DATA PREPARATION

Libraries

## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Import datasets used for processing or analyses;

# Set working directory
#setwd("C:/Users/Dani Grant/OneDrive/Desktop") # Dani
setwd("~/Documents/COVID Studies/NSF Rapid Study/Wave2_US") # Alex

# Import dataset
d <- read.csv("Covid-19_NSF_RAPID_US_Wave2_Cleaned.csv", header = T, stringsAsFactors = F) 

d <- d[d$election_timing != 'Pre-election',] # Exclude Pre-election

d.parties <- d[d$party_factor != "Independent",]

# d.ex <- read.csv("Covid-19_NSF_RAPID_US_Wave2_Cleaned_nov8.excl.csv", header = T, stringsAsFactors = F) # excluding Nov. 8th people

Codes and variable construction

###################
# codes for 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$partyCont == -1 | d$partyCont == -2 | d$partyCont == -3] <- 'Democrat'
d$party_factor[d$partyCont == 0] <- 'Independent'
d$party_factor[d$partyCont == 1 | d$partyCont == 2 | d$partyCont == 3] <- 'Republican'

## Order of party variable
d$party_factor <- factor(d$party_factor, levels = c('Democrat', 'Republican','Independent'))


### dummy and contrast codes for party

## Contrast codes

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 

### democrat
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

### republican
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

### independent
d$pIndD[d$party_factor == 'Democrat'] <- 1
d$pIndD[d$party_factor == 'Republican'] <- 0
d$pIndD[d$party_factor == 'Independent'] <- 0

d$pIndR[d$party_factor == 'Democrat'] <- 0
d$pIndR[d$party_factor == 'Republican'] <- 1
d$pIndR[d$party_factor == 'Independent'] <- 0


## Partisan Identity Strength

# Z-scoring partisan importance
d$partyImp.z <- ifelse(d$party_factor == "Independent", NA,
                    scale(d$partyImp[d$party_factor != "Independent"]))

# Z-scoring partisan identity
d$partyExt.z <- NA
d$partyCont.abs <- abs(d$partyCont)

d$partyExt.z <- ifelse(d$party_factor == "Independent", NA,
                    scale(d$partyCont.abs[d$party_factor != "Independent"]))

# Combined measure
d$IDstrength <- NA
d$IDstrength <- rowMeans(d[,c('partyImp.z','partyExt.z')], na.rm = T)


### Political ideology
d$polIdeology <- NA
d$polIdeology <- rowMeans(d[,c('symbolic_beliefs_1', 'symbolic_beliefs_2', 'symbolic_beliefs_3')], na.rm = T)

# reliability
psych::alpha(d[,c('symbolic_beliefs_1', 'symbolic_beliefs_2', 'symbolic_beliefs_3')]) # alpha = .95
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("symbolic_beliefs_1", "symbolic_beliefs_2", 
##     "symbolic_beliefs_3")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.95      0.95    0.94      0.86  18 0.0027 0.17 1.7     0.88
## 
##  lower alpha upper     95% confidence boundaries
## 0.94 0.95 0.95 
## 
##  Reliability if an item is dropped:
##                    raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r
## symbolic_beliefs_1      0.88      0.88    0.79      0.79  7.6   0.0066    NA
## symbolic_beliefs_2      0.94      0.94    0.88      0.88 14.8   0.0036    NA
## symbolic_beliefs_3      0.95      0.95    0.91      0.91 19.6   0.0028    NA
##                    med.r
## symbolic_beliefs_1  0.79
## symbolic_beliefs_2  0.88
## symbolic_beliefs_3  0.91
## 
##  Item statistics 
##                       n raw.r std.r r.cor r.drop  mean  sd
## symbolic_beliefs_1 1225  0.98  0.98  0.97   0.94  0.14 1.7
## symbolic_beliefs_2 1224  0.95  0.95  0.91   0.88 -0.02 1.8
## symbolic_beliefs_3 1226  0.93  0.94  0.89   0.86  0.40 1.7
## 
## Non missing response frequency for each item
##                      -3   -2   -1    0    1    2    3 miss
## symbolic_beliefs_1 0.08 0.12 0.07 0.39 0.09 0.14 0.11 0.01
## symbolic_beliefs_2 0.11 0.14 0.10 0.32 0.08 0.13 0.11 0.01
## symbolic_beliefs_3 0.06 0.10 0.08 0.33 0.12 0.17 0.13 0.01
########################
# election timing codes
########################

## Order of timing var
d$election_timing <- factor(d$election_timing, levels = c('During-election','Post-election'))

### Timing codes
## Contrast
d$tDur_Post <- NA
d$tDur_Post[d$election_timing == 'During-election'] <- -.5
d$tDur_Post[d$election_timing == 'Post-election'] <- .5

## Dummy

# During
d$tDur <- NA
d$tDur[d$election_timing == 'During-election'] <- 0
d$tDur[d$election_timing == 'Post-election'] <- 1

# Post
d$tPost <- NA
d$tPost[d$election_timing == 'During-election'] <- 1
d$tPost[d$election_timing == 'Post-election'] <- 0


##################
# Media measures
##################

# Media Measures - All, including Fox
d$allMediaExposure <- rowMeans(d[,c("mediaExposure_1",
                                     "mediaExposure_2",
                                     "mediaExposure_3",
                                     "mediaExposure_4",
                                     "mediaExposure_5",
                                     "mediaExposure_6",
                                     "mediaExposure_7",
                                     "mediaExposure_8",
                                     "mediaExposure_9",
                                     "mediaExposure_10",
                                     "mediaExposure_11",
                                     "mediaExposure_12",
                                     "mediaExposure_13",
                                     "mediaExposure_14",
                                     "mediaExposure_15")], na.rm = T)


d$allMediaTrust <- rowMeans(d[,c("mediaTrust_1",
                                  "mediaTrust_2",
                                  "mediaTrust_3",
                                  "mediaTrust_4",
                                  "mediaTrust_5",
                                  "mediaTrust_6",
                                  "mediaTrust_7",
                                  "mediaTrust_8",
                                  "mediaTrust_9",
                                  "mediaTrust_10",
                                  "mediaTrust_11",
                                  "mediaTrust_12",
                                  "mediaTrust_13",
                                  "mediaTrust_14",
                                  "mediaTrust_15")], na.rm = T)


# Media Measures - Excluding Fox
d$otherMediaExposure <- rowMeans(d[,c("mediaExposure_1",
                                 "mediaExposure_2",
                                 "mediaExposure_3",
                                 "mediaExposure_4",
                                 
                                 "mediaExposure_6",
                                 "mediaExposure_7",
                                 "mediaExposure_8",
                                 "mediaExposure_9",
                                 "mediaExposure_10",
                                 "mediaExposure_11",
                                 "mediaExposure_12",
                                 "mediaExposure_13",
                                 "mediaExposure_14",
                                 "mediaExposure_15")], na.rm = T)



d$otherMediaTrust <- rowMeans(d[,c("mediaTrust_1",
                              "mediaTrust_2",
                              "mediaTrust_3",
                              "mediaTrust_4",
                              
                              "mediaTrust_6",
                              "mediaTrust_7",
                              "mediaTrust_8",
                              "mediaTrust_9",
                              "mediaTrust_10",
                              "mediaTrust_11",
                              "mediaTrust_12",
                              "mediaTrust_13",
                              "mediaTrust_14",
                              "mediaTrust_15")], na.rm = T)


## recenter media/fox trust to match media/fox exposure scale

d$foxTrust <- d$mediaTrust_5 + 3
d$foxExposure <- d$mediaExposure_5
d$foxPerception <- (d$foxExposure + d$foxTrust)/2


d$otherMediaTrust <- d$otherMediaTrust + 3
d$otherMediaPerception <- (d$otherMediaExposure + d$otherMediaTrust)/2


## mean center measures

d$foxExposure.c <- d$foxExposure - mean(d$foxExposure, na.rm = T)

d$otherMediaExposure.c <- d$otherMediaExposure - mean(d$otherMediaExposure, na.rm = T)

d$foxPerception.c <- d$foxPerception - mean(d$foxPerception, na.rm = T)

d$otherMediaPerception.c <- d$otherMediaExposure - mean(d$otherMediaExposure, na.rm = T)


## Composite measure: Fox perception - Other perception
d$FmO_MediaPerception <- NA
d$FmO_MediaPerception <- d$foxPerception - d$otherMediaPerception

############################
## vote legitimacy measure
############################

d$voteLegit <- (d$ownvote_conf + d$overallvote_conf)/2

# For use in repeated measures ANOVA type analyses
d$Own_Nat_conf_diff <- d$ownvote_conf - d$overallvote_conf
psych::describe(d$Own_Nat_conf_diff)
##    vars    n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 1208 0.39 0.98      0    0.24   0  -4   4     8  1.4     3.45 0.03
### centering
d$ownvote.c <- d$ownvote_conf - mean(d$ownvote_conf, na.rm = T)
d$overallvote.c <- d$overallvote_conf - mean(d$overallvote_conf, na.rm = T)
d$voteLegit.c <- d$voteLegit - mean(d$voteLegit, na.rm = T)


########################
# expected win measure
########################

#reverse code biden to trump rating win --> trump definitely win = 1; biden definitely win = 9
d$electPredict_T_B2 <- 10 - d$electPredict_B_T
d$electPredict_T_B3 <- 10 - d$electPredict_B_T.1

#combine to make 1 column
d$electPredictTB <- ifelse(!is.na(d$electPredict_T_B), d$electPredict_T_B, 
                           ifelse(!is.na(d$electPredict_T_B2), d$electPredict_T_B2, 
                                  ifelse(!is.na(d$electPredict_T_B3), d$electPredict_T_B3, NA)))

d$electPredictTB.plot <- d$electPredictTB - 5 

####################
# Emotion measures
####################

# Emotions:
# Anger (1) 
# Guilt (2) 
# Shame (3) 
# Pride (4) 
# Gratitude (5) 
# Hope (6) 
# Happiness (7) 
# Embarrassment (8) 
# Nervousness (9) 
# Distress (10) 
# Excitement (11) 
# Irritability (12)

d$positive <- rowMeans(d[,c('emotion_4', 'emotion_5', 'emotion_6', 'emotion_7', 'emotion_11')], na.rm = T)
d$positive.c <- d$positive - mean(d$positive, na.rm = T)


d$negative <- rowMeans(d[,c("emotion_1", "emotion_2", "emotion_3", "emotion_8", "emotion_9", "emotion_10", "emotion_12")], na.rm = T)
d$negative.c <- d$negative - mean(d$negative, na.rm = T)

# overall emotions
d$emotions <- NA
d$emotions <- d$positive - d$negative

Alphas and correlations

# Vote Confidence measures
psych::alpha(d[,c("ownvote_conf",
                  "overallvote_conf")]) # alpha = .86
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("ownvote_conf", "overallvote_conf")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.86      0.86    0.75      0.75   6 0.0082  3.5 1.3     0.75
## 
##  lower alpha upper     95% confidence boundaries
## 0.84 0.86 0.87 
## 
##  Reliability if an item is dropped:
##                  raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## ownvote_conf          0.71      0.75    0.56      0.75   3       NA     0  0.75
## overallvote_conf      0.79      0.75    0.56      0.75   3       NA     0  0.75
## 
##  Item statistics 
##                     n raw.r std.r r.cor r.drop mean  sd
## ownvote_conf     1209  0.93  0.94  0.81   0.75  3.7 1.3
## overallvote_conf 1208  0.94  0.94  0.81   0.75  3.3 1.4
## 
## Non missing response frequency for each item
##                     1    2    3    4    5 miss
## ownvote_conf     0.11 0.10 0.18 0.23 0.38 0.02
## overallvote_conf 0.16 0.16 0.19 0.22 0.28 0.02
cor.test(d$ownvote_conf,d$overallvote_conf) # r = .75
## 
##  Pearson's product-moment correlation
## 
## data:  d$ownvote_conf and d$overallvote_conf
## t = 39.45, df = 1206, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7248916 0.7742292
## sample estimates:
##       cor 
## 0.7506048
# Media Measures

## Fox News
psych::alpha(d[,c("foxExposure",
                  "foxTrust")]) # alpha = .81
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("foxExposure", "foxTrust")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##        0.8       0.8    0.67      0.67 4.1 0.011  2.4 1.2     0.67
## 
##  lower alpha upper     95% confidence boundaries
## 0.78 0.8 0.82 
## 
##  Reliability if an item is dropped:
##             raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## foxExposure      0.72      0.67    0.45      0.67   2       NA     0  0.67
## foxTrust         0.63      0.67    0.45      0.67   2       NA     0  0.67
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean  sd
## foxExposure 1214  0.92  0.91  0.75   0.67  2.2 1.4
## foxTrust    1213  0.91  0.91  0.75   0.67  2.6 1.3
## 
## Non missing response frequency for each item
##                1    2    3    4    5 miss
## foxExposure 0.48 0.17 0.14 0.11 0.11 0.02
## foxTrust    0.31 0.18 0.24 0.20 0.08 0.02
cor.test(d$foxExposure, d$foxTrust) # r = .67
## 
##  Pearson's product-moment correlation
## 
## data:  d$foxExposure and d$foxTrust
## t = 31.508, df = 1211, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6390341 0.7009824
## sample estimates:
##       cor 
## 0.6711784
## Exposure (Other sources)
psych::alpha(d[,c("mediaExposure_1","mediaExposure_2","mediaExposure_3","mediaExposure_4","mediaExposure_6","mediaExposure_7","mediaExposure_8","mediaExposure_9","mediaExposure_10","mediaExposure_11","mediaExposure_12","mediaExposure_13","mediaExposure_14","mediaExposure_15")]) # alpha = .93
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("mediaExposure_1", "mediaExposure_2", 
##     "mediaExposure_3", "mediaExposure_4", "mediaExposure_6", 
##     "mediaExposure_7", "mediaExposure_8", "mediaExposure_9", 
##     "mediaExposure_10", "mediaExposure_11", "mediaExposure_12", 
##     "mediaExposure_13", "mediaExposure_14", "mediaExposure_15")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.93      0.94    0.95      0.51  15 0.0027    2 0.91      0.5
## 
##  lower alpha upper     95% confidence boundaries
## 0.93 0.93 0.94 
## 
##  Reliability if an item is dropped:
##                  raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r
## mediaExposure_1       0.93      0.93    0.94      0.51  13   0.0030 0.0085
## mediaExposure_2       0.93      0.93    0.94      0.51  14   0.0030 0.0093
## mediaExposure_3       0.93      0.93    0.94      0.50  13   0.0031 0.0084
## mediaExposure_4       0.93      0.93    0.94      0.51  13   0.0030 0.0098
## mediaExposure_6       0.93      0.93    0.94      0.52  14   0.0029 0.0097
## mediaExposure_7       0.93      0.93    0.94      0.51  13   0.0030 0.0103
## mediaExposure_8       0.93      0.93    0.94      0.52  14   0.0029 0.0098
## mediaExposure_9       0.93      0.93    0.94      0.50  13   0.0030 0.0090
## mediaExposure_10      0.93      0.93    0.94      0.52  14   0.0029 0.0099
## mediaExposure_11      0.93      0.93    0.94      0.52  14   0.0029 0.0084
## mediaExposure_12      0.93      0.93    0.94      0.52  14   0.0029 0.0089
## mediaExposure_13      0.93      0.93    0.94      0.51  14   0.0030 0.0091
## mediaExposure_14      0.93      0.93    0.94      0.52  14   0.0029 0.0090
## mediaExposure_15      0.93      0.93    0.94      0.51  13   0.0030 0.0099
##                  med.r
## mediaExposure_1   0.49
## mediaExposure_2   0.49
## mediaExposure_3   0.48
## mediaExposure_4   0.49
## mediaExposure_6   0.50
## mediaExposure_7   0.48
## mediaExposure_8   0.50
## mediaExposure_9   0.49
## mediaExposure_10  0.50
## mediaExposure_11  0.50
## mediaExposure_12  0.50
## mediaExposure_13  0.50
## mediaExposure_14  0.50
## mediaExposure_15  0.48
## 
##  Item statistics 
##                     n raw.r std.r r.cor r.drop mean   sd
## mediaExposure_1  1213  0.78  0.78  0.77   0.74  1.9 1.26
## mediaExposure_2  1213  0.73  0.75  0.72   0.69  1.8 1.14
## mediaExposure_3  1213  0.81  0.81  0.81   0.77  1.9 1.22
## mediaExposure_4  1213  0.77  0.78  0.76   0.73  1.7 1.13
## mediaExposure_6  1212  0.71  0.69  0.66   0.65  2.4 1.51
## mediaExposure_7  1213  0.77  0.77  0.75   0.73  2.0 1.33
## mediaExposure_8  1214  0.66  0.66  0.62   0.60  1.9 1.19
## mediaExposure_9  1213  0.79  0.81  0.79   0.76  1.7 1.09
## mediaExposure_10 1213  0.69  0.70  0.67   0.64  1.5 0.97
## mediaExposure_11 1214  0.70  0.70  0.68   0.64  1.8 1.20
## mediaExposure_12 1213  0.71  0.69  0.67   0.65  2.4 1.37
## mediaExposure_13 1213  0.74  0.73  0.71   0.69  2.4 1.36
## mediaExposure_14 1213  0.71  0.70  0.68   0.65  2.4 1.34
## mediaExposure_15 1213  0.77  0.78  0.76   0.73  1.8 1.20
## 
## Non missing response frequency for each item
##                  -2 -1 0    1    2    3    4    5 miss
## mediaExposure_1   0  0 0 0.56 0.16 0.13 0.08 0.06 0.02
## mediaExposure_2   0  0 0 0.61 0.17 0.11 0.08 0.04 0.02
## mediaExposure_3   0  0 0 0.58 0.17 0.12 0.07 0.06 0.02
## mediaExposure_4   0  0 0 0.61 0.18 0.11 0.06 0.04 0.02
## mediaExposure_6   0  0 0 0.43 0.15 0.13 0.14 0.15 0.02
## mediaExposure_7   0  0 0 0.56 0.16 0.10 0.11 0.08 0.02
## mediaExposure_8   0  0 0 0.56 0.17 0.14 0.09 0.04 0.02
## mediaExposure_9   0  0 0 0.66 0.15 0.09 0.07 0.03 0.02
## mediaExposure_10  0  0 0 0.77 0.09 0.07 0.04 0.02 0.02
## mediaExposure_11  0  0 0 0.63 0.14 0.12 0.06 0.06 0.02
## mediaExposure_12  0  0 0 0.37 0.21 0.17 0.15 0.10 0.02
## mediaExposure_13  0  0 0 0.37 0.22 0.16 0.15 0.09 0.02
## mediaExposure_14  0  0 0 0.36 0.23 0.17 0.14 0.09 0.02
## mediaExposure_15  0  0 0 0.61 0.14 0.12 0.08 0.05 0.02
## Trust (Other sources)
psych::alpha(d[,c("mediaTrust_1","mediaTrust_2","mediaTrust_3","mediaTrust_4","mediaTrust_6","mediaTrust_7","mediaTrust_8","mediaTrust_9","mediaTrust_10","mediaTrust_11","mediaTrust_12","mediaTrust_13","mediaTrust_14","mediaTrust_15")]) # alpha = .97
## Number of categories should be increased  in order to count frequencies.
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("mediaTrust_1", "mediaTrust_2", "mediaTrust_3", 
##     "mediaTrust_4", "mediaTrust_6", "mediaTrust_7", "mediaTrust_8", 
##     "mediaTrust_9", "mediaTrust_10", "mediaTrust_11", "mediaTrust_12", 
##     "mediaTrust_13", "mediaTrust_14", "mediaTrust_15")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase  mean sd median_r
##       0.97      0.97    0.97      0.69  31 0.0013 -0.04  1     0.69
## 
##  lower alpha upper     95% confidence boundaries
## 0.97 0.97 0.97 
## 
##  Reliability if an item is dropped:
##               raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## mediaTrust_1       0.97      0.97    0.97      0.68  28   0.0014 0.0055  0.69
## mediaTrust_2       0.97      0.97    0.97      0.71  31   0.0013 0.0044  0.70
## mediaTrust_3       0.97      0.97    0.97      0.68  28   0.0014 0.0057  0.69
## mediaTrust_4       0.97      0.97    0.97      0.69  29   0.0014 0.0060  0.69
## mediaTrust_6       0.97      0.97    0.97      0.69  29   0.0014 0.0051  0.69
## mediaTrust_7       0.97      0.97    0.97      0.69  28   0.0014 0.0053  0.69
## mediaTrust_8       0.97      0.97    0.97      0.70  30   0.0013 0.0056  0.70
## mediaTrust_9       0.97      0.97    0.97      0.69  29   0.0014 0.0060  0.69
## mediaTrust_10      0.97      0.97    0.97      0.70  30   0.0013 0.0055  0.70
## mediaTrust_11      0.97      0.97    0.97      0.70  30   0.0013 0.0052  0.70
## mediaTrust_12      0.97      0.97    0.97      0.68  28   0.0014 0.0048  0.69
## mediaTrust_13      0.97      0.97    0.97      0.68  28   0.0014 0.0046  0.69
## mediaTrust_14      0.97      0.97    0.97      0.68  28   0.0014 0.0050  0.69
## mediaTrust_15      0.97      0.97    0.97      0.69  29   0.0014 0.0059  0.69
## 
##  Item statistics 
##                  n raw.r std.r r.cor r.drop   mean  sd
## mediaTrust_1  1212  0.88  0.88  0.87   0.86 -0.020 1.3
## mediaTrust_2  1212  0.74  0.74  0.71   0.70  0.036 1.1
## mediaTrust_3  1212  0.87  0.87  0.87   0.85 -0.052 1.2
## mediaTrust_4  1212  0.86  0.86  0.85   0.83 -0.087 1.1
## mediaTrust_6  1213  0.86  0.86  0.85   0.83 -0.016 1.4
## mediaTrust_7  1212  0.87  0.87  0.86   0.85 -0.129 1.3
## mediaTrust_8  1213  0.80  0.81  0.79   0.77 -0.181 1.1
## mediaTrust_9  1212  0.84  0.84  0.83   0.81 -0.231 1.2
## mediaTrust_10 1213  0.78  0.79  0.77   0.75 -0.361 1.0
## mediaTrust_11 1212  0.78  0.78  0.76   0.74 -0.005 1.2
## mediaTrust_12 1212  0.89  0.89  0.89   0.87  0.111 1.3
## mediaTrust_13 1213  0.90  0.90  0.90   0.88  0.094 1.3
## mediaTrust_14 1212  0.89  0.88  0.88   0.87  0.136 1.3
## mediaTrust_15 1212  0.84  0.84  0.83   0.81  0.149 1.2
## Trust/consumption (other sources)
psych::alpha(d[,c("mediaTrust_1","mediaTrust_2","mediaTrust_3","mediaTrust_4","mediaTrust_6","mediaTrust_7","mediaTrust_8","mediaTrust_9","mediaTrust_10","mediaTrust_11","mediaTrust_12","mediaTrust_13","mediaTrust_14","mediaTrust_15","mediaExposure_1","mediaExposure_2","mediaExposure_3","mediaExposure_4","mediaExposure_6","mediaExposure_7","mediaExposure_8","mediaExposure_9","mediaExposure_10","mediaExposure_11","mediaExposure_12","mediaExposure_13","mediaExposure_14","mediaExposure_15")]) # alpha = .96
## Number of categories should be increased  in order to count frequencies.
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("mediaTrust_1", "mediaTrust_2", "mediaTrust_3", 
##     "mediaTrust_4", "mediaTrust_6", "mediaTrust_7", "mediaTrust_8", 
##     "mediaTrust_9", "mediaTrust_10", "mediaTrust_11", "mediaTrust_12", 
##     "mediaTrust_13", "mediaTrust_14", "mediaTrust_15", "mediaExposure_1", 
##     "mediaExposure_2", "mediaExposure_3", "mediaExposure_4", 
##     "mediaExposure_6", "mediaExposure_7", "mediaExposure_8", 
##     "mediaExposure_9", "mediaExposure_10", "mediaExposure_11", 
##     "mediaExposure_12", "mediaExposure_13", "mediaExposure_14", 
##     "mediaExposure_15")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
##       0.96      0.96    0.98      0.47  25 0.0016 0.96 0.86     0.45
## 
##  lower alpha upper     95% confidence boundaries
## 0.96 0.96 0.96 
## 
##  Reliability if an item is dropped:
##                  raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## mediaTrust_1          0.96      0.96    0.98      0.46  23   0.0017 0.027  0.44
## mediaTrust_2          0.96      0.96    0.98      0.47  24   0.0016 0.028  0.45
## mediaTrust_3          0.96      0.96    0.98      0.46  23   0.0017 0.027  0.44
## mediaTrust_4          0.96      0.96    0.98      0.47  24   0.0017 0.027  0.45
## mediaTrust_6          0.96      0.96    0.98      0.47  24   0.0017 0.027  0.44
## mediaTrust_7          0.96      0.96    0.98      0.47  23   0.0017 0.027  0.44
## mediaTrust_8          0.96      0.96    0.98      0.47  24   0.0017 0.028  0.45
## mediaTrust_9          0.96      0.96    0.98      0.47  24   0.0017 0.027  0.45
## mediaTrust_10         0.96      0.96    0.98      0.47  24   0.0017 0.028  0.45
## mediaTrust_11         0.96      0.96    0.98      0.47  24   0.0017 0.027  0.45
## mediaTrust_12         0.96      0.96    0.98      0.47  24   0.0017 0.026  0.44
## mediaTrust_13         0.96      0.96    0.98      0.46  23   0.0017 0.026  0.44
## mediaTrust_14         0.96      0.96    0.98      0.47  24   0.0017 0.026  0.44
## mediaTrust_15         0.96      0.96    0.98      0.47  24   0.0017 0.027  0.45
## mediaExposure_1       0.96      0.96    0.98      0.47  24   0.0016 0.029  0.45
## mediaExposure_2       0.96      0.96    0.98      0.48  25   0.0016 0.027  0.45
## mediaExposure_3       0.96      0.96    0.98      0.47  24   0.0016 0.029  0.45
## mediaExposure_4       0.96      0.96    0.98      0.47  24   0.0016 0.028  0.45
## mediaExposure_6       0.96      0.96    0.98      0.47  24   0.0017 0.029  0.44
## mediaExposure_7       0.96      0.96    0.98      0.47  24   0.0017 0.029  0.44
## mediaExposure_8       0.96      0.96    0.98      0.48  25   0.0016 0.028  0.46
## mediaExposure_9       0.96      0.96    0.98      0.47  24   0.0016 0.028  0.45
## mediaExposure_10      0.96      0.96    0.98      0.48  25   0.0016 0.027  0.45
## mediaExposure_11      0.96      0.96    0.98      0.48  25   0.0016 0.028  0.45
## mediaExposure_12      0.96      0.96    0.98      0.47  24   0.0016 0.029  0.45
## mediaExposure_13      0.96      0.96    0.98      0.47  24   0.0017 0.029  0.45
## mediaExposure_14      0.96      0.96    0.98      0.47  24   0.0016 0.029  0.45
## mediaExposure_15      0.96      0.96    0.98      0.47  24   0.0016 0.029  0.45
## 
##  Item statistics 
##                     n raw.r std.r r.cor r.drop   mean   sd
## mediaTrust_1     1212  0.81  0.81  0.80   0.79 -0.020 1.27
## mediaTrust_2     1212  0.65  0.66  0.64   0.62  0.036 1.14
## mediaTrust_3     1212  0.81  0.81  0.80   0.79 -0.052 1.24
## mediaTrust_4     1212  0.77  0.78  0.77   0.76 -0.087 1.13
## mediaTrust_6     1213  0.79  0.78  0.78   0.77 -0.016 1.39
## mediaTrust_7     1212  0.80  0.79  0.79   0.78 -0.129 1.28
## mediaTrust_8     1213  0.73  0.73  0.73   0.71 -0.181 1.10
## mediaTrust_9     1212  0.77  0.77  0.76   0.75 -0.231 1.15
## mediaTrust_10    1213  0.69  0.70  0.69   0.67 -0.361 1.04
## mediaTrust_11    1212  0.68  0.68  0.68   0.66 -0.005 1.24
## mediaTrust_12    1212  0.79  0.78  0.78   0.77  0.111 1.28
## mediaTrust_13    1213  0.81  0.80  0.80   0.79  0.094 1.27
## mediaTrust_14    1212  0.78  0.78  0.78   0.76  0.136 1.26
## mediaTrust_15    1212  0.75  0.75  0.74   0.73  0.149 1.21
## mediaExposure_1  1213  0.67  0.67  0.66   0.64  1.917 1.26
## mediaExposure_2  1213  0.56  0.57  0.56   0.53  1.763 1.14
## mediaExposure_3  1213  0.69  0.69  0.69   0.66  1.857 1.22
## mediaExposure_4  1213  0.61  0.62  0.61   0.58  1.747 1.13
## mediaExposure_6  1212  0.72  0.71  0.70   0.68  2.421 1.51
## mediaExposure_7  1213  0.70  0.70  0.69   0.67  1.984 1.33
## mediaExposure_8  1214  0.54  0.55  0.52   0.50  1.879 1.19
## mediaExposure_9  1213  0.65  0.66  0.65   0.62  1.657 1.09
## mediaExposure_10 1213  0.53  0.54  0.52   0.50  1.455 0.97
## mediaExposure_11 1214  0.58  0.59  0.58   0.55  1.766 1.20
## mediaExposure_12 1213  0.68  0.68  0.67   0.65  2.406 1.37
## mediaExposure_13 1213  0.70  0.69  0.68   0.67  2.383 1.36
## mediaExposure_14 1213  0.67  0.66  0.65   0.64  2.370 1.34
## mediaExposure_15 1213  0.64  0.64  0.63   0.61  1.817 1.20
## Other media sources (composite)
psych::alpha(d[,c("otherMediaExposure",
                  "otherMediaTrust")]) # alpha = .72
## Number of categories should be increased  in order to count frequencies.
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("otherMediaExposure", "otherMediaTrust")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.73      0.73    0.57      0.57 2.7 0.015  2.5 0.86     0.57
## 
##  lower alpha upper     95% confidence boundaries
## 0.7 0.73 0.76 
## 
##  Reliability if an item is dropped:
##                    raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## otherMediaExposure      0.51      0.57    0.33      0.57 1.3       NA     0
## otherMediaTrust         0.64      0.57    0.33      0.57 1.3       NA     0
##                    med.r
## otherMediaExposure  0.57
## otherMediaTrust     0.57
## 
##  Item statistics 
##                       n raw.r std.r r.cor r.drop mean   sd
## otherMediaExposure 1214  0.87  0.89  0.67   0.57    2 0.91
## otherMediaTrust    1213  0.90  0.89  0.67   0.57    3 1.03
cor.test(d$otherMediaExposure, d$otherMediaTrust) # r = .57
## 
##  Pearson's product-moment correlation
## 
## data:  d$otherMediaExposure and d$otherMediaTrust
## t = 24.346, df = 1211, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.534202 0.609859
## sample estimates:
##       cor 
## 0.5732511
## All media sources (composite, includes Fox and other media sources)?
psych::alpha(d[,c("allMediaTrust",
                  "allMediaExposure")]) # alpha = .70
## Number of categories should be increased  in order to count frequencies.
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("allMediaTrust", "allMediaExposure")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##        0.7      0.71    0.55      0.55 2.4 0.017 0.96 0.81     0.55
## 
##  lower alpha upper     95% confidence boundaries
## 0.67 0.7 0.74 
## 
##  Reliability if an item is dropped:
##                  raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## allMediaTrust          0.6      0.55     0.3      0.55 1.2       NA     0  0.55
## allMediaExposure       0.5      0.55     0.3      0.55 1.2       NA     0  0.55
## 
##  Item statistics 
##                     n raw.r std.r r.cor r.drop   mean   sd
## allMediaTrust    1213  0.89  0.88  0.65   0.55 -0.066 0.96
## allMediaExposure 1214  0.87  0.88  0.65   0.55  1.975 0.88
cor.test(d$allMediaTrust, d$allMediaExposure) # r = .54
## 
##  Pearson's product-moment correlation
## 
## data:  d$allMediaTrust and d$allMediaExposure
## t = 22.703, df = 1211, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5056712 0.5847079
## sample estimates:
##       cor 
## 0.5464049
## Correlation of Fox and Other
cor.test(d$otherMediaPerception, d$foxPerception)
## 
##  Pearson's product-moment correlation
## 
## data:  d$otherMediaPerception and d$foxPerception
## t = 0.19238, df = 1211, p-value = 0.8475
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05077303  0.06179444
## sample estimates:
##         cor 
## 0.005528217
# Dems
cor.test(d[d$party_factor == "Democrat",]$otherMediaPerception, d[d$party_factor == "Democrat",]$foxPerception)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Democrat", ]$otherMediaPerception and d[d$party_factor == "Democrat", ]$foxPerception
## t = 3.4603, df = 556, p-value = 0.0005811
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06294656 0.22547940
## sample estimates:
##       cor 
## 0.1451924
# Reps
cor.test(d[d$party_factor == "Republican",]$otherMediaPerception, d[d$party_factor == "Republican",]$foxPerception)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Republican", ]$otherMediaPerception and d[d$party_factor == "Republican", ]$foxPerception
## t = 4.3053, df = 462, p-value = 2.038e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1072839 0.2823811
## sample estimates:
##       cor 
## 0.1963978
# Ind
cor.test(d[d$party_factor == "Independent",]$otherMediaPerception, d[d$party_factor == "Independent",]$foxPerception)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Independent", ]$otherMediaPerception and d[d$party_factor == "Independent", ]$foxPerception
## t = 7.4811, df = 188, p-value = 2.743e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3612391 0.5816573
## sample estimates:
##       cor 
## 0.4789625
# Emotions
## positive 
psych::alpha(d[,c("emotion_4",
                  "emotion_5",
                  "emotion_6",
                  "emotion_7",
                  "emotion_11")])  # alpha is .93
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("emotion_4", "emotion_5", "emotion_6", 
##     "emotion_7", "emotion_11")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.93      0.93    0.92      0.73  14 0.003  3.3 1.8     0.73
## 
##  lower alpha upper     95% confidence boundaries
## 0.93 0.93 0.94 
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r  S/N alpha se   var.r med.r
## emotion_4       0.92      0.92    0.90      0.74 11.5   0.0038 0.00253  0.73
## emotion_5       0.92      0.92    0.89      0.73 10.8   0.0040 0.00140  0.72
## emotion_6       0.93      0.93    0.91      0.76 12.5   0.0035 0.00132  0.74
## emotion_7       0.91      0.91    0.88      0.71  9.9   0.0043 0.00046  0.71
## emotion_11      0.92      0.92    0.89      0.73 11.0   0.0039 0.00217  0.73
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## emotion_4  1213  0.88  0.88  0.83   0.81  3.1 2.1
## emotion_5  1215  0.90  0.89  0.86   0.83  3.1 2.1
## emotion_6  1214  0.86  0.86  0.80   0.77  3.9 2.1
## emotion_7  1214  0.92  0.92  0.90   0.87  3.1 2.0
## emotion_11 1215  0.89  0.89  0.86   0.83  3.2 2.1
## 
## Non missing response frequency for each item
##               1    2    3    4    5    6    7 miss
## emotion_4  0.36 0.11 0.10 0.15 0.09 0.08 0.10 0.02
## emotion_5  0.38 0.11 0.10 0.14 0.09 0.08 0.10 0.02
## emotion_6  0.18 0.13 0.11 0.17 0.13 0.12 0.16 0.02
## emotion_7  0.35 0.13 0.11 0.16 0.08 0.07 0.10 0.02
## emotion_11 0.33 0.13 0.10 0.17 0.09 0.09 0.10 0.02
## negative
psych::alpha(d[,c("emotion_1",
                  "emotion_2",
                  "emotion_3",
                  "emotion_8",
                  "emotion_9",
                  "emotion_10",
                  "emotion_12")])  # alpha is .88
## 
## Reliability analysis   
## Call: psych::alpha(x = d[, c("emotion_1", "emotion_2", "emotion_3", 
##     "emotion_8", "emotion_9", "emotion_10", "emotion_12")])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.88      0.87    0.88      0.49 6.7 0.0051  2.9 1.5     0.51
## 
##  lower alpha upper     95% confidence boundaries
## 0.87 0.88 0.89 
## 
##  Reliability if an item is dropped:
##            raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## emotion_1       0.85      0.84    0.85      0.47 5.3   0.0064 0.024  0.50
## emotion_2       0.89      0.89    0.89      0.57 7.9   0.0051 0.011  0.52
## emotion_3       0.86      0.85    0.85      0.48 5.6   0.0058 0.030  0.51
## emotion_8       0.86      0.85    0.85      0.49 5.7   0.0058 0.029  0.50
## emotion_9       0.86      0.85    0.86      0.49 5.9   0.0058 0.025  0.51
## emotion_10      0.84      0.84    0.84      0.46 5.2   0.0065 0.023  0.50
## emotion_12      0.85      0.84    0.85      0.47 5.3   0.0064 0.024  0.50
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## emotion_1  1215  0.83  0.82  0.79   0.74  3.2 2.1
## emotion_2  1215  0.48  0.53  0.40   0.38  1.6 1.3
## emotion_3  1215  0.77  0.77  0.74   0.67  2.5 2.0
## emotion_8  1215  0.77  0.76  0.72   0.66  2.9 2.2
## emotion_9  1215  0.75  0.74  0.69   0.64  3.7 2.1
## emotion_10 1215  0.84  0.83  0.81   0.76  3.3 2.0
## emotion_12 1214  0.82  0.81  0.78   0.73  3.4 2.1
## 
## Non missing response frequency for each item
##               1    2    3    4    5    6    7 miss
## emotion_1  0.31 0.16 0.10 0.15 0.09 0.08 0.11 0.02
## emotion_2  0.79 0.07 0.04 0.06 0.02 0.01 0.02 0.02
## emotion_3  0.54 0.10 0.07 0.10 0.06 0.06 0.08 0.02
## emotion_8  0.45 0.11 0.08 0.11 0.07 0.07 0.12 0.02
## emotion_9  0.23 0.13 0.13 0.16 0.12 0.09 0.14 0.02
## emotion_10 0.29 0.15 0.13 0.15 0.10 0.08 0.10 0.02
## emotion_12 0.27 0.13 0.13 0.16 0.11 0.09 0.11 0.02

w/in participant media correlation

Probing Media Measures…

medper <- lm(foxPerception ~ otherMediaPerception, data = d)
summary(medper)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3955 -1.3647 -0.3727  1.1137  2.6353 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.356720   0.108049  21.812   <2e-16 ***
## otherMediaPerception 0.007977   0.041463   0.192    0.847    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.243 on 1211 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  3.056e-05,  Adjusted R-squared:  -0.0007952 
## F-statistic: 0.03701 on 1 and 1211 DF,  p-value: 0.8475
mediaparty <- lm(foxPerception ~ otherMediaPerception * (pDem_Rep + pInd_Not), data = d)
summary(mediaparty)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception * (pDem_Rep + 
##     pInd_Not), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5482 -0.9271 -0.1763  0.7846  3.3627 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    1.47433    0.11951  12.337  < 2e-16 ***
## otherMediaPerception           0.37429    0.04670   8.015 2.57e-15 ***
## pDem_Rep                       1.08546    0.25132   4.319 1.70e-05 ***
## pInd_Not                       1.03812    0.28609   3.629 0.000297 ***
## otherMediaPerception:pDem_Rep  0.05987    0.09495   0.631 0.528414    
## otherMediaPerception:pInd_Not -0.31641    0.11396  -2.776 0.005581 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.125 on 1206 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1832, Adjusted R-squared:  0.1798 
## F-statistic: 54.11 on 5 and 1206 DF,  p-value: < 2.2e-16
mediaparty.d <- lm(foxPerception ~ otherMediaPerception * (pDemR + pDemI), data = d)
summary(mediaparty.d)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception * (pDemR + 
##     pDemI), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5482 -0.9271 -0.1763  0.7846  3.3627 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 1.27418    0.21240   5.999 2.62e-09 ***
## otherMediaPerception        0.23994    0.07170   3.346 0.000844 ***
## pDemR                       1.08546    0.25132   4.319 1.70e-05 ***
## pDemI                      -0.49539    0.33342  -1.486 0.137601    
## otherMediaPerception:pDemR  0.05987    0.09495   0.631 0.528414    
## otherMediaPerception:pDemI  0.34635    0.12600   2.749 0.006069 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.125 on 1206 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1832, Adjusted R-squared:  0.1798 
## F-statistic: 54.11 on 5 and 1206 DF,  p-value: < 2.2e-16
mediaparty.r <- lm(foxPerception ~ otherMediaPerception * (pRepD + pRepI), data = d)
summary(mediaparty.r)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception * (pRepD + 
##     pRepI), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5482 -0.9271 -0.1763  0.7846  3.3627 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 2.35964    0.13433  17.566  < 2e-16 ***
## otherMediaPerception        0.29981    0.06224   4.817 1.64e-06 ***
## pRepD                      -1.08546    0.25132  -4.319 1.70e-05 ***
## pRepI                      -1.58085    0.29000  -5.451 6.06e-08 ***
## otherMediaPerception:pRepD -0.05987    0.09495  -0.631   0.5284    
## otherMediaPerception:pRepI  0.28648    0.12086   2.370   0.0179 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.125 on 1206 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1832, Adjusted R-squared:  0.1798 
## F-statistic: 54.11 on 5 and 1206 DF,  p-value: < 2.2e-16
mediaparty.i <- lm(foxPerception ~ otherMediaPerception * (pIndD + pIndR), data = d)
summary(mediaparty.i)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception * (pIndD + 
##     pIndR), data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5482 -0.9271 -0.1763  0.7846  3.3627 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.7788     0.2570   3.030  0.00250 ** 
## otherMediaPerception         0.5863     0.1036   5.659 1.90e-08 ***
## pIndD                        0.4954     0.3334   1.486  0.13760    
## pIndR                        1.5809     0.2900   5.451 6.06e-08 ***
## otherMediaPerception:pIndD  -0.3463     0.1260  -2.749  0.00607 ** 
## otherMediaPerception:pIndR  -0.2865     0.1209  -2.370  0.01793 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.125 on 1206 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1832, Adjusted R-squared:  0.1798 
## F-statistic: 54.11 on 5 and 1206 DF,  p-value: < 2.2e-16
# Correlations 

## Trust
cor.test(d$otherMediaTrust, d$foxTrust)
## 
##  Pearson's product-moment correlation
## 
## data:  d$otherMediaTrust and d$foxTrust
## t = -0.99667, df = 1211, p-value = 0.3191
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08477754  0.02770139
## sample estimates:
##        cor 
## -0.0286287
# Dem
cor.test(d[d$party_factor == "Democrat",]$otherMediaTrust, d[d$party_factor == "Democrat",]$foxTrust)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Democrat", ]$otherMediaTrust and d[d$party_factor == "Democrat", ]$foxTrust
## t = 1.234, df = 556, p-value = 0.2177
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03087468  0.13468383
## sample estimates:
##        cor 
## 0.05226368
# Rep
cor.test(d[d$party_factor == "Republican",]$otherMediaTrust, d[d$party_factor == "Republican",]$foxTrust)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Republican", ]$otherMediaTrust and d[d$party_factor == "Republican", ]$foxTrust
## t = 4.9851, df = 462, p-value = 8.773e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1377340 0.3105773
## sample estimates:
##       cor 
## 0.2259331
# Ind
cor.test(d[d$party_factor == "Independent",]$otherMediaTrust, d[d$party_factor == "Independent",]$foxTrust)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Independent", ]$otherMediaTrust and d[d$party_factor == "Independent", ]$foxTrust
## t = 6.5711, df = 188, p-value = 4.774e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3088223 0.5412321
## sample estimates:
##       cor 
## 0.4321764
## Exposure
cor.test(d$otherMediaExposure, d$foxExposure)
## 
##  Pearson's product-moment correlation
## 
## data:  d$otherMediaExposure and d$foxExposure
## t = 7.7822, df = 1212, p-value = 1.519e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1639026 0.2710882
## sample estimates:
##       cor 
## 0.2181532
# Dem
cor.test(d[d$party_factor == "Democrat",]$otherMediaExposure, d[d$party_factor == "Democrat",]$foxExposure)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Democrat", ]$otherMediaExposure and d[d$party_factor == "Democrat", ]$foxExposure
## t = 8.6119, df = 557, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2674617 0.4139510
## sample estimates:
##       cor 
## 0.3427885
# Rep
cor.test(d[d$party_factor == "Republican",]$otherMediaExposure, d[d$party_factor == "Republican",]$foxExposure)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Republican", ]$otherMediaExposure and d[d$party_factor == "Republican", ]$foxExposure
## t = 6.3784, df = 462, p-value = 4.348e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1986014 0.3660423
## sample estimates:
##     cor 
## 0.28449
# Ind
cor.test(d[d$party_factor == "Independent",]$otherMediaExposure, d[d$party_factor == "Independent",]$foxExposure)
## 
##  Pearson's product-moment correlation
## 
## data:  d[d$party_factor == "Independent", ]$otherMediaExposure and d[d$party_factor == "Independent", ]$foxExposure
## t = 9.8501, df = 188, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4810432 0.6701383
## sample estimates:
##       cor 
## 0.5834434

Leaf Requests

fox.party <- lm(foxPerception ~ pDem_Rep + pInd_Not, data = d)
summary(fox.party)
## 
## Call:
## lm(formula = foxPerception ~ pDem_Rep + pInd_Not, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9558 -0.9668 -0.1579  1.0332  3.0332 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.36120    0.03680  64.163  < 2e-16 ***
## pDem_Rep     0.98897    0.07253  13.635  < 2e-16 ***
## pInd_Not     0.30344    0.09127   3.325 0.000911 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.154 on 1209 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1382, Adjusted R-squared:  0.1368 
## F-statistic: 96.96 on 2 and 1209 DF,  p-value: < 2.2e-16
fox.other <- lm(foxPerception ~ otherMediaPerception, data = d)
summary(fox.other)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3955 -1.3647 -0.3727  1.1137  2.6353 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.356720   0.108049  21.812   <2e-16 ***
## otherMediaPerception 0.007977   0.041463   0.192    0.847    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.243 on 1211 degrees of freedom
##   (23 observations deleted due to missingness)
## Multiple R-squared:  3.056e-05,  Adjusted R-squared:  -0.0007952 
## F-statistic: 0.03701 on 1 and 1211 DF,  p-value: 0.8475
fox.party.other <- lm(foxPerception ~ otherMediaPerception + pDem_Rep + pInd_Not, data = d)
summary(fox.party.other)
## 
## Call:
## lm(formula = foxPerception ~ otherMediaPerception + pDem_Rep + 
##     pInd_Not, data = d)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6026 -0.9221 -0.1882  0.8017  3.3936 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           1.57240    0.10946  14.365  < 2e-16 ***
## otherMediaPerception  0.32737    0.04291   7.630 4.75e-14 ***
## pDem_Rep              1.28306    0.08068  15.904  < 2e-16 ***
## pInd_Not              0.27548    0.08926   3.086  0.00207 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.128 on 1208 degrees of freedom
##   (24 observations deleted due to missingness)
## Multiple R-squared:  0.1778, Adjusted R-squared:  0.1758 
## F-statistic:  87.1 on 3 and 1208 DF,  p-value: < 2.2e-16