library(readr)
library(psych)
library(lme4)
## Loading required package: Matrix
library(lmerTest)
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
d <- read.csv (file.choose())
# continuous party
d$partyCont <- NA
d$partyCont[d$demStrength == 1] <- -3
d$partyCont[d$demStrength == 2] <- -2
d$partyCont[d$partyClose== 1] <- -1
d$partyCont[d$partyClose == 3] <- 0
d$partyCont[d$repStrength == 1] <- 3
d$partyCont[d$repStrength == 2] <- 2
d$partyCont[d$partyClose == 2] <- 1
# party factor
d$party_factor <- NA
d$party_factor[d$partyCont < 0] <- 'Democrat'
d$party_factor[d$partyCont == 0] <- 'Independent'
d$party_factor[d$partyCont > 0] <- 'Republican'
## Order of timing var
d$election_timing <- factor(d$election_timing, levels = c('Pre-election', 'During-election','Post-election'))
d$party_factor <- factor(d$party_factor, levels = c('Democrat', 'Republican','Independent'))
### Party Factor
d$pDem_Rep <- NA
d$pDem_Rep[d$party_factor == 'Democrat'] <- -.5
d$pDem_Rep[d$party_factor == 'Independent'] <- 0
d$pDem_Rep[d$party_factor == 'Republican'] <- .5
d$pInd_Not <- NA
d$pInd_Not[d$party_factor == 'Democrat'] <- .33
d$pInd_Not[d$party_factor == 'Independent'] <- -.67
d$pInd_Not[d$party_factor == 'Republican'] <- .33
#dummy codes for party ID
d$pDemR[d$party_factor == 'Democrat'] <- 0
d$pDemR[d$party_factor == 'Republican'] <- 1
d$pDemR[d$party_factor == 'Independent'] <- 0
d$pDemI[d$party_factor == 'Democrat'] <- 0
d$pDemI[d$party_factor == 'Republican'] <- 0
d$pDemI[d$party_factor == 'Independent'] <- 1
d$pRepD[d$party_factor == 'Democrat'] <- 1
d$pRepD[d$party_factor == 'Republican'] <- 0
d$pRepD[d$party_factor == 'Independent'] <- 0
d$pRepI[d$party_factor == 'Democrat'] <- 0
d$pRepI[d$party_factor == 'Republican'] <- 0
d$pRepI[d$party_factor == 'Independent'] <- 1
d$IndR[d$party_factor == 'Democrat'] <- 0
d$IndR[d$party_factor == 'Republican'] <- 1
d$IndR[d$party_factor == 'Independent'] <- 0
d$IndD[d$party_factor == 'Democrat'] <- 1
d$IndD[d$party_factor == 'Republican'] <- 0
d$IndD[d$party_factor == 'Independent'] <- 0
## negative emotions
d$negEmo <- (d$emotion_1 + d$emotion_3 + d$emotion_8 + d$emotion_9 + d$emotion_12)/5
d$negEmo.c <- d$negEmo - mean(d$negEmo, na.rm = T)
d2 <- d[d$election_timing != "Pre-election",]
### Timing codes
## Contrast
d2$tDur_Post <- NA
d2$tDur_Post[d2$election_timing == 'During-election'] <- -.5
d2$tDur_Post[d2$election_timing == 'Post-election'] <- .5
# Dummy
# Post!
d2$tDurD <- NA
d2$tDurD[d2$election_timing == 'During-election'] <- 0
d2$tDurD[d2$election_timing == 'Post-election'] <- 1
# During!
d2$tPostD <- NA
d2$tPostD[d2$election_timing == 'During-election'] <- 1
d2$tPostD[d2$election_timing == 'Post-election'] <- 0
negative emotions
main model
negEmotion.m <- lm(negEmo.c ~ (pDem_Rep + pInd_Not) * (tDur_Post), data = d2)
summary(negEmotion.m)
##
## Call:
## lm(formula = negEmo.c ~ (pDem_Rep + pInd_Not) * (tDur_Post),
## data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5920 -1.3920 -0.2589 1.1411 4.3456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.12636 0.05225 -2.418 0.0157 *
## pDem_Rep 0.06104 0.10288 0.593 0.5531
## pInd_Not 0.53800 0.12965 4.150 3.56e-05 ***
## tDur_Post -0.16826 0.10450 -1.610 0.1076
## pDem_Rep:tDur_Post 1.08933 0.20576 5.294 1.42e-07 ***
## pInd_Not:tDur_Post -0.06123 0.25930 -0.236 0.8134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.635 on 1208 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.04018, Adjusted R-squared: 0.03621
## F-statistic: 10.11 on 5 and 1208 DF, p-value: 1.671e-09
dummy coded for dem
negEmotion.D <- lm(negEmo.c ~ (pDemR + pDemI) * (tDur_Post), data = d2)
summary(negEmotion.D)
##
## Call:
## lm(formula = negEmo.c ~ (pDemR + pDemI) * (tDur_Post), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5920 -1.3920 -0.2589 1.1411 4.3456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02066 0.06945 0.297 0.766177
## pDemR 0.06104 0.10288 0.593 0.553113
## pDemI -0.50748 0.13779 -3.683 0.000241 ***
## tDur_Post -0.73313 0.13889 -5.278 1.54e-07 ***
## pDemR:tDur_Post 1.08933 0.20576 5.294 1.42e-07 ***
## pDemI:tDur_Post 0.60589 0.27557 2.199 0.028092 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.635 on 1208 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.04018, Adjusted R-squared: 0.03621
## F-statistic: 10.11 on 5 and 1208 DF, p-value: 1.671e-09
dummy coded for rep
negEmotion.R <- lm(negEmo.c ~ (pRepD + pRepI) * (tDur_Post), data = d2)
summary(negEmotion.R)
##
## Call:
## lm(formula = negEmo.c ~ (pRepD + pRepI) * (tDur_Post), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5920 -1.3920 -0.2589 1.1411 4.3456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08169 0.07591 1.076 0.2820
## pRepD -0.06104 0.10288 -0.593 0.5531
## pRepI -0.56851 0.14115 -4.028 5.99e-05 ***
## tDur_Post 0.35620 0.15182 2.346 0.0191 *
## pRepD:tDur_Post -1.08933 0.20576 -5.294 1.42e-07 ***
## pRepI:tDur_Post -0.48344 0.28231 -1.712 0.0871 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.635 on 1208 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.04018, Adjusted R-squared: 0.03621
## F-statistic: 10.11 on 5 and 1208 DF, p-value: 1.671e-09
dummy coded for independent
negEmotion.I <- lm(negEmo.c ~ (IndR + IndD) * (tDur_Post), data = d2)
summary(negEmotion.I)
##
## Call:
## lm(formula = negEmo.c ~ (IndR + IndD) * (tDur_Post), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5920 -1.3920 -0.2589 1.1411 4.3456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4868 0.1190 -4.091 4.59e-05 ***
## IndR 0.5685 0.1412 4.028 5.99e-05 ***
## IndD 0.5075 0.1378 3.683 0.000241 ***
## tDur_Post -0.1272 0.2380 -0.535 0.593028
## IndR:tDur_Post 0.4834 0.2823 1.712 0.087070 .
## IndD:tDur_Post -0.6059 0.2756 -2.199 0.028092 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.635 on 1208 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.04018, Adjusted R-squared: 0.03621
## F-statistic: 10.11 on 5 and 1208 DF, p-value: 1.671e-09
means
print("means democrat")
## [1] "means democrat"
dDem <- d2[d2$party_factor == "Democrat",]
mean(dDem$negEmo[dDem$election_timing == "During-election"], na.rm = T)
## [1] 3.592032
mean(dDem$negEmo[dDem$election_timing == "Post-election"], na.rm = T)
## [1] 2.8589
print("means republican")
## [1] "means republican"
dRep <- d2[d2$party_factor == "Republican",]
mean(dRep$negEmo[dRep$election_timing == "During-election"], na.rm = T)
## [1] 3.108403
mean(dRep$negEmo[dRep$election_timing == "Post-election"], na.rm = T)
## [1] 3.464602
print("means independent")
## [1] "means independent"
dInd <- d2[d2$party_factor == "Independent",]
mean(dInd$negEmo[dInd$election_timing == "During-election"], na.rm = T)
## [1] 2.781609
mean(dInd$negEmo[dInd$election_timing == "Post-election"], na.rm = T)
## [1] 2.654369
decomposing anger interaction
Model: Dem vs Rep interegnum There is no sig difference between Dems and Reps during the election, t(1209) = -1.30, p = 0.194.
anger.m <- lm(emotion_1 ~ (pDem_Rep + pInd_Not) * (tDurD), data = d2)
summary(anger.m)
##
## Call:
## lm(formula = emotion_1 ~ (pDem_Rep + pInd_Not) * (tDurD), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7345 -1.7217 -0.6295 1.3705 4.4138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.20523 0.09448 33.923 < 2e-16 ***
## pDem_Rep -0.23873 0.18349 -1.301 0.194
## pInd_Not 0.92391 0.23600 3.915 9.55e-05 ***
## tDurD -0.14125 0.12950 -1.091 0.276
## pDem_Rep:tDurD 1.25156 0.25531 4.902 1.08e-06 ***
## pInd_Not:tDurD -0.42658 0.32113 -1.328 0.184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.028 on 1209 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.04703, Adjusted R-squared: 0.04309
## F-statistic: 11.93 on 5 and 1209 DF, p-value: 2.711e-11
Model: Dem vs Rep declared There is a significant difference between Dems and Reps such that Reps are 1.01 units angrier then Dems, t(1209) = 5.71, p<.001.
anger.m <- lm(emotion_1 ~ (pDem_Rep + pInd_Not) * (tPostD), data = d2)
summary(anger.m)
##
## Call:
## lm(formula = emotion_1 ~ (pDem_Rep + pInd_Not) * (tPostD), data = d2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7345 -1.7217 -0.6295 1.3705 4.4138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.06398 0.08856 34.597 < 2e-16 ***
## pDem_Rep 1.01283 0.17752 5.706 1.46e-08 ***
## pInd_Not 0.49733 0.21778 2.284 0.0226 *
## tPostD 0.14125 0.12950 1.091 0.2756
## pDem_Rep:tPostD -1.25156 0.25531 -4.902 1.08e-06 ***
## pInd_Not:tPostD 0.42658 0.32113 1.328 0.1843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.028 on 1209 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.04703, Adjusted R-squared: 0.04309
## F-statistic: 11.93 on 5 and 1209 DF, p-value: 2.711e-11