.144 0.00172 **
## index_ANexp_w3:DvR 0.0019353 0.0010856 1.783 0.07497 .
## index_ANexp_w3:IvDR 0.0001644 0.0012387 0.133 0.89445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9634 on 916 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3179, Adjusted R-squared: 0.3112
## F-statistic: 47.43 on 9 and 916 DF, p-value: < 2.2e-16- plot
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Rep_1 + Ind_1) +
## index_ANexp_w1 + index_ANexp_w2 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.4229 0.1671 0.5958 2.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2924042 0.0864780 26.509 < 2e-16 ***
## index_ANexp_w3 0.0019866 0.0008006 2.481 0.01327 *
## Rep_1 -0.4814893 0.1083585 -4.443 9.93e-06 ***
## Ind_1 -0.2662420 0.1326003 -2.008 0.04495 *
## index_ANexp_w1 -0.0001013 0.0006606 -0.153 0.87810
## index_ANexp_w2 -0.0005659 0.0007135 -0.793 0.42791
## vaxxAttitudes_w1.c 0.1894784 0.0232998 8.132 1.36e-15 ***
## vaxxAttitudes_w2.c 0.0730067 0.0232198 3.144 0.00172 **
## index_ANexp_w3:Rep_1 0.0019353 0.0010856 1.783 0.07497 .
## index_ANexp_w3:Ind_1 0.0008032 0.0013032 0.616 0.53781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9634 on 916 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3179, Adjusted R-squared: 0.3112
## F-statistic: 47.43 on 9 and 916 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Dem_1 + Ind_1) +
## index_ANexp_w1 + index_ANexp_w2 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.4229 0.1671 0.5958 2.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8109149 0.0702822 25.766 < 2e-16 ***
## index_ANexp_w3 0.0039218 0.0010092 3.886 0.000109 ***
## Dem_1 0.4814893 0.1083585 4.443 9.93e-06 ***
## Ind_1 0.2152473 0.1254660 1.716 0.086577 .
## index_ANexp_w1 -0.0001013 0.0006606 -0.153 0.878098
## index_ANexp_w2 -0.0005659 0.0007135 -0.793 0.427914
## vaxxAttitudes_w1.c 0.1894784 0.0232998 8.132 1.36e-15 ***
## vaxxAttitudes_w2.c 0.0730067 0.0232198 3.144 0.001719 **
## index_ANexp_w3:Dem_1 -0.0019353 0.0010856 -1.783 0.074968 .
## index_ANexp_w3:Ind_1 -0.0011320 0.0013999 -0.809 0.418911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9634 on 916 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3179, Adjusted R-squared: 0.3112
## F-statistic: 47.43 on 9 and 916 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Dem_1 + Rep_1) +
## index_ANexp_w1 + index_ANexp_w2 + vaxxAttitudes_w1.c + vaxxAttitudes_w2.c,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2229 -0.4229 0.1671 0.5958 2.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0261623 0.1097898 18.455 < 2e-16 ***
## index_ANexp_w3 0.0027898 0.0011894 2.346 0.01921 *
## Dem_1 0.2662420 0.1326003 2.008 0.04495 *
## Rep_1 -0.2152473 0.1254660 -1.716 0.08658 .
## index_ANexp_w1 -0.0001013 0.0006606 -0.153 0.87810
## index_ANexp_w2 -0.0005659 0.0007135 -0.793 0.42791
## vaxxAttitudes_w1.c 0.1894784 0.0232998 8.132 1.36e-15 ***
## vaxxAttitudes_w2.c 0.0730067 0.0232198 3.144 0.00172 **
## index_ANexp_w3:Dem_1 -0.0008032 0.0013032 -0.616 0.53781
## index_ANexp_w3:Rep_1 0.0011320 0.0013999 0.809 0.41891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9634 on 916 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3179, Adjusted R-squared: 0.3112
## F-statistic: 47.43 on 9 and 916 DF, p-value: < 2.2e-16longitudinal: vaxx behavior with avg w1 + w2
1. controlling model
a. contrast coded model
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 + (DvR + IvDR) + avgVaxxAttitudes.c +
## avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4220 -0.4483 0.1976 0.5982 2.0248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0005718 0.0588113 34.017 < 2e-16 ***
## index_ANexp_w3 0.0027630 0.0006653 4.153 3.59e-05 ***
## DvR -0.3719694 0.0747934 -4.973 7.85e-07 ***
## IvDR 0.0191846 0.0861244 0.223 0.824
## avgVaxxAttitudes.c 0.2650621 0.0168063 15.772 < 2e-16 ***
## avgANexp.c -0.0005143 0.0007275 -0.707 0.480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9673 on 920 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3092, Adjusted R-squared: 0.3055
## F-statistic: 82.37 on 5 and 920 DF, p-value: < 2.2e-16- plot
## Warning: Removed 36 row(s) containing missing values (geom_path).
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 + (Rep_1 + Ind_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4220 -0.4483 0.1976 0.5982 2.0248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1928874 0.0705025 31.104 < 2e-16 ***
## index_ANexp_w3 0.0027630 0.0006653 4.153 3.59e-05 ***
## Rep_1 -0.3719694 0.0747934 -4.973 7.85e-07 ***
## Ind_1 -0.2051693 0.0934089 -2.196 0.0283 *
## avgVaxxAttitudes.c 0.2650621 0.0168063 15.772 < 2e-16 ***
## avgANexp.c -0.0005143 0.0007275 -0.707 0.4798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9673 on 920 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3092, Adjusted R-squared: 0.3055
## F-statistic: 82.37 on 5 and 920 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 + (Dem_1 + Ind_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4220 -0.4483 0.1976 0.5982 2.0248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8209180 0.0711424 25.595 < 2e-16 ***
## index_ANexp_w3 0.0027630 0.0006653 4.153 3.59e-05 ***
## Dem_1 0.3719694 0.0747934 4.973 7.85e-07 ***
## Ind_1 0.1668001 0.0943749 1.767 0.0775 .
## avgVaxxAttitudes.c 0.2650621 0.0168063 15.772 < 2e-16 ***
## avgANexp.c -0.0005143 0.0007275 -0.707 0.4798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9673 on 920 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3092, Adjusted R-squared: 0.3055
## F-statistic: 82.37 on 5 and 920 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 + (Dem_1 + Rep_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4220 -0.4483 0.1976 0.5982 2.0248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9877181 0.0900352 22.077 < 2e-16 ***
## index_ANexp_w3 0.0027630 0.0006653 4.153 3.59e-05 ***
## Dem_1 0.2051693 0.0934089 2.196 0.0283 *
## Rep_1 -0.1668001 0.0943749 -1.767 0.0775 .
## avgVaxxAttitudes.c 0.2650621 0.0168063 15.772 < 2e-16 ***
## avgANexp.c -0.0005143 0.0007275 -0.707 0.4798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9673 on 920 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3092, Adjusted R-squared: 0.3055
## F-statistic: 82.37 on 5 and 920 DF, p-value: < 2.2e-162. interaction model
a. contrast coded model
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (DvR + IvDR) + avgVaxxAttitudes.c +
## avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2858 -0.4548 0.1777 0.5942 2.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9964751 0.0592640 33.688 < 2e-16 ***
## index_ANexp_w3 0.0030347 0.0006944 4.370 1.38e-05 ***
## DvR -0.5264715 0.1072889 -4.907 1.09e-06 ***
## IvDR 0.0293396 0.1173679 0.250 0.8027
## avgVaxxAttitudes.c 0.2621725 0.0168810 15.531 < 2e-16 ***
## avgANexp.c -0.0006535 0.0007319 -0.893 0.3721
## index_ANexp_w3:DvR 0.0021859 0.0010850 2.015 0.0442 *
## index_ANexp_w3:IvDR 0.0001805 0.0012367 0.146 0.8840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9663 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3123, Adjusted R-squared: 0.307
## F-statistic: 59.55 on 7 and 918 DF, p-value: < 2.2e-16- plot
b. simple effects for Dem
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Rep_1 + Ind_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2858 -0.4548 0.1777 0.5942 2.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2693929 0.0830672 27.320 < 2e-16 ***
## index_ANexp_w3 0.0020013 0.0008025 2.494 0.0128 *
## Rep_1 -0.5264715 0.1072889 -4.907 1.09e-06 ***
## Ind_1 -0.2925754 0.1323784 -2.210 0.0273 *
## avgVaxxAttitudes.c 0.2621725 0.0168810 15.531 < 2e-16 ***
## avgANexp.c -0.0006535 0.0007319 -0.893 0.3721
## index_ANexp_w3:Rep_1 0.0021859 0.0010850 2.015 0.0442 *
## index_ANexp_w3:Ind_1 0.0009125 0.0013012 0.701 0.4833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9663 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3123, Adjusted R-squared: 0.307
## F-statistic: 59.55 on 7 and 918 DF, p-value: < 2.2e-16c. simple effects for Rep
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Dem_1 + Ind_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2858 -0.4548 0.1777 0.5942 2.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7429214 0.0823207 21.172 < 2e-16 ***
## index_ANexp_w3 0.0041872 0.0010072 4.157 3.52e-05 ***
## Dem_1 0.5264715 0.1072889 4.907 1.09e-06 ***
## Ind_1 0.2338961 0.1256258 1.862 0.0629 .
## avgVaxxAttitudes.c 0.2621725 0.0168810 15.531 < 2e-16 ***
## avgANexp.c -0.0006535 0.0007319 -0.893 0.3721
## index_ANexp_w3:Dem_1 -0.0021859 0.0010850 -2.015 0.0442 *
## index_ANexp_w3:Ind_1 -0.0012734 0.0013980 -0.911 0.3626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9663 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3123, Adjusted R-squared: 0.307
## F-statistic: 59.55 on 7 and 918 DF, p-value: < 2.2e-16d. simple effects for Ind
##
## Call:
## lm(formula = vaxxBehavior ~ index_ANexp_w3 * (Dem_1 + Rep_1) +
## avgVaxxAttitudes.c + avgANexp.c, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2858 -0.4548 0.1777 0.5942 2.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9768175 0.1087707 18.174 <2e-16 ***
## index_ANexp_w3 0.0029138 0.0011832 2.463 0.0140 *
## Dem_1 0.2925754 0.1323784 2.210 0.0273 *
## Rep_1 -0.2338961 0.1256258 -1.862 0.0629 .
## avgVaxxAttitudes.c 0.2621725 0.0168810 15.531 <2e-16 ***
## avgANexp.c -0.0006535 0.0007319 -0.893 0.3721
## index_ANexp_w3:Dem_1 -0.0009125 0.0013012 -0.701 0.4833
## index_ANexp_w3:Rep_1 0.0012734 0.0013980 0.911 0.3626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9663 on 918 degrees of freedom
## (2927 observations deleted due to missingness)
## Multiple R-squared: 0.3123, Adjusted R-squared: 0.307
## F-statistic: 59.55 on 7 and 918 DF, p-value: < 2.2e-16race models
wave 1
. vaxxAttitudes ~ ANALYTIC + (black vs. white)
m9.cc <- lm(vaxxAttitudes_w1 ~ (bVw + bwVo) * index_ANexp_w1, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (bVw + bwVo) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0772 -1.6377 0.3328 1.8025 3.7097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1282494 0.0763902 -1.679 0.093271 .
## bVw 0.7147115 0.1864353 3.834 0.000129 ***
## bwVo 0.4481257 0.1084244 4.133 3.67e-05 ***
## index_ANexp_w1 0.0066100 0.0005854 11.291 < 2e-16 ***
## bVw:index_ANexp_w1 0.0018346 0.0014284 1.284 0.199098
## bwVo:index_ANexp_w1 -0.0013567 0.0008311 -1.632 0.102684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.076 on 3340 degrees of freedom
## (507 observations deleted due to missingness)
## Multiple R-squared: 0.07098, Adjusted R-squared: 0.06959
## F-statistic: 51.04 on 5 and 3340 DF, p-value: < 2.2e-16- plot
. vaxxAttitudes ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxAttitudes_w1 ~ (bVw + bwVo) * index_ANexp_w1 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (bVw + bwVo) * index_ANexp_w1 +
## DvR + IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3623 -1.6942 0.2234 1.6957 4.4502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2380724 0.0753864 -3.158 0.0016 **
## bVw 1.1988647 0.1875947 6.391 1.88e-10 ***
## bwVo 0.4992539 0.1061180 4.705 2.65e-06 ***
## index_ANexp_w1 0.0054148 0.0005826 9.295 < 2e-16 ***
## DvR -0.9927525 0.0850982 -11.666 < 2e-16 ***
## IvDR 0.5418547 0.0896199 6.046 1.65e-09 ***
## bVw:index_ANexp_w1 0.0006130 0.0014113 0.434 0.6640
## bwVo:index_ANexp_w1 -0.0011724 0.0008167 -1.436 0.1512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.025 on 3328 degrees of freedom
## (517 observations deleted due to missingness)
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.1169
## F-statistic: 64.05 on 7 and 3328 DF, p-value: < 2.2e-16- plot
wave 2
. vaxxAttitudes ~ ANALYTIC + (black vs. white)
m9.cc <- lm(vaxxAttitudes_w2 ~ (bVw + bwVo) * index_ANexp_w2, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (bVw + bwVo) * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1494 -1.4225 0.0235 1.7543 3.9451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3731620 0.0851781 -4.381 1.23e-05 ***
## bVw 0.9169914 0.2115370 4.335 1.52e-05 ***
## bwVo 0.2268305 0.1187656 1.910 0.0563 .
## index_ANexp_w2 0.0064572 0.0006860 9.413 < 2e-16 ***
## bVw:index_ANexp_w2 0.0002905 0.0017187 0.169 0.8658
## bwVo:index_ANexp_w2 -0.0004274 0.0009475 -0.451 0.6520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.043 on 2527 degrees of freedom
## (1320 observations deleted due to missingness)
## Multiple R-squared: 0.05714, Adjusted R-squared: 0.05527
## F-statistic: 30.63 on 5 and 2527 DF, p-value: < 2.2e-16- plot
. vaxxAttitudes ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxAttitudes_w2 ~ (bVw + bwVo) * index_ANexp_w2 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ (bVw + bwVo) * index_ANexp_w2 +
## DvR + IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7454 -1.4606 0.0959 1.7625 4.4133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4249936 0.0862943 -4.925 8.99e-07 ***
## bVw 1.1925530 0.2153127 5.539 3.37e-08 ***
## bwVo 0.3052345 0.1206889 2.529 0.0115 *
## index_ANexp_w2 0.0054401 0.0006989 7.784 1.02e-14 ***
## DvR -0.5750815 0.0976408 -5.890 4.39e-09 ***
## IvDR 0.4743602 0.1076767 4.405 1.10e-05 ***
## bVw:index_ANexp_w2 -0.0005195 0.0017166 -0.303 0.7622
## bwVo:index_ANexp_w2 -0.0003607 0.0009537 -0.378 0.7053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.021 on 2490 degrees of freedom
## (1355 observations deleted due to missingness)
## Multiple R-squared: 0.07727, Adjusted R-squared: 0.07468
## F-statistic: 29.79 on 7 and 2490 DF, p-value: < 2.2e-16- plot
wave 3
. vaxxBehavior ~ ANALYTIC + (black vs. white)
m9.cc <- lm(vaxxBehavior ~ (bVw + bwVo) * index_ANexp_w3, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxBehavior ~ (bVw + bwVo) * index_ANexp_w3, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3407 -0.2765 0.4347 0.8526 1.1376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9824205 0.0752718 26.337 < 2e-16 ***
## bVw -0.2148697 0.1941839 -1.107 0.2688
## bwVo 0.0251809 0.1004700 0.251 0.8021
## index_ANexp_w3 0.0039245 0.0006093 6.441 1.81e-10 ***
## bVw:index_ANexp_w3 0.0028467 0.0015727 1.810 0.0706 .
## bwVo:index_ANexp_w3 0.0007264 0.0008129 0.894 0.3718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 1041 degrees of freedom
## (2806 observations deleted due to missingness)
## Multiple R-squared: 0.08156, Adjusted R-squared: 0.07715
## F-statistic: 18.49 on 5 and 1041 DF, p-value: < 2.2e-16- plot
. vaxxBehavior ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxBehavior ~ (bVw + bwVo) * index_ANexp_w3 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxBehavior ~ (bVw + bwVo) * index_ANexp_w3 + DvR +
## IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0033 -0.3820 0.3366 0.7237 1.3182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9474228 0.0755492 25.777 < 2e-16 ***
## bVw 0.0308560 0.1933709 0.160 0.873
## bwVo 0.0537625 0.1029014 0.522 0.601
## index_ANexp_w3 0.0030245 0.0006183 4.892 1.16e-06 ***
## DvR -0.5916911 0.0814379 -7.266 7.40e-13 ***
## IvDR 0.1261486 0.0933416 1.351 0.177
## bVw:index_ANexp_w3 0.0021947 0.0015477 1.418 0.156
## bwVo:index_ANexp_w3 0.0007038 0.0008247 0.853 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.098 on 1015 degrees of freedom
## (2830 observations deleted due to missingness)
## Multiple R-squared: 0.1284, Adjusted R-squared: 0.1223
## F-statistic: 21.35 on 7 and 1015 DF, p-value: < 2.2e-16- plot
area models
wave 1
. vaxxAttitudes ~ ANALYTIC + (rural vs. other)
m9.cc <- lm(vaxxAttitudes_w1 ~ (ruralVother) * index_ANexp_w1, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ (ruralVother) * index_ANexp_w1,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2457 -1.6085 0.2599 1.8258 3.1401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0268101 0.0670421 -0.400 0.6893
## ruralVother 0.2266196 0.1340843 1.690 0.0911 .
## index_ANexp_w1 0.0059604 0.0006673 8.933 <2e-16 ***
## ruralVother:index_ANexp_w1 0.0014290 0.0013345 1.071 0.2844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.095 on 3336 degrees of freedom
## (513 observations deleted due to missingness)
## Multiple R-squared: 0.05406, Adjusted R-squared: 0.05321
## F-statistic: 63.55 on 3 and 3336 DF, p-value: < 2.2e-16- plot
. vaxxAttitudes ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxAttitudes_w1 ~ ruralVother * index_ANexp_w1 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxAttitudes_w1 ~ ruralVother * index_ANexp_w1 +
## DvR + IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2974 -1.6885 0.2084 1.8092 3.3611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0238663 0.0678988 0.351 0.725
## ruralVother 0.0802455 0.1328275 0.604 0.546
## index_ANexp_w1 0.0044374 0.0006764 6.561 6.19e-11 ***
## DvR -0.7548458 0.0834855 -9.042 < 2e-16 ***
## IvDR 0.5147483 0.0908241 5.668 1.57e-08 ***
## ruralVother:index_ANexp_w1 0.0021627 0.0013172 1.642 0.101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 3324 degrees of freedom
## (523 observations deleted due to missingness)
## Multiple R-squared: 0.08652, Adjusted R-squared: 0.08515
## F-statistic: 62.97 on 5 and 3324 DF, p-value: < 2.2e-16- plot
wave 2
. vaxxAttitudes ~ ANALYTIC + (black vs. white)
m9.cc <- lm(vaxxAttitudes_w2 ~ ruralVother * index_ANexp_w2, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ ruralVother * index_ANexp_w2,
## data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7472 -1.3943 -0.0094 1.7752 3.2704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1445784 0.0727307 -1.988 0.0469 *
## ruralVother 0.2516786 0.1454613 1.730 0.0837 .
## index_ANexp_w2 0.0055989 0.0007881 7.104 1.57e-12 ***
## ruralVother:index_ANexp_w2 -0.0001416 0.0015762 -0.090 0.9284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.056 on 2480 degrees of freedom
## (1369 observations deleted due to missingness)
## Multiple R-squared: 0.03881, Adjusted R-squared: 0.03764
## F-statistic: 33.37 on 3 and 2480 DF, p-value: < 2.2e-16- plot
. vaxxAttitudes ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxAttitudes_w2 ~ ruralVother * index_ANexp_w2 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxAttitudes_w2 ~ ruralVother * index_ANexp_w2 +
## DvR + IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6238 -1.4535 0.0791 1.7289 3.5527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1464634 0.0754152 -1.942 0.052239 .
## ruralVother 0.1866780 0.1454117 1.284 0.199336
## index_ANexp_w2 0.0046787 0.0008140 5.748 1.02e-08 ***
## DvR -0.3602081 0.0957839 -3.761 0.000173 ***
## IvDR 0.4669786 0.1087877 4.293 1.83e-05 ***
## ruralVother:index_ANexp_w2 0.0002409 0.0015717 0.153 0.878193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.046 on 2471 degrees of freedom
## (1376 observations deleted due to missingness)
## Multiple R-squared: 0.05142, Adjusted R-squared: 0.0495
## F-statistic: 26.79 on 5 and 2471 DF, p-value: < 2.2e-16- plot
wave 3
. vaxxBehavior ~ ANALYTIC + (black vs. white)
m9.cc <- lm(vaxxBehavior ~ ruralVother * index_ANexp_w3, data = dw)
summary(m9.cc)##
## Call:
## lm(formula = vaxxBehavior ~ ruralVother * index_ANexp_w3, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2057 -0.2824 0.3934 0.8199 1.3717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8185788 0.0594456 30.592 < 2e-16 ***
## ruralVother 0.3805089 0.1188912 3.200 0.00141 **
## index_ANexp_w3 0.0048173 0.0007273 6.623 5.71e-11 ***
## ruralVother:index_ANexp_w3 -0.0015632 0.0014547 -1.075 0.28282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.12 on 1009 degrees of freedom
## (2840 observations deleted due to missingness)
## Multiple R-squared: 0.08653, Adjusted R-squared: 0.08382
## F-statistic: 31.86 on 3 and 1009 DF, p-value: < 2.2e-16- plot
. vaxxBehavior ~ ANALYTIC * (black vs. white) + party
m10.cc <- lm(vaxxBehavior ~ ruralVother * index_ANexp_w3 + DvR + IvDR, data = dw)
summary(m10.cc)##
## Call:
## lm(formula = vaxxBehavior ~ ruralVother * index_ANexp_w3 + DvR +
## IvDR, data = dw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0854 -0.4130 0.3494 0.7578 1.4898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8850462 0.0605729 31.120 < 2e-16 ***
## ruralVother 0.2776550 0.1173689 2.366 0.0182 *
## index_ANexp_w3 0.0033582 0.0007433 4.518 6.99e-06 ***
## DvR -0.5473833 0.0801065 -6.833 1.44e-11 ***
## IvDR 0.1141358 0.0929579 1.228 0.2198
## ruralVother:index_ANexp_w3 -0.0004389 0.0014347 -0.306 0.7597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.096 on 1004 degrees of freedom
## (2843 observations deleted due to missingness)
## Multiple R-squared: 0.1279, Adjusted R-squared: 0.1236
## F-statistic: 29.45 on 5 and 1004 DF, p-value: < 2.2e-16- plot
