1. means polarization

describe(ds$media_1, na.rm = T)  #NTTimes = 3.78    
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 18 3.78 2.76      4    3.88 2.97  -2   8    10 -0.1    -0.68 0.65
describe(ds$media_2, na.rm = T)  #WSJ = -1.38    
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 16 -1.38 4.94     -2   -1.57 4.45  -8   8    16 0.76    -0.69 1.23
describe(ds$media_3, na.rm = T)  #WashPost = 1.88   
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 16 1.88 4.22      2    1.86 2.97  -6  10    16 0.39    -0.34 1.06
describe(ds$media_4, na.rm = T)  #USA today = 1.67  
##    vars  n mean  sd median trimmed  mad min max range skew kurtosis  se
## X1    1 12 1.67 3.8      0     1.2 1.48  -2  10    12 1.09    -0.25 1.1
describe(ds$media_5, na.rm = T)  #Fox News = -6.78  
##    vars  n  mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 18 -6.78 4.76     -8   -7.25 1.48 -10   4    14 1.53     0.64 1.12
describe(ds$media_6, na.rm = T)  #CNN = 5.56         
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 18 5.56 2.91      6    5.56 2.97   1  10     9 0.09     -1.3 0.69
describe(ds$media_7, na.rm = T)  #MSNBC = 5.17      
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 18 5.17 3.76      6    5.31 5.19  -2  10    12 -0.32    -1.25 0.89
describe(ds$media_8, na.rm = T)  #Yahoo = 1.92           
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis  se
## X1    1 12 1.92 3.82      0     1.5 2.22  -2  10    12 0.92    -0.58 1.1
describe(ds$media_9, na.rm = T)  #HuffPost = 5.56   
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 16 5.56 3.16      6    5.79 2.97  -2  10    12 -0.9     0.02 0.79
describe(ds$media_10, na.rm = T) #AOL = 2             
##    vars n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 7    2 4.32      0       2   0  -2  10    12 0.85    -1.08 1.63
describe(ds$media_11, na.rm = T) #NPR = 3.5       
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 14  3.5 3.67      2    3.25 2.97   0  10    10 0.51    -1.53 0.98
describe(ds$media_12, na.rm = T) #ABC = 0.81        
##    vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## X1    1 16 0.81 2.01      1    0.79 1.48  -2   4     6 -0.13     -1.2 0.5
describe(ds$media_13, na.rm = T) #NBC = 2.18     
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 17 2.18 3.38      2    2.07 1.48  -4  10    14 0.62     0.13 0.82
describe(ds$media_14, na.rm = T) #CBS = 3.24         
##    vars  n mean  sd median trimmed  mad min max range skew kurtosis   se
## X1    1 17 3.24 3.4      2    3.13 2.97  -2  10    12 0.59    -0.94 0.82
describe(ds$media_15, na.rm = T) #PBS = 1.38        
##    vars  n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 16 1.38 2.13      0    1.14   0   0   6     6 1.26     0.05 0.53

2. interrate reliability

kripp.alpha(as.matrix(ds), method = c("interval"))
##  Krippendorff's alpha
## 
##  Subjects = 16 
##    Raters = 18 
##     alpha = 0.466

3. create media polarization index

d$media_1 <-  3.78 #NYTimes = 3.78
d$media_2 <- -1.38 #WSJ = -1.38
d$media_3 <- 1.88  #WashPost = 1.88
d$media_4 <- 1.67  #USA today = 1.67
d$media_5 <- -6.78 #Fox News = -6.78
d$media_6 <- 5.56  #CNN = 5.56
d$media_7 <- 5.17  #MSNBC = 5.17
d$media_8 <- 1.92  #Yahoo = 1.92
d$media_9 <- 5.56  #HuffPost = 5.56
d$media_10 <- 2    #AOL = 2
d$media_11 <- 3.5  #NPR = 3.5
d$media_12 <- 0.81 #ABC = 0.81
d$media_13 <- 2.18 #NBC = 2.18
d$media_14 <- 3.24 #CBS = 3.24
d$media_15 <- 1.38 #PBS = 1.38

#calculate index

d$index_1  <- d$mediaPerception_1 *d$media_1 
d$index_2  <- d$mediaPerception_2 *d$media_2 
d$index_3  <- d$mediaPerception_3 *d$media_3 
d$index_4  <- d$mediaPerception_4 *d$media_4 
d$index_5  <- d$mediaPerception_5 *d$media_5 
d$index_6  <- d$mediaPerception_6 *d$media_6 
d$index_7  <- d$mediaPerception_7 *d$media_7 
d$index_8  <- d$mediaPerception_8 *d$media_8 
d$index_9  <- d$mediaPerception_9 *d$media_9 
d$index_10 <- d$mediaPerception_10*d$media_10
d$index_11 <- d$mediaPerception_11*d$media_11
d$index_12 <- d$mediaPerception_12*d$media_12
d$index_13 <- d$mediaPerception_13*d$media_13
d$index_14 <- d$mediaPerception_14*d$media_14
d$index_15 <- d$mediaPerception_15*d$media_15

d$mediaIndex <- (d$index_1 +
                   d$index_2 +
                   d$index_3 +
                   d$index_4 +
                   d$index_5 +
                   d$index_6 +
                   d$index_7 +
                   d$index_8 +
                   d$index_9 +
                   d$index_10+
                   d$index_11+
                   d$index_12+
                   d$index_13+
                   d$index_14+
                   d$index_15)/15

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

4. analyses

a. own vote ~ index * partyID

m1 <- lm(ownvote.c ~  mediaIndex.c * (pDem_Rep + pInd_Not), data = d)
tab_model(m1)
  ownvote.c
Predictors Estimates CI p
(Intercept) -0.19 -0.28 – -0.11 <0.001
mediaIndex.c 0.17 0.13 – 0.21 <0.001
pDem_Rep -0.54 -0.72 – -0.35 <0.001
pInd_Not 0.68 0.48 – 0.87 <0.001
mediaIndex.c * pDem_Rep -0.09 -0.17 – -0.01 0.023
mediaIndex.c * pInd_Not -0.10 -0.20 – 0.00 0.055
Observations 1202
R2 / R2 adjusted 0.202 / 0.198

b. national vote ~ index * partyID

m2 <- lm(overallvote.c ~  mediaIndex.c * (pDem_Rep + pInd_Not), data = d)
tab_model(m2)
  overallvote.c
Predictors Estimates CI p
(Intercept) -0.05 -0.13 – 0.03 0.191
mediaIndex.c 0.26 0.22 – 0.29 <0.001
pDem_Rep -0.95 -1.13 – -0.78 <0.001
pInd_Not 0.41 0.22 – 0.59 <0.001
mediaIndex.c * pDem_Rep 0.09 0.02 – 0.16 0.013
mediaIndex.c * pInd_Not -0.12 -0.21 – -0.03 0.013
Observations 1201
R2 / R2 adjusted 0.389 / 0.387

c. voteLegit ~ index * partyID

m3 <- lm(voteLegit.c ~  mediaIndex.c * (pDem_Rep + pInd_Not), data = d)
tab_model(m3)
  voteLegit.c
Predictors Estimates CI p
(Intercept) -0.12 -0.20 – -0.05 0.002
mediaIndex.c 0.21 0.18 – 0.25 <0.001
pDem_Rep -0.75 -0.91 – -0.58 <0.001
pInd_Not 0.54 0.36 – 0.71 <0.001
mediaIndex.c * pDem_Rep 0.00 -0.07 – 0.07 0.989
mediaIndex.c * pInd_Not -0.11 -0.20 – -0.02 0.021
Observations 1201
R2 / R2 adjusted 0.319 / 0.316