### Sets my working Directory ###
setwd("E:/Methods")
### Calls the tidyverse Package ###
library(tidyverse)
### Calls the anesr Package ###
library(anesr)
### Calls the dplyr Package ###
library(dplyr)
### Calls the MASS Package ###
library(MASS)
### Calls the ggplot2 Package ###
library(ggplot2)
### Calls the ggeffects Package ###
library(ggeffects)
### Calls the gridExtra Package ###
library(gridExtra)
### Calls the sjPlot Package ###
library(sjPlot)
### Calls the stargazer Package ###
library(stargazer)
### Calls the icr Package
library(icr)
### Calls the fast Dummies package ###
library(fastDummies)
### Assigns data as time series 2016 ###
data("timeseries_2016")
### Assigns the time series 2016 data as anes16 ###
anes16 <- timeseries_2016
### A function that eliminates nonsensical values from the anes16 data set (values that are coded as less than 0) used later to clean the data set anes16 ###
clean <- function(x){ifelse (x < 0, NA, x)}
### The Registration variable had 3 possible responses not registered, registered, registered at another address this function combines the last two options into one as I am not interested in where someone registered, but only that they are registered ###
clean_R1 <- function(x){ifelse (x == 2, 1, x)}
### Makes it so when x=3 it becomes 0 for the variable Register ###
clean_R2 <- function(x){ifelse (x == 3, 0, x)}
### Makes the responses for dichotomous variables from 0 to 1 instead of 1 to 2 ###
clean_2 <- function(x){ifelse (x == 2, 0, x)}
### These functions reorder the ordinal variables of Attention_p, Interest_C, and Trust_W ###
clean_AP1 <- function(x){ifelse (x == 1, 20, x)}
clean_AP2 <- function(x){ifelse (x == 5, 1, x)}
clean_AP3 <- function(x){ifelse (x == 20, 5, x)}
clean_AP4 <- function(x){ifelse (x == 2, 25, x)}
clean_AP5 <- function(x){ifelse (x == 4, 2, x)}
clean_AP6 <- function(x){ifelse (x == 25, 4, x)}
clean_IC1 <- function(x){ifelse (x == 1, 20, x)}
clean_IC2 <- function(x){ifelse (x == 3, 1, x)}
clean_IC3 <- function(x){ifelse (x == 20, 3, x)}
Clean_TW1 <- function(x){ifelse (x == 1, 20, x)}
Clean_TW2 <- function(x){ifelse (x == 5, 1, x)}
Clean_TW3 <- function(x){ifelse (x == 20, 5, x)}
Clean_TW4 <- function(x){ifelse (x == 2, 25, x)}
Clean_TW5 <- function(x){ifelse (x == 4, 2, x)}
Clean_TW6 <- function(x){ifelse (x == 25, 4, x)}
### For the variables related to political knowledge the answer is either Republican or Democrat here I reorder the variables so that Democrat is 0 AKA the wrong ansewer to the question ###
Clean_HK1 <- function(x){ifelse (x == 1, 0, x)}
Clean_HK2 <- function(x){ifelse (x == 2, 1, x)}
Clean_SK1 <- function(x){ifelse (x == 1, 0, x)}
Clean_SK2 <- function(x){ifelse (x == 2, 1, x)}
### Changes the control variable for strength of party ID to partisanship ###
Clean_P1 <- function(x){ifelse (x == 7, 1, x)}
Clean_P2 <- function(x){ifelse (x == 6, 2, x)}
Clean_P3 <- function(x){ifelse (x == 5, 3, x)}
Clean_P4 <- function(x){ifelse (x == 1, 20, x)}
Clean_P5<- function(x){ifelse (x == 4, 1, x)}
Clean_P6<- function(x){ifelse (x == 20, 4, x)}
Clean_P7<- function(x){ifelse (x == 2, 25, x)}
Clean_P8<- function(x){ifelse (x == 3, 2, x)}
Clean_P9<- function(x){ifelse (x == 25, 3, x)}
### Eliminates the other category for the control variable of highest level of Education ###
Clean_E1 <- function(x){ifelse (x > 16, NA, x)}
### Reorders the variables Community_W and Volunteer_W ####
Clean_CW1 <- function(x){ifelse (x == 2, 0, x)}
Clean_VW1 <- function(x){ifelse (x == 2, 0, x)}
### Creates the data set anes_clean by using the clean function which eliminates values less than 0 (values that have no value and would make results unusable or nonsensical) ###
anes_clean <- anes16 %>% mutate(across (everything(), clean))
### Creates the Vote Variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Vote <- anes_clean %>% dplyr::select(V162039) %>% mutate(across (everything(), clean_2)) %>% unlist() %>% as.factor()
Tab_Vote <- table(Vote)
tab_df(Tab_Vote, file = "Tab_Vote_sj1")
table(Vote)
## Vote
## 0 1
## 356 2365
## Creates the Register variable and presents the variable in a table and makes it useable in logistic regression by converting it into a factor ###
Register <- anes_clean %>% dplyr::select(V161011) %>% mutate(across (everything(), clean_R1)) %>% mutate(across (everything(), clean_R2)) %>% unlist() %>% as.factor()
Tab_Register <- table(Register)
tab_df(Tab_Register, file = "Tab_Register_sj1")
table(Register)
## Register
## 0 1
## 605 3657
### Creates the Attention_P Variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Attention_P <- anes_clean %>% dplyr::select(V161003) %>% mutate(across (everything(), clean_AP1)) %>% mutate(across (everything(), clean_AP2)) %>% mutate(across (everything(), clean_AP3)) %>% mutate(across (everything(), clean_AP4)) %>% mutate(across (everything(), clean_AP5 )) %>% mutate(across (everything(), clean_AP6))%>% unlist() %>% as.factor()
Tab_Attention_P <- table(Attention_P)
tab_df(Tab_Attention_P, file = "Tab_Attention_P_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
|
84
|
942
|
885
|
1496
|
863
|
table(Attention_P)
## Attention_P
## 1 2 3 4 5
## 84 942 885 1496 863
### Creates the Interest_C variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ##
Interest_C <- anes_clean %>% dplyr::select(V161004) %>% mutate(across (everything(), clean_IC1)) %>% mutate(across (everything(), clean_IC2)) %>% mutate(across (everything(), clean_IC3)) %>% mutate(across (everything(), clean_IC3)) %>% unlist() %>% as.factor()
Tab_Interest_C <- table(Interest_C)
tab_df(Tab_Interest_C, file = "Tab_Interest_C_sj1")
table(Interest_C)
## Interest_C
## 1 2 3
## 521 1519 2230
### Creates the House_K variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
House_K <- anes_clean %>% dplyr::select(V161515) %>% mutate(across (everything(), Clean_HK1)) %>% mutate(across (everything(), Clean_HK2)) %>% unlist() %>% as.factor()
Tab_House_K <- table(House_K)
tab_df(Tab_House_K, file = "Tab_House_K_sj1")
table(House_K)
## House_K
## 0 1
## 1092 2995
### Creates the Senate_K variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Senate_K <- anes_clean %>% dplyr::select(V161516) %>% mutate(across (everything(), Clean_SK1)) %>% mutate(across (everything(), Clean_SK2)) %>% unlist() %>% as.factor()
Tab_Senate_K <- table(Senate_K)
tab_df(Tab_Senate_K, file = "Tab_Senate_K_sj1")
table(Senate_K)
## Senate_K
## 0 1
## 1341 2740
### Creates the Trust_W variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Trust_W <- anes_clean %>% dplyr::select(V161215) %>% mutate(across (everything(), Clean_TW1)) %>% mutate(across (everything(), Clean_TW2)) %>% mutate(across (everything(), Clean_TW3)) %>% mutate(across (everything(), Clean_TW4)) %>% mutate(across (everything(), Clean_TW5)) %>% mutate(across (everything(), Clean_TW6)) %>% unlist() %>% as.factor()
Tab_Trust_W <- table(Trust_W)
tab_df(Tab_Trust_W, file = "Tab_Trust_W_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
|
545
|
1826
|
1382
|
429
|
66
|
table(Trust_W)
## Trust_W
## 1 2 3 4 5
## 545 1826 1382 429 66
### Creates the Government_C variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Government_C <- anes_clean %>% dplyr::select(V161218) %>% unlist() %>% as.factor()
Tab_Government_C <- table(Government_C)
tab_df(Tab_Government_C, file = "Tab_Government_C_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
|
167
|
1319
|
1484
|
1218
|
34
|
table(Government_C)
## Government_C
## 1 2 3 4 5
## 167 1319 1484 1218 34
### Creates the Community_W variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Community_W <- anes_clean %>% dplyr::select(V162195) %>% mutate(across (everything(), Clean_CW1)) %>% unlist() %>% as.factor()
Tab_Community_W <- table(Community_W)
tab_df(Tab_Community_W, file = "Tab_Community_W_sj1")
table(Community_W)
## Community_W
## 0 1
## 2424 1213
### Creates the Volunteer_W variable and presents the variable in a table and makes it usable in logistic regression by converting it into a factor ###
Volunteer_W <- anes_clean %>% dplyr::select(V162197) %>% mutate(across (everything(), Clean_VW1)) %>% unlist() %>% as.factor()
Tab_Volunteer_W <- table(Volunteer_W)
tab_df(Tab_Volunteer_W, file = "Tab_Volunteer_W_sj1")
table(Volunteer_W)
## Volunteer_W
## 0 1
## 2021 1615
### Selects each column/variable from the anes_clean data set which is made up of the variables/columns that ask whether the respondent has watched a given program then unlists the variable/column to make it usable then assigns the unlisted variable/column to the name of the program it pertains to ###
Twenty_Twenty <- anes_clean %>% dplyr::select (V161364) %>% unlist()
table(Twenty_Twenty)
## Twenty_Twenty
## 0 1
## 2877 852
All_In_with_Chris_Hayes <- anes_clean %>% dplyr::select (V161365) %>% unlist()
table(All_In_with_Chris_Hayes)
## All_In_with_Chris_Hayes
## 0 1
## 3569 160
The_Blacklist <- anes_clean %>% dplyr::select (V161366) %>% unlist()
table(The_Blacklist)
## The_Blacklist
## 0 1
## 3298 431
CBS_Evening_News_with_Scott_Pelley <- anes_clean %>% dplyr::select (V161367) %>% unlist()
table(CBS_Evening_News_with_Scott_Pelley)
## CBS_Evening_News_with_Scott_Pelley
## 0 1
## 3120 609
Criminal_Minds <- anes_clean %>% dplyr::select (V161368) %>% unlist()
table(Criminal_Minds)
## Criminal_Minds
## 0 1
## 2987 742
Empire <- anes_clean %>% dplyr::select (V161369) %>% unlist()
table(Empire)
## Empire
## 0 1
## 3434 295
Hannity <- anes_clean %>% dplyr::select (V161370) %>% unlist()
table(Hannity)
## Hannity
## 0 1
## 3337 392
Jimmy_Kimmel_Live <- anes_clean %>% dplyr::select (V161371) %>% unlist()
table(Jimmy_Kimmel_Live)
## Jimmy_Kimmel_Live
## 0 1
## 3315 414
The_Kelly_File <- anes_clean %>% dplyr::select (V161372) %>% unlist()
table(The_Kelly_File)
## The_Kelly_File
## 0 1
## 3364 365
Modern_Family <- anes_clean %>% dplyr::select (V161373) %>% unlist()
table(Modern_Family)
## Modern_Family
## 0 1
## 3084 645
NCIS <- anes_clean %>% dplyr::select (V161374) %>% unlist()
table(NCIS)
## NCIS
## 0 1
## 2874 855
The_Nightly_Show_with_Larry_Wilmore <- anes_clean %>% dplyr::select (V161375) %>% unlist()
table(The_Nightly_Show_with_Larry_Wilmore)
## The_Nightly_Show_with_Larry_Wilmore
## 0 1
## 3651 78
Sunday_Night_Football <- anes_clean %>% dplyr::select (V161376) %>% unlist()
table(Sunday_Night_Football)
## Sunday_Night_Football
## 0 1
## 2479 1250
Scorpion <- anes_clean %>% dplyr::select (V161377) %>% unlist()
table(Scorpion)
## Scorpion
## 0 1
## 3435 294
The_Simpsons <- anes_clean %>% dplyr::select (V161378) %>% unlist()
table(The_Simpsons)
## The_Simpsons
## 0 1
## 3479 250
Today <- anes_clean %>% dplyr::select (V161379) %>% unlist()
table(Today)
## Today
## 0 1
## 3019 710
Sixty_Minutes <- anes_clean %>% dplyr::select (V161380) %>% unlist()
table(Sixty_Minutes)
## Sixty_Minutes
## 0 1
## 2585 1144
Anderson_Cooper_Three_Hundred_and_Sixty <- anes_clean %>% dplyr::select (V161381) %>% unlist()
table(Anderson_Cooper_Three_Hundred_and_Sixty)
## Anderson_Cooper_Three_Hundred_and_Sixty
## 0 1
## 3168 561
CBS_This_Morning <- anes_clean %>% dplyr::select (V161382) %>% unlist()
table(CBS_This_Morning)
## CBS_This_Morning
## 0 1
## 3024 705
Dancing_with_the_Stars <- anes_clean %>% dplyr::select (V161383) %>% unlist()
table(Dancing_with_the_Stars)
## Dancing_with_the_Stars
## 0 1
## 3182 547
Face_the_Nation <- anes_clean %>% dplyr::select (V161384) %>% unlist()
table(Face_the_Nation)
## Face_the_Nation
## 0 1
## 3350 379
House_of_Cards <- anes_clean %>% dplyr::select (V161385) %>% unlist()
table(House_of_Cards)
## House_of_Cards
## 0 1
## 3389 340
Hardball_with_Chris_Matthews <- anes_clean %>% dplyr::select (V161386) %>% unlist()
table(Hardball_with_Chris_Matthews)
## Hardball_with_Chris_Matthews
## 0 1
## 3455 274
Judge_Judy <- anes_clean %>% dplyr::select (V161387) %>% unlist()
table(Judge_Judy)
## Judge_Judy
## 0 1
## 3266 463
Meet_the_Press <- anes_clean %>% dplyr::select (V161388) %>% unlist()
table(Meet_the_Press)
## Meet_the_Press
## 0 1
## 3265 464
Game_of_Thrones <- anes_clean %>% dplyr::select (V161389) %>% unlist()
table(Game_of_Thrones)
## Game_of_Thrones
## 0 1
## 3225 504
NBC_Nightly_News_with_Lester_Holt <- anes_clean %>% dplyr::select (V161390) %>% unlist()
table(NBC_Nightly_News_with_Lester_Holt)
## NBC_Nightly_News_with_Lester_Holt
## 0 1
## 2936 793
On_the_Record_with_Greta_Van_Susteren <- anes_clean %>% dplyr::select (V161391) %>% unlist()
table(On_the_Record_with_Greta_Van_Susteren)
## On_the_Record_with_Greta_Van_Susteren
## 0 1
## 3445 284
Daredevil <- anes_clean %>% dplyr::select (V161392) %>% unlist()
table(Daredevil)
## Daredevil
## 0 1
## 3588 141
The_Rachel_Maddow_Show <- anes_clean %>% dplyr::select (V161393) %>% unlist()
table(The_Rachel_Maddow_Show)
## The_Rachel_Maddow_Show
## 0 1
## 3450 279
Shark_Tank <- anes_clean %>% dplyr::select (V161394) %>% unlist()
table(Shark_Tank)
## Shark_Tank
## 0 1
## 3101 628
The_Voice <- anes_clean %>% dplyr::select(V161395) %>% unlist()
table(The_Voice)
## The_Voice
## 0 1
## 3000 729
ABC_World_News_with_David_Muir <- anes_clean %>% dplyr::select (V161396) %>% unlist()
table(ABC_World_News_with_David_Muir)
## ABC_World_News_with_David_Muir
## 0 1
## 3039 690
Blue_bloods <- anes_clean %>% dplyr::select (V161397) %>% unlist()
table(Blue_bloods)
## Blue_bloods
## 0 1
## 3152 577
Conan <- anes_clean %>% dplyr::select (V161398) %>% unlist()
table(Conan)
## Conan
## 0 1
## 3571 158
Dateline_NBC <- anes_clean %>% dplyr::select (V161399) %>% unlist()
table(Dateline_NBC)
## Dateline_NBC
## 0 1
## 2734 995
Good_Morning_America <- anes_clean %>% dplyr::select (V161400) %>% unlist()
table(Good_Morning_America)
## Good_Morning_America
## 0 1
## 2768 961
Hawaii_Five_O <- anes_clean %>% dplyr::select (V161401) %>% unlist()
table(Hawaii_Five_O)
## Hawaii_Five_O
## 0 1
## 3371 358
Madam_Secretary <- anes_clean %>% dplyr::select (V161402) %>% unlist()
table(Madam_Secretary)
## Madam_Secretary
## 0 1
## 3368 361
Nancy_Grace <- anes_clean %>% dplyr::select (V161403) %>% unlist()
table(Nancy_Grace)
## Nancy_Grace
## 0 1
## 3541 188
Erin_Burnett_Outfront <- anes_clean %>% dplyr::select (V161404) %>% unlist()
table(Erin_Burnett_Outfront)
## Erin_Burnett_Outfront
## 0 1
## 3603 126
PBS_News_Hour <- anes_clean %>% dplyr::select (V161405) %>% unlist()
table(PBS_News_Hour)
## PBS_News_Hour
## 0 1
## 3293 436
Scandal <- anes_clean %>% dplyr::select (V161406) %>% unlist()
table(Scandal)
## Scandal
## 0 1
## 3397 332
The_Big_Bang_Theory <- anes_clean %>% dplyr::select (V161407) %>% unlist()
table(The_Big_Bang_Theory)
## The_Big_Bang_Theory
## 0 1
## 2731 998
The_Late_Show_with_Stephen_Colbert <- anes_clean %>% dplyr::select(V161408) %>% unlist()
table(The_Late_Show_with_Stephen_Colbert)
## The_Late_Show_with_Stephen_Colbert
## 0 1
## 3339 390
The_O_Reilly_Factor <- anes_clean %>% dplyr::select (V161409) %>% unlist()
table(The_O_Reilly_Factor)
## The_O_Reilly_Factor
## 0 1
## 3157 572
The_Tonight_Show_Starring_Jimmy_Fallon <- anes_clean %>% dplyr::select(V161410) %>% unlist()
table(The_Tonight_Show_Starring_Jimmy_Fallon)
## The_Tonight_Show_Starring_Jimmy_Fallon
## 0 1
## 3057 672
Alpha_House <- anes_clean %>% dplyr::select(V161411) %>% unlist()
table(Alpha_House)
## Alpha_House
## 0 1
## 3702 27
### Selects the control variables from anes_clean and makes them usable in logistic regression by unlisting them ###
Gender <- anes_clean %>% dplyr::select (V161342) %>% unlist () %>% as.factor()
Gender1 <- anes_clean %>% dplyr::select (V161342) %>% unlist ()
Tab_Gender1 <- table(Gender1)
tab_df(Tab_Gender1, file = "Tab_Gender_sj1")
table(Gender1)
## Gender1
## 1 2 3
## 1987 2231 11
Race <- anes_clean %>% dplyr::select (V161310x) %>% unlist () %>% as.factor()
Race1 <- anes_clean %>% dplyr::select (V161310x) %>% unlist ()
Tab_Race1 <- table(Race1)
tab_df(Tab_Race1, file = "Tab_Race_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
X6
|
|
3038
|
397
|
148
|
27
|
450
|
177
|
table(Race1)
## Race1
## 1 2 3 4 5 6
## 3038 397 148 27 450 177
table(Race)
## Race
## 1 2 3 4 5 6
## 3038 397 148 27 450 177
Age <- anes_clean %>% dplyr::select (V161267x) %>% unlist ()
Tab_Age <- table(Age)
tab_df(Tab_Age, file = "Tab_Age_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
X6
|
X7
|
X8
|
X9
|
X10
|
X11
|
X12
|
X13
|
|
121
|
207
|
323
|
387
|
374
|
281
|
339
|
349
|
432
|
385
|
384
|
239
|
328
|
table(Age)
## Age
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 121 207 323 387 374 281 339 349 432 385 384 239 328
Income <- anes_clean %>% dplyr::select(V161361x) %>% unlist ()
Tab_Income <- table(Income)
tab_df(Tab_Income, file = "Tab_Income_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
X6
|
X7
|
X8
|
X9
|
X10
|
X11
|
X12
|
X13
|
X14
|
X15
|
X16
|
X17
|
X18
|
X19
|
X20
|
X21
|
X22
|
X23
|
X24
|
X25
|
X26
|
X27
|
X28
|
|
275
|
96
|
133
|
37
|
110
|
52
|
153
|
64
|
143
|
34
|
213
|
166
|
178
|
154
|
204
|
85
|
205
|
107
|
138
|
126
|
231
|
176
|
191
|
182
|
166
|
154
|
154
|
141
|
table(Income)
## Income
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 275 96 133 37 110 52 153 64 143 34 213 166 178 154 204 85 205 107 138 126
## 21 22 23 24 25 26 27 28
## 231 176 191 182 166 154 154 141
Partisanship<- anes_clean %>% dplyr::select(V161158x) %>% mutate(across (everything(), Clean_P1)) %>% mutate(across (everything(), Clean_P2)) %>% mutate(across (everything(), Clean_P3)) %>% mutate(across (everything(), Clean_P4)) %>% mutate(across (everything(), Clean_P5)) %>% mutate(across (everything(), Clean_P6)) %>% mutate(across (everything(), Clean_P7)) %>% mutate(across (everything(), Clean_P8)) %>% mutate(across (everything(), Clean_P9)) %>% unlist ()
Tab_Partisanship <- table(Partisanship)
tab_df(Tab_Partisanship, file = "Tab_Partisanship_sj1")
|
X1
|
X2
|
X3
|
X4
|
|
579
|
990
|
1067
|
1611
|
table(Partisanship)
## Partisanship
## 1 2 3 4
## 579 990 1067 1611
Education <- anes_clean %>% dplyr::select(V161270) %>% mutate(across (everything(), Clean_E1)) %>% unlist ()
Tab_Education <- table(Education)
tab_df(Tab_Education, file = "Tab_Education_sj1")
|
X1
|
X2
|
X3
|
X4
|
X5
|
X6
|
X7
|
X8
|
X9
|
X10
|
X11
|
X12
|
X13
|
X14
|
X15
|
X16
|
|
1
|
3
|
15
|
22
|
32
|
40
|
62
|
107
|
810
|
898
|
313
|
288
|
955
|
499
|
88
|
93
|
table(Education)
## Education
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 3 15 22 32 40 62 107 810 898 313 288 955 499 88 93
### Creates the group of shows that will be used in the logistic regression models ###
Traditional_Political_News_Programs_GLM <- as.data.frame(Twenty_Twenty + CBS_Evening_News_with_Scott_Pelley + Sixty_Minutes + Face_the_Nation + Meet_the_Press + NBC_Nightly_News_with_Lester_Holt + ABC_World_News_with_David_Muir + Dateline_NBC + PBS_News_Hour) %>% unlist ()
Entertainment_or_Opinion_Political_News_Programs_GLM <- as.data.frame(All_In_with_Chris_Hayes + Hannity + Jimmy_Kimmel_Live + The_Kelly_File + The_Nightly_Show_with_Larry_Wilmore + Today + Anderson_Cooper_Three_Hundred_and_Sixty + CBS_This_Morning + Hardball_with_Chris_Matthews + On_the_Record_with_Greta_Van_Susteren + The_Rachel_Maddow_Show + Good_Morning_America + Nancy_Grace + Erin_Burnett_Outfront + The_O_Reilly_Factor) %>% unlist ()
Expressly_Political_Entertainment_Programs_GLM <- as.data.frame(House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>% unlist ()
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM <- as.data.frame(The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>% unlist ()
Apolitical_Entertainment_Programs_GLM <- as.data.frame(Empire + Modern_Family + Sunday_Night_Football + The_Simpsons + Dancing_with_the_Stars + Shark_Tank + The_Voice + Conan + The_Big_Bang_Theory + The_Tonight_Show_Starring_Jimmy_Fallon) %>% unlist ()
All_Programs_GLM <- as.data.frame(Twenty_Twenty + CBS_Evening_News_with_Scott_Pelley + Sixty_Minutes + Face_the_Nation + Meet_the_Press + NBC_Nightly_News_with_Lester_Holt + ABC_World_News_with_David_Muir + Dateline_NBC + PBS_News_Hour + All_In_with_Chris_Hayes + Hannity + Jimmy_Kimmel_Live + The_Kelly_File + The_Nightly_Show_with_Larry_Wilmore + Today + Anderson_Cooper_Three_Hundred_and_Sixty + CBS_This_Morning + Hardball_with_Chris_Matthews + On_the_Record_with_Greta_Van_Susteren + The_Rachel_Maddow_Show + Good_Morning_America + Nancy_Grace + Erin_Burnett_Outfront + The_O_Reilly_Factor + House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House + The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O + Empire + Modern_Family + Sunday_Night_Football + The_Simpsons + Dancing_with_the_Stars + Shark_Tank + The_Voice + Conan + The_Big_Bang_Theory + The_Tonight_Show_Starring_Jimmy_Fallon) %>% unlist ()
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_V_GLM_C <- glm(Vote ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_V_GLM_C)
##
## Call:
## glm(formula = Vote ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6758 0.3597 0.4516 0.5457 1.1411
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.265178 0.451183 -0.588 0.556707
## All_Programs_GLM 0.025061 0.014326 1.749 0.080228 .
## Race2 -0.228627 0.206405 -1.108 0.268006
## Race3 -0.512857 0.370039 -1.386 0.165761
## Race4 0.342920 1.088970 0.315 0.752835
## Race5 -0.023987 0.234470 -0.102 0.918515
## Race6 0.728181 0.436239 1.669 0.095073 .
## Partisanship 0.080140 0.066142 1.212 0.225652
## Income 0.034723 0.009106 3.813 0.000137 ***
## Age 0.093861 0.020074 4.676 2.93e-06 ***
## Gender2 -0.300517 0.136092 -2.208 0.027231 *
## Gender3 12.996236 438.536630 0.030 0.976358
## Education 0.069449 0.032336 2.148 0.031739 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1680.6 on 2271 degrees of freedom
## Residual deviance: 1606.6 on 2259 degrees of freedom
## (1998 observations deleted due to missingness)
## AIC: 1632.6
##
## Number of Fisher Scoring iterations: 13
stargazer(All_V_GLM_C, type = "html", out = "All_V_star2")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Vote</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.025<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.014)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.229</td></tr>
## <tr><td style="text-align:left"></td><td>(0.206)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.513</td></tr>
## <tr><td style="text-align:left"></td><td>(0.370)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.343</td></tr>
## <tr><td style="text-align:left"></td><td>(1.089)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.024</td></tr>
## <tr><td style="text-align:left"></td><td>(0.234)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.728<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.436)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.080</td></tr>
## <tr><td style="text-align:left"></td><td>(0.066)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.035<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.094<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.301<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.136)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>12.996</td></tr>
## <tr><td style="text-align:left"></td><td>(438.537)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.069<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.032)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-0.265</td></tr>
## <tr><td style="text-align:left"></td><td>(0.451)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>2,272</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-803.317</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>1,632.634</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_V_GLM_C_EXP <- exp(coef(All_V_GLM_C))
All_V_GLM_C_Prob1 <- All_V_GLM_C_EXP - 1
All_V_GLM_C_Prob2 <- All_V_GLM_C_Prob1 * 100
All_V_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -2.329309e+01 2.537757e+00 -2.043750e+01 -4.012177e+01
## Race4 Race5 Race6 Partisanship
## 4.090564e+01 -2.370208e+00 1.071309e+02 8.343921e+00
## Income Age Gender2 Gender3
## 3.533315e+00 9.840687e+00 -2.595650e+01 4.407503e+07
## Education
## 7.191693e+00
tab_df(All_V_GLM_C_Prob2, file = "All_V_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-23.29
|
2.54
|
-20.44
|
-40.12
|
40.91
|
-2.37
|
107.13
|
8.34
|
3.53
|
9.84
|
-25.96
|
44075027.04
|
7.19
|
Shows_V_GLM <- glm(Vote ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_V_GLM)
##
## Call:
## glm(formula = Vote ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7075 0.3571 0.4491 0.5492 1.2354
##
## Coefficients:
## Estimate
## (Intercept) -0.293296
## Traditional_Political_News_Programs_GLM 0.022822
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.011719
## Expressly_Political_Entertainment_Programs_GLM 0.023087
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.119337
## Apolitical_Entertainment_Programs_GLM -0.017134
## Race2 -0.216202
## Race3 -0.485737
## Race4 0.295309
## Race5 -0.014268
## Race6 0.735446
## Partisanship 0.090707
## Income 0.036678
## Age 0.087921
## Gender2 -0.309651
## Gender3 12.929370
## Education 0.071230
## Std. Error
## (Intercept) 0.464432
## Traditional_Political_News_Programs_GLM 0.041397
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.042914
## Expressly_Political_Entertainment_Programs_GLM 0.096622
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.054577
## Apolitical_Entertainment_Programs_GLM 0.046692
## Race2 0.207114
## Race3 0.372099
## Race4 1.089300
## Race5 0.235435
## Race6 0.437371
## Partisanship 0.066538
## Income 0.009182
## Age 0.021897
## Gender2 0.136636
## Gender3 438.467810
## Education 0.032808
## z value
## (Intercept) -0.632
## Traditional_Political_News_Programs_GLM 0.551
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.273
## Expressly_Political_Entertainment_Programs_GLM 0.239
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 2.187
## Apolitical_Entertainment_Programs_GLM -0.367
## Race2 -1.044
## Race3 -1.305
## Race4 0.271
## Race5 -0.061
## Race6 1.682
## Partisanship 1.363
## Income 3.994
## Age 4.015
## Gender2 -2.266
## Gender3 0.029
## Education 2.171
## Pr(>|z|)
## (Intercept) 0.5277
## Traditional_Political_News_Programs_GLM 0.5814
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.7848
## Expressly_Political_Entertainment_Programs_GLM 0.8112
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.0288 *
## Apolitical_Entertainment_Programs_GLM 0.7136
## Race2 0.2965
## Race3 0.1918
## Race4 0.7863
## Race5 0.9517
## Race6 0.0927 .
## Partisanship 0.1728
## Income 6.49e-05 ***
## Age 5.94e-05 ***
## Gender2 0.0234 *
## Gender3 0.9765
## Education 0.0299 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1680.6 on 2271 degrees of freedom
## Residual deviance: 1602.9 on 2255 degrees of freedom
## (1998 observations deleted due to missingness)
## AIC: 1636.9
##
## Number of Fisher Scoring iterations: 13
stargazer(Shows_V_GLM, type = "html", out = "Shows_V_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Vote</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.023</td></tr>
## <tr><td style="text-align:left"></td><td>(0.041)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.012</td></tr>
## <tr><td style="text-align:left"></td><td>(0.043)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.023</td></tr>
## <tr><td style="text-align:left"></td><td>(0.097)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>0.119<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.055)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.017</td></tr>
## <tr><td style="text-align:left"></td><td>(0.047)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.216</td></tr>
## <tr><td style="text-align:left"></td><td>(0.207)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.486</td></tr>
## <tr><td style="text-align:left"></td><td>(0.372)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.295</td></tr>
## <tr><td style="text-align:left"></td><td>(1.089)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.014</td></tr>
## <tr><td style="text-align:left"></td><td>(0.235)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.735<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.437)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.091</td></tr>
## <tr><td style="text-align:left"></td><td>(0.067)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.037<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.088<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.022)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.310<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.137)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>12.929</td></tr>
## <tr><td style="text-align:left"></td><td>(438.468)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.071<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.033)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-0.293</td></tr>
## <tr><td style="text-align:left"></td><td>(0.464)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>2,272</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-801.473</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>1,636.947</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_V_GLM_EXP <- exp(coef(Shows_V_GLM))
Shows_V_GLM_Prob1 <- Shows_V_GLM_EXP - 1
Shows_V_GLM_Prob2 <- Shows_V_GLM_Prob1 * 100
Shows_V_GLM_Prob2
## (Intercept)
## -2.541989e+01
## Traditional_Political_News_Programs_GLM
## 2.308477e+00
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 1.178765e+00
## Expressly_Political_Entertainment_Programs_GLM
## 2.335567e+00
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## 1.267500e+01
## Apolitical_Entertainment_Programs_GLM
## -1.698850e+00
## Race2
## -1.944276e+01
## Race3
## -3.847564e+01
## Race4
## 3.435411e+01
## Race5
## -1.416685e+00
## Race6
## 1.086413e+02
## Partisanship
## 9.494763e+00
## Income
## 3.735911e+00
## Age
## 9.190134e+00
## Gender2
## -2.662968e+01
## Gender3
## 4.122429e+07
## Education
## 7.382863e+00
tab_df(Shows_V_GLM_Prob2, file = "Shows_V_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-25.42
|
2.31
|
1.18
|
2.34
|
12.67
|
-1.70
|
-19.44
|
-38.48
|
34.35
|
-1.42
|
108.64
|
9.49
|
3.74
|
9.19
|
-26.63
|
41224288.04
|
7.38
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
Issue_V_GLM_GG <- ggpredict(Shows_V_GLM, terms = "Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM")
Issue_V_GLM_P <- plot(Issue_V_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity of Voting") + xlab ("Number of Issue Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_V_GLM_GG <- ggpredict(All_V_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_V_GLM_P <- plot(All_V_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity of Voting") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(Issue_V_GLM_P, All_V_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_R_GLM_C <- glm(Register ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_R_GLM_C)
##
## Call:
## glm(formula = Register ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1350 0.2139 0.3414 0.5206 1.7478
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.963605 0.341324 -11.612 < 2e-16 ***
## All_Programs_GLM 0.029608 0.012434 2.381 0.017255 *
## Race2 0.435539 0.204005 2.135 0.032766 *
## Race3 -0.998710 0.258128 -3.869 0.000109 ***
## Race4 -0.959346 0.563795 -1.702 0.088833 .
## Race5 -0.079401 0.164413 -0.483 0.629141
## Race6 0.195428 0.280615 0.696 0.486162
## Partisanship 0.561206 0.051102 10.982 < 2e-16 ***
## Income 0.051013 0.007617 6.698 2.12e-11 ***
## Age 0.154084 0.016660 9.249 < 2e-16 ***
## Gender2 0.239518 0.111268 2.153 0.031348 *
## Gender3 0.861485 1.097516 0.785 0.432488
## Education 0.214315 0.026788 8.000 1.24e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2733.3 on 3480 degrees of freedom
## Residual deviance: 2232.0 on 3468 degrees of freedom
## (789 observations deleted due to missingness)
## AIC: 2258
##
## Number of Fisher Scoring iterations: 5
stargazer(All_R_GLM_C, type = "html", out = "All_R_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Register</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.030<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.436<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.204)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.999<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.258)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>-0.959<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.564)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.079</td></tr>
## <tr><td style="text-align:left"></td><td>(0.164)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.195</td></tr>
## <tr><td style="text-align:left"></td><td>(0.281)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.561<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.051)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.051<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.154<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.017)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.240<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.111)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.861</td></tr>
## <tr><td style="text-align:left"></td><td>(1.098)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.214<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.027)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-3.964<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.341)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,481</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,115.977</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>2,257.954</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_R_GLM_C_EXP <- exp(coef(All_R_GLM_C))
All_R_GLM_C_Prob1 <- All_R_GLM_C_EXP - 1
All_R_GLM_C_Prob2 <- All_R_GLM_C_Prob1 * 100
All_R_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -98.100549 3.005083 54.579577 -63.164564
## Race4 Race5 Race6 Partisanship
## -61.685674 -7.633068 21.583067 75.278503
## Income Age Gender2 Gender3
## 5.233710 16.658864 27.063696 136.667251
## Education
## 23.901304
tab_df(All_R_GLM_C_Prob2, file = "All_R_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-98.10
|
3.01
|
54.58
|
-63.16
|
-61.69
|
-7.63
|
21.58
|
75.28
|
5.23
|
16.66
|
27.06
|
136.67
|
23.90
|
Shows_R_GLM <- glm(Register ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_R_GLM)
##
## Call:
## glm(formula = Register ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1238 0.2130 0.3412 0.5229 1.7404
##
## Coefficients:
## Estimate
## (Intercept) -3.957324
## Traditional_Political_News_Programs_GLM 0.007655
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.029599
## Expressly_Political_Entertainment_Programs_GLM 0.127133
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.033530
## Apolitical_Entertainment_Programs_GLM 0.030283
## Race2 0.439827
## Race3 -0.989807
## Race4 -0.937722
## Race5 -0.068791
## Race6 0.184623
## Partisanship 0.559869
## Income 0.050640
## Age 0.159015
## Gender2 0.245061
## Gender3 0.862959
## Education 0.210124
## Std. Error
## (Intercept) 0.350853
## Traditional_Political_News_Programs_GLM 0.036610
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.038875
## Expressly_Political_Entertainment_Programs_GLM 0.089606
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.044150
## Apolitical_Entertainment_Programs_GLM 0.039882
## Race2 0.204580
## Race3 0.258636
## Race4 0.565369
## Race5 0.165615
## Race6 0.281733
## Partisanship 0.051326
## Income 0.007666
## Age 0.017962
## Gender2 0.111813
## Gender3 1.100276
## Education 0.027283
## z value
## (Intercept) -11.279
## Traditional_Political_News_Programs_GLM 0.209
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.761
## Expressly_Political_Entertainment_Programs_GLM 1.419
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.759
## Apolitical_Entertainment_Programs_GLM 0.759
## Race2 2.150
## Race3 -3.827
## Race4 -1.659
## Race5 -0.415
## Race6 0.655
## Partisanship 10.908
## Income 6.606
## Age 8.853
## Gender2 2.192
## Gender3 0.784
## Education 7.702
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Traditional_Political_News_Programs_GLM 0.83437
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.44643
## Expressly_Political_Entertainment_Programs_GLM 0.15596
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.44757
## Apolitical_Entertainment_Programs_GLM 0.44767
## Race2 0.03156 *
## Race3 0.00013 ***
## Race4 0.09720 .
## Race5 0.67787
## Race6 0.51227
## Partisanship < 2e-16 ***
## Income 3.95e-11 ***
## Age < 2e-16 ***
## Gender2 0.02840 *
## Gender3 0.43286
## Education 1.34e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2733.3 on 3480 degrees of freedom
## Residual deviance: 2230.3 on 3464 degrees of freedom
## (789 observations deleted due to missingness)
## AIC: 2264.3
##
## Number of Fisher Scoring iterations: 5
stargazer(Shows_R_GLM, type = "html", out = "Shows_R_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Register</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.008</td></tr>
## <tr><td style="text-align:left"></td><td>(0.037)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.030</td></tr>
## <tr><td style="text-align:left"></td><td>(0.039)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.127</td></tr>
## <tr><td style="text-align:left"></td><td>(0.090)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>0.034</td></tr>
## <tr><td style="text-align:left"></td><td>(0.044)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>0.030</td></tr>
## <tr><td style="text-align:left"></td><td>(0.040)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.440<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.205)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.990<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.259)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>-0.938<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.565)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.069</td></tr>
## <tr><td style="text-align:left"></td><td>(0.166)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.185</td></tr>
## <tr><td style="text-align:left"></td><td>(0.282)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.560<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.051)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.051<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.159<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.018)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.245<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.112)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.863</td></tr>
## <tr><td style="text-align:left"></td><td>(1.100)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.210<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.027)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-3.957<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.351)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,481</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,115.138</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>2,264.277</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_R_GLM_EXP <- exp(coef(Shows_R_GLM))
Shows_R_GLM_Prob1 <- Shows_R_GLM_EXP - 1
Shows_R_GLM_Prob2 <- Shows_R_GLM_Prob1 * 100
Shows_R_GLM_Prob2
## (Intercept)
## -98.0885796
## Traditional_Political_News_Programs_GLM
## 0.7684659
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 3.0041032
## Expressly_Political_Entertainment_Programs_GLM
## 13.5567534
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## 3.4098861
## Apolitical_Entertainment_Programs_GLM
## 3.0746077
## Race2
## 55.2438661
## Race3
## -62.8351459
## Race4
## -60.8481244
## Race5
## -6.6478659
## Race6
## 20.2765346
## Partisanship
## 75.0442599
## Income
## 5.1944586
## Age
## 17.2355314
## Gender2
## 27.7699040
## Gender3
## 137.0163960
## Education
## 23.3830764
tab_df(Shows_R_GLM_Prob2, file = "Shows_R_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-98.09
|
0.77
|
3.00
|
13.56
|
3.41
|
3.07
|
55.24
|
-62.84
|
-60.85
|
-6.65
|
20.28
|
75.04
|
5.19
|
17.24
|
27.77
|
137.02
|
23.38
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
All_R_GLM_GG <- ggpredict(All_R_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_R_GLM_P<- plot(All_R_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity of Registering") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(All_R_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
### Creates a logistic regression model for all 6 groups of shows against all 10 dependent variables with control variables then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_AP_GLM_C <- polr(Attention_P ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(All_AP_GLM_C)))
## Value Std. Error t value
## All_Programs_GLM 0.0587149448 0.006792667 8.643871880
## Race2 -0.1539951865 0.110941097 -1.388080619
## Race3 -1.0438005663 0.181412506 -5.753740967
## Race4 0.0021047208 0.546982955 0.003847873
## Race5 0.0142350501 0.106723671 0.133382313
## Race6 0.2553976184 0.166697466 1.532102582
## Partisanship 0.3052428328 0.030439571 10.027829794
## Income -0.0005883141 0.004394325 -0.133880405
## Age 0.1266365317 0.009700680 13.054397662
## Gender2 -0.5863789352 0.063916720 -9.174108620
## Gender3 -0.2352698037 0.627106016 -0.375167512
## Education 0.1972302199 0.015473683 12.746171484
## 1|2 -0.4726898638 0.234185888 -2.018438718
## 2|3 2.6326496218 0.201338786 13.075720159
## 3|4 3.7802806728 0.205961885 18.354273028
## 4|5 5.6516865578 0.217969297 25.928819473
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value Std. Error t value p value
## All_Programs_GLM 0.0587149448 0.006792667 8.643871880 5.433948e-18
## Race2 -0.1539951865 0.110941097 -1.388080619 1.651125e-01
## Race3 -1.0438005663 0.181412506 -5.753740967 8.728988e-09
## Race4 0.0021047208 0.546982955 0.003847873 9.969298e-01
## Race5 0.0142350501 0.106723671 0.133382313 8.938910e-01
## Race6 0.2553976184 0.166697466 1.532102582 1.254971e-01
## Partisanship 0.3052428328 0.030439571 10.027829794 1.150168e-23
## Income -0.0005883141 0.004394325 -0.133880405 8.934971e-01
## Age 0.1266365317 0.009700680 13.054397662 5.998256e-39
## Gender2 -0.5863789352 0.063916720 -9.174108620 4.553258e-20
## Gender3 -0.2352698037 0.627106016 -0.375167512 7.075359e-01
## Education 0.1972302199 0.015473683 12.746171484 3.274349e-37
## 1|2 -0.4726898638 0.234185888 -2.018438718 4.354559e-02
## 2|3 2.6326496218 0.201338786 13.075720159 4.532509e-39
## 3|4 3.7802806728 0.205961885 18.354273028 3.051436e-75
## 4|5 5.6516865578 0.217969297 25.928819473 3.152279e-148
stargazer(All_AP_GLM_C, type = "html", out = "All_AP_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Attention_P</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.059<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.154</td></tr>
## <tr><td style="text-align:left"></td><td>(0.111)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-1.044<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.181)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.002</td></tr>
## <tr><td style="text-align:left"></td><td>(0.547)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.014</td></tr>
## <tr><td style="text-align:left"></td><td>(0.107)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.255</td></tr>
## <tr><td style="text-align:left"></td><td>(0.167)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.305<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.030)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>-0.001</td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.127<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.586<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.235</td></tr>
## <tr><td style="text-align:left"></td><td>(0.627)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.197<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.015)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,485</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_AP_GLM_C_EXP <- exp(coef(All_AP_GLM_C))
All_AP_GLM_C_Prob1 <- All_AP_GLM_C_EXP - 1
All_AP_GLM_C_Prob2 <- All_AP_GLM_C_Prob1 * 100
All_AP_GLM_C_Prob2
## All_Programs_GLM Race2 Race3 Race4
## 6.04729043 -14.27238525 -64.78860965 0.21069373
## Race5 Race6 Partisanship Income
## 1.43368509 29.09748342 35.69544760 -0.05881411
## Age Gender2 Gender3 Education
## 13.50044047 -44.36618217 -20.96424205 21.80244218
tab_df(All_AP_GLM_C_Prob2, file = "All_AP_sj1")
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
6.05
|
-14.27
|
-64.79
|
0.21
|
1.43
|
29.10
|
35.70
|
-0.06
|
13.50
|
-44.37
|
-20.96
|
21.80
|
Shows_AP_GLM <- polr(Attention_P ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(Shows_AP_GLM)))
## Value
## Traditional_Political_News_Programs_GLM 0.057269732
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.211860445
## Expressly_Political_Entertainment_Programs_GLM 0.161582567
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.077966635
## Apolitical_Entertainment_Programs_GLM -0.044351714
## Race2 -0.243256369
## Race3 -1.111221153
## Race4 -0.061098981
## Race5 -0.076779921
## Race6 0.211499104
## Partisanship 0.292763700
## Income -0.002960909
## Age 0.112496883
## Gender2 -0.557002676
## Gender3 -0.286976501
## Education 0.182531620
## 1|2 -0.895464031
## 2|3 2.222129542
## 3|4 3.389663433
## 4|5 5.315005345
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019247382
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020676243
## Expressly_Political_Entertainment_Programs_GLM 0.045517645
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024110220
## Apolitical_Entertainment_Programs_GLM 0.021800675
## Race2 0.111987981
## Race3 0.182932327
## Race4 0.549978949
## Race5 0.107678868
## Race6 0.166280846
## Partisanship 0.030614984
## Income 0.004432498
## Age 0.010523195
## Gender2 0.064205778
## Gender3 0.627339909
## Education 0.015669878
## 1|2 0.239022569
## 2|3 0.206564128
## 3|4 0.210677500
## 4|5 0.221892299
## t value
## Traditional_Political_News_Programs_GLM 2.9754557
## Entertainment_or_Opinion_Political_News_Programs_GLM 10.2465637
## Expressly_Political_Entertainment_Programs_GLM 3.5498885
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -3.2337588
## Apolitical_Entertainment_Programs_GLM -2.0344193
## Race2 -2.1721650
## Race3 -6.0744931
## Race4 -0.1110933
## Race5 -0.7130454
## Race6 1.2719391
## Partisanship 9.5627587
## Income -0.6680001
## Age 10.6903733
## Gender2 -8.6752734
## Gender3 -0.4574498
## Education 11.6485665
## 1|2 -3.7463577
## 2|3 10.7575771
## 3|4 16.0893471
## 4|5 23.9530861
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value
## Traditional_Political_News_Programs_GLM 0.057269732
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.211860445
## Expressly_Political_Entertainment_Programs_GLM 0.161582567
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.077966635
## Apolitical_Entertainment_Programs_GLM -0.044351714
## Race2 -0.243256369
## Race3 -1.111221153
## Race4 -0.061098981
## Race5 -0.076779921
## Race6 0.211499104
## Partisanship 0.292763700
## Income -0.002960909
## Age 0.112496883
## Gender2 -0.557002676
## Gender3 -0.286976501
## Education 0.182531620
## 1|2 -0.895464031
## 2|3 2.222129542
## 3|4 3.389663433
## 4|5 5.315005345
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019247382
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020676243
## Expressly_Political_Entertainment_Programs_GLM 0.045517645
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024110220
## Apolitical_Entertainment_Programs_GLM 0.021800675
## Race2 0.111987981
## Race3 0.182932327
## Race4 0.549978949
## Race5 0.107678868
## Race6 0.166280846
## Partisanship 0.030614984
## Income 0.004432498
## Age 0.010523195
## Gender2 0.064205778
## Gender3 0.627339909
## Education 0.015669878
## 1|2 0.239022569
## 2|3 0.206564128
## 3|4 0.210677500
## 4|5 0.221892299
## t value
## Traditional_Political_News_Programs_GLM 2.9754557
## Entertainment_or_Opinion_Political_News_Programs_GLM 10.2465637
## Expressly_Political_Entertainment_Programs_GLM 3.5498885
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -3.2337588
## Apolitical_Entertainment_Programs_GLM -2.0344193
## Race2 -2.1721650
## Race3 -6.0744931
## Race4 -0.1110933
## Race5 -0.7130454
## Race6 1.2719391
## Partisanship 9.5627587
## Income -0.6680001
## Age 10.6903733
## Gender2 -8.6752734
## Gender3 -0.4574498
## Education 11.6485665
## 1|2 -3.7463577
## 2|3 10.7575771
## 3|4 16.0893471
## 4|5 23.9530861
## p value
## Traditional_Political_News_Programs_GLM 2.925536e-03
## Entertainment_or_Opinion_Political_News_Programs_GLM 1.226258e-24
## Expressly_Political_Entertainment_Programs_GLM 3.853943e-04
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 1.221726e-03
## Apolitical_Entertainment_Programs_GLM 4.190933e-02
## Race2 2.984322e-02
## Race3 1.243800e-09
## Race4 9.115424e-01
## Race5 4.758177e-01
## Race6 2.033948e-01
## Partisanship 1.146588e-21
## Income 5.041335e-01
## Age 1.129156e-26
## Gender2 4.125581e-18
## Gender3 6.473478e-01
## Education 2.333510e-31
## 1|2 1.794207e-04
## 2|3 5.458647e-27
## 3|4 3.030159e-58
## 4|5 8.580706e-127
stargazer(Shows_AP_GLM, type = "html", out = "Shows_AP_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Attention_P</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.057<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.019)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.212<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.021)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.162<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.046)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>-0.078<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.024)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.044<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.022)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.243<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.112)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-1.111<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.183)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>-0.061</td></tr>
## <tr><td style="text-align:left"></td><td>(0.550)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.077</td></tr>
## <tr><td style="text-align:left"></td><td>(0.108)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.211</td></tr>
## <tr><td style="text-align:left"></td><td>(0.166)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.293<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.031)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>-0.003</td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.112<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.011)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.557<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.287</td></tr>
## <tr><td style="text-align:left"></td><td>(0.627)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.183<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.016)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,485</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_AP_GLM_EXP <- exp(coef(Shows_AP_GLM))
Shows_AP_GLM_Prob1 <- Shows_AP_GLM_EXP - 1
Shows_AP_GLM_Prob2 <- Shows_AP_GLM_Prob1 * 100
Shows_AP_GLM_Prob2
## Traditional_Political_News_Programs_GLM
## 5.894140
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 23.597539
## Expressly_Political_Entertainment_Programs_GLM
## 17.536950
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## -7.500471
## Apolitical_Entertainment_Programs_GLM
## -4.338256
## Race2
## -21.592952
## Race3
## -67.084324
## Race4
## -5.926988
## Race5
## -7.390636
## Race6
## 23.552886
## Partisanship
## 34.012608
## Income
## -0.295653
## Age
## 11.906877
## Gender2
## -42.707627
## Gender3
## -24.947064
## Education
## 20.025210
tab_df(Shows_AP_GLM_Prob2, file = "Shows_AP_GLM_sj1")
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
5.89
|
23.60
|
17.54
|
-7.50
|
-4.34
|
-21.59
|
-67.08
|
-5.93
|
-7.39
|
23.55
|
34.01
|
-0.30
|
11.91
|
-42.71
|
-24.95
|
20.03
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_AP_GLM_GG <- ggpredict(Shows_AP_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_AP_GLM_P <- plot(News_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
News_AP_GLM_P
Opinion_AP_GLM_GG <- ggpredict(Shows_AP_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_AP_GLM_P <- plot(Opinion_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_AP_GLM_P
Entertainment_AP_GLM_GG <- ggpredict(Shows_AP_GLM, terms = "Expressly_Political_Entertainment_Programs_GLM")
Entertainment_AP_GLM_P <- plot(Entertainment_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of Political Entertainment Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Entertainment_AP_GLM_P
Issue_AP_GLM_GG <- ggpredict(Shows_AP_GLM, terms = "Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM")
Issue_AP_GLM_P <- plot(Issue_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of Issue Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Issue_AP_GLM_P
Apolitical_AP_GLM_GG <- ggpredict(Shows_AP_GLM, terms = "Apolitical_Entertainment_Programs_GLM")
Apolitical_AP_GLM_P <- plot(Apolitical_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of Apolitical Entertainment Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Apolitical_AP_GLM_P
All_AP_GLM_GG <- ggpredict(All_AP_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_AP_GLM_P<- plot(All_AP_GLM_GG) + ggtitle(" ") + ylab ("Predicted Probablity that the Respondent Pays Attention to Politics more often") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_AP_GLM_P
### Creates a logistic regression model for all 6 groups of shows against all 10 dependent variables with control variables then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_IC_GLM_C <- polr(Interest_C ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(All_IC_GLM_C)))
## Value Std. Error t value
## All_Programs_GLM 0.073456364 0.007906472 9.2906622
## Race2 -0.115089836 0.121466708 -0.9475011
## Race3 -0.905196166 0.190984938 -4.7396207
## Race4 0.142944124 0.545514478 0.2620354
## Race5 0.146572899 0.117313095 1.2494164
## Race6 0.234919629 0.181671461 1.2931014
## Partisanship 0.333214478 0.033397608 9.9771960
## Income 0.004200045 0.004874017 0.8617214
## Age 0.145425948 0.010773799 13.4981120
## Gender2 -0.362456550 0.070965134 -5.1075300
## Gender3 0.286749953 0.688490677 0.4164907
## Education 0.163244645 0.016953921 9.6287250
## 1|2 1.776818892 0.218499346 8.1319186
## 2|3 4.039650263 0.227039776 17.7926984
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value Std. Error t value p value
## All_Programs_GLM 0.073456364 0.007906472 9.2906622 1.533313e-20
## Race2 -0.115089836 0.121466708 -0.9475011 3.433835e-01
## Race3 -0.905196166 0.190984938 -4.7396207 2.141187e-06
## Race4 0.142944124 0.545514478 0.2620354 7.932941e-01
## Race5 0.146572899 0.117313095 1.2494164 2.115128e-01
## Race6 0.234919629 0.181671461 1.2931014 1.959760e-01
## Partisanship 0.333214478 0.033397608 9.9771960 1.918106e-23
## Income 0.004200045 0.004874017 0.8617214 3.888408e-01
## Age 0.145425948 0.010773799 13.4981120 1.604350e-41
## Gender2 -0.362456550 0.070965134 -5.1075300 3.263972e-07
## Gender3 0.286749953 0.688490677 0.4164907 6.770510e-01
## Education 0.163244645 0.016953921 9.6287250 6.047520e-22
## 1|2 1.776818892 0.218499346 8.1319186 4.225486e-16
## 2|3 4.039650263 0.227039776 17.7926984 8.050787e-71
stargazer(All_IC_GLM_C, type = "html", out = "All_IC_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Interest_C</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.073<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.115</td></tr>
## <tr><td style="text-align:left"></td><td>(0.121)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.905<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.191)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.143</td></tr>
## <tr><td style="text-align:left"></td><td>(0.546)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.147</td></tr>
## <tr><td style="text-align:left"></td><td>(0.117)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.235</td></tr>
## <tr><td style="text-align:left"></td><td>(0.182)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.333<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.033)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.004</td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.145<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.011)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.362<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.071)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.287</td></tr>
## <tr><td style="text-align:left"></td><td>(0.688)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.163<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.017)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,485</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_IC_GLM_C_EXP <- exp(coef(All_IC_GLM_C))
All_IC_GLM_C_Prob1 <-All_IC_GLM_C_EXP - 1
All_IC_GLM_C_Prob2 <- All_IC_GLM_C_Prob1 * 100
All_IC_GLM_C_Prob2
## All_Programs_GLM Race2 Race3 Race4
## 7.6221573 -10.8713929 -59.5537464 15.3665338
## Race5 Race6 Partisanship Income
## 15.7859334 26.4807111 39.5446559 0.4208877
## Age Gender2 Gender3 Education
## 15.6532088 -30.4035448 33.2091087 17.7324681
tab_df(All_IC_GLM_C_Prob2, file = "All_IC_sj1")
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
7.62
|
-10.87
|
-59.55
|
15.37
|
15.79
|
26.48
|
39.54
|
0.42
|
15.65
|
-30.40
|
33.21
|
17.73
|
Shows_IC_GLM <- polr(Interest_C ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(Shows_IC_GLM)))
## Value
## Traditional_Political_News_Programs_GLM 0.074753569
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.253683381
## Expressly_Political_Entertainment_Programs_GLM 0.206013675
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.079954553
## Apolitical_Entertainment_Programs_GLM -0.037156186
## Race2 -0.215386363
## Race3 -0.990015356
## Race4 0.077496713
## Race5 0.048686600
## Race6 0.183153031
## Partisanship 0.318621422
## Income 0.001888737
## Age 0.131250230
## Gender2 -0.328156858
## Gender3 0.217298385
## Education 0.146376048
## 1|2 1.343310053
## 2|3 3.644165288
## Std. Error
## Traditional_Political_News_Programs_GLM 0.022618375
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.025746278
## Expressly_Political_Entertainment_Programs_GLM 0.052740497
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.027440152
## Apolitical_Entertainment_Programs_GLM 0.024948561
## Race2 0.123266819
## Race3 0.192463598
## Race4 0.557477324
## Race5 0.118987891
## Race6 0.182823954
## Partisanship 0.033842158
## Income 0.004962176
## Age 0.011715009
## Gender2 0.071738773
## Gender3 0.687208415
## Education 0.017363150
## 1|2 0.225924202
## 2|3 0.233457555
## t value
## Traditional_Political_News_Programs_GLM 3.3049929
## Entertainment_or_Opinion_Political_News_Programs_GLM 9.8532061
## Expressly_Political_Entertainment_Programs_GLM 3.9061762
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -2.9137796
## Apolitical_Entertainment_Programs_GLM -1.4893118
## Race2 -1.7473182
## Race3 -5.1439096
## Race4 0.1390132
## Race5 0.4091727
## Race6 1.0018000
## Partisanship 9.4149262
## Income 0.3806267
## Age 11.2035961
## Gender2 -4.5743305
## Gender3 0.3162045
## Education 8.4302706
## 1|2 5.9458440
## 2|3 15.6095411
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value
## Traditional_Political_News_Programs_GLM 0.074753569
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.253683381
## Expressly_Political_Entertainment_Programs_GLM 0.206013675
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.079954553
## Apolitical_Entertainment_Programs_GLM -0.037156186
## Race2 -0.215386363
## Race3 -0.990015356
## Race4 0.077496713
## Race5 0.048686600
## Race6 0.183153031
## Partisanship 0.318621422
## Income 0.001888737
## Age 0.131250230
## Gender2 -0.328156858
## Gender3 0.217298385
## Education 0.146376048
## 1|2 1.343310053
## 2|3 3.644165288
## Std. Error
## Traditional_Political_News_Programs_GLM 0.022618375
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.025746278
## Expressly_Political_Entertainment_Programs_GLM 0.052740497
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.027440152
## Apolitical_Entertainment_Programs_GLM 0.024948561
## Race2 0.123266819
## Race3 0.192463598
## Race4 0.557477324
## Race5 0.118987891
## Race6 0.182823954
## Partisanship 0.033842158
## Income 0.004962176
## Age 0.011715009
## Gender2 0.071738773
## Gender3 0.687208415
## Education 0.017363150
## 1|2 0.225924202
## 2|3 0.233457555
## t value
## Traditional_Political_News_Programs_GLM 3.3049929
## Entertainment_or_Opinion_Political_News_Programs_GLM 9.8532061
## Expressly_Political_Entertainment_Programs_GLM 3.9061762
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -2.9137796
## Apolitical_Entertainment_Programs_GLM -1.4893118
## Race2 -1.7473182
## Race3 -5.1439096
## Race4 0.1390132
## Race5 0.4091727
## Race6 1.0018000
## Partisanship 9.4149262
## Income 0.3806267
## Age 11.2035961
## Gender2 -4.5743305
## Gender3 0.3162045
## Education 8.4302706
## 1|2 5.9458440
## 2|3 15.6095411
## p value
## Traditional_Political_News_Programs_GLM 9.497881e-04
## Entertainment_or_Opinion_Political_News_Programs_GLM 6.639164e-23
## Expressly_Political_Entertainment_Programs_GLM 9.376814e-05
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 3.570820e-03
## Apolitical_Entertainment_Programs_GLM 1.364053e-01
## Race2 8.058215e-02
## Race3 2.690790e-07
## Race4 8.894397e-01
## Race5 6.824129e-01
## Race6 3.164402e-01
## Partisanship 4.734150e-21
## Income 7.034803e-01
## Age 3.915113e-29
## Gender2 4.777452e-06
## Gender3 7.518473e-01
## Education 3.448669e-17
## 1|2 2.750360e-09
## 2|3 6.268628e-55
stargazer(Shows_IC_GLM, type = "html", out = "Shows_IC_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Interest_C</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.075<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.023)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.254<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.026)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.206<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.053)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>-0.080<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.027)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.037</td></tr>
## <tr><td style="text-align:left"></td><td>(0.025)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.215<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.123)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.990<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.192)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.077</td></tr>
## <tr><td style="text-align:left"></td><td>(0.557)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.049</td></tr>
## <tr><td style="text-align:left"></td><td>(0.119)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.183</td></tr>
## <tr><td style="text-align:left"></td><td>(0.183)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.319<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.034)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.002</td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.131<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.328<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.072)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.217</td></tr>
## <tr><td style="text-align:left"></td><td>(0.687)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.146<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.017)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,485</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_IC_GLM_EXP <- exp(coef(Shows_IC_GLM))
Shows_IC_GLM_Prob1 <- Shows_IC_GLM_EXP - 1
Shows_IC_GLM_Prob2 <- Shows_IC_GLM_Prob1 * 100
Shows_IC_GLM_Prob2
## Traditional_Political_News_Programs_GLM
## 7.7618559
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 28.8763693
## Expressly_Political_Entertainment_Programs_GLM
## 22.8770008
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## -7.6841700
## Apolitical_Entertainment_Programs_GLM
## -3.6474366
## Race2
## -19.3770117
## Race3
## -62.8429015
## Race4
## 8.0578681
## Race5
## 4.9891263
## Race6
## 20.0998184
## Partisanship
## 37.5230594
## Income
## 0.1890521
## Age
## 14.0253071
## Gender2
## -27.9749966
## Gender3
## 24.2714854
## Education
## 15.7631432
tab_df(Shows_IC_GLM_Prob2, file = "Shows_IC_GLM_sj1")
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
7.76
|
28.88
|
22.88
|
-7.68
|
-3.65
|
-19.38
|
-62.84
|
8.06
|
4.99
|
20.10
|
37.52
|
0.19
|
14.03
|
-27.97
|
24.27
|
15.76
|
### Creates Probability Prediction Plots for the Statistically Signifigant Results ###
News_IC_GLM_GG <-ggpredict(Shows_IC_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_IC_GLM_P <- plot(News_IC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent is More Interested in Campaigns") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
News_IC_GLM_P
Opinion_IC_GLM_GG <- ggpredict(Shows_IC_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_IC_GLM_P <- plot(Opinion_IC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent is More Interested in Campaigns") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_IC_GLM_P
Entertainment_IC_GLM_GG <- ggpredict(Shows_IC_GLM, terms = "Expressly_Political_Entertainment_Programs_GLM")
Entertainment_IC_GLM_P <- plot(Entertainment_IC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent is More Interested in Campaigns") + xlab ("Number of Political Entertainment Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Entertainment_IC_GLM_P
Issue_IC_GLM_GG <- ggpredict(Shows_IC_GLM, terms = "Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM")
Issue_IC_GLM_P <- plot(Issue_IC_GLM_GG) + ggtitle(" ") + ylab ("Probablity of Intent to Vote") + xlab ("Number of Issue Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Issue_IC_GLM_P
All_IC_GLM_GG <- ggpredict(All_IC_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_IC_GLM_P<- plot(All_IC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent is More Interested in Campaigns") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_IC_GLM_P
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_HK_GLM_C <- glm(House_K ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_HK_GLM_C)
##
## Call:
## glm(formula = House_K ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3800 -1.0319 0.6216 0.7858 1.5877
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.831963 0.252771 -7.248 4.24e-13 ***
## All_Programs_GLM 0.019915 0.008935 2.229 0.025829 *
## Race2 0.027871 0.139055 0.200 0.841144
## Race3 -0.521710 0.221680 -2.353 0.018601 *
## Race4 0.620771 0.668789 0.928 0.353303
## Race5 -0.225122 0.130441 -1.726 0.084373 .
## Race6 0.165405 0.216904 0.763 0.445718
## Partisanship 0.113547 0.038660 2.937 0.003314 **
## Income 0.026971 0.005667 4.760 1.94e-06 ***
## Age 0.089785 0.012365 7.261 3.83e-13 ***
## Gender2 -0.297558 0.083457 -3.565 0.000363 ***
## Gender3 1.180713 1.080143 1.093 0.274346
## Education 0.143845 0.019813 7.260 3.87e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3884.1 on 3406 degrees of freedom
## Residual deviance: 3636.5 on 3394 degrees of freedom
## (863 observations deleted due to missingness)
## AIC: 3662.5
##
## Number of Fisher Scoring iterations: 4
stargazer(All_HK_GLM_C, type = "html", out = "All_HK_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>House_K</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.020<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.028</td></tr>
## <tr><td style="text-align:left"></td><td>(0.139)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.522<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.222)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.621</td></tr>
## <tr><td style="text-align:left"></td><td>(0.669)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.225<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.130)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.165</td></tr>
## <tr><td style="text-align:left"></td><td>(0.217)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.114<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.039)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.027<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.090<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.298<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.083)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>1.181</td></tr>
## <tr><td style="text-align:left"></td><td>(1.080)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.144<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-1.832<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.253)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,407</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,818.226</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,662.451</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_HK_GLM_C_EXP <- exp(coef(All_HK_GLM_C))
All_HK_GLM_C_Prob1 <- All_HK_GLM_C_EXP - 1
All_HK_GLM_C_Prob2 <- All_HK_GLM_C_Prob1 * 100
All_HK_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -83.990100 2.011456 2.826297 -40.649510
## Race4 Race5 Race6 Partisanship
## 86.036235 -20.158157 17.987112 12.024458
## Income Age Gender2 Gender3
## 2.733752 9.393920 -25.737013 225.669511
## Education
## 15.470461
tab_df(All_HK_GLM_C_Prob2, file = "All_HK_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-83.99
|
2.01
|
2.83
|
-40.65
|
86.04
|
-20.16
|
17.99
|
12.02
|
2.73
|
9.39
|
-25.74
|
225.67
|
15.47
|
Shows_HK_GLM <- glm(House_K ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_HK_GLM)
##
## Call:
## glm(formula = House_K ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5347 -1.0343 0.6089 0.7906 1.6514
##
## Coefficients:
## Estimate
## (Intercept) -1.648737
## Traditional_Political_News_Programs_GLM -0.034154
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.105801
## Expressly_Political_Entertainment_Programs_GLM 0.244959
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.001677
## Apolitical_Entertainment_Programs_GLM -0.045810
## Race2 -0.002791
## Race3 -0.525667
## Race4 0.632226
## Race5 -0.243702
## Race6 0.124422
## Partisanship 0.106230
## Income 0.025890
## Age 0.089630
## Gender2 -0.277430
## Gender3 1.133632
## Education 0.130610
## Std. Error
## (Intercept) 0.261126
## Traditional_Political_News_Programs_GLM 0.025625
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.028659
## Expressly_Political_Entertainment_Programs_GLM 0.065468
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.032248
## Apolitical_Entertainment_Programs_GLM 0.028907
## Race2 0.139913
## Race3 0.222392
## Race4 0.672507
## Race5 0.131627
## Race6 0.217848
## Partisanship 0.038977
## Income 0.005739
## Age 0.013425
## Gender2 0.083887
## Gender3 1.082799
## Education 0.020188
## z value
## (Intercept) -6.314
## Traditional_Political_News_Programs_GLM -1.333
## Entertainment_or_Opinion_Political_News_Programs_GLM 3.692
## Expressly_Political_Entertainment_Programs_GLM 3.742
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.052
## Apolitical_Entertainment_Programs_GLM -1.585
## Race2 -0.020
## Race3 -2.364
## Race4 0.940
## Race5 -1.851
## Race6 0.571
## Partisanship 2.725
## Income 4.511
## Age 6.676
## Gender2 -3.307
## Gender3 1.047
## Education 6.470
## Pr(>|z|)
## (Intercept) 2.72e-10 ***
## Traditional_Political_News_Programs_GLM 0.182584
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.000223 ***
## Expressly_Political_Entertainment_Programs_GLM 0.000183 ***
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.958518
## Apolitical_Entertainment_Programs_GLM 0.113018
## Race2 0.984085
## Race3 0.018094 *
## Race4 0.347164
## Race5 0.064104 .
## Race6 0.567904
## Partisanship 0.006421 **
## Income 6.44e-06 ***
## Age 2.45e-11 ***
## Gender2 0.000942 ***
## Gender3 0.295124
## Education 9.82e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3884.1 on 3406 degrees of freedom
## Residual deviance: 3611.2 on 3390 degrees of freedom
## (863 observations deleted due to missingness)
## AIC: 3645.2
##
## Number of Fisher Scoring iterations: 4
stargazer(Shows_HK_GLM, type = "html", out = "Shows_HK_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>House_K</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>-0.034</td></tr>
## <tr><td style="text-align:left"></td><td>(0.026)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.106<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.029)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.245<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.065)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>-0.002</td></tr>
## <tr><td style="text-align:left"></td><td>(0.032)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.046</td></tr>
## <tr><td style="text-align:left"></td><td>(0.029)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.003</td></tr>
## <tr><td style="text-align:left"></td><td>(0.140)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.526<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.222)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.632</td></tr>
## <tr><td style="text-align:left"></td><td>(0.673)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.244<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.132)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.124</td></tr>
## <tr><td style="text-align:left"></td><td>(0.218)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.106<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.039)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.026<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.090<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.013)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.277<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.084)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>1.134</td></tr>
## <tr><td style="text-align:left"></td><td>(1.083)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.131<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-1.649<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.261)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,407</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,805.602</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,645.204</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_HK_GLM_EXP <- exp(coef(Shows_HK_GLM))
Shows_HK_GLM_Prob1 <- Shows_HK_GLM_EXP - 1
Shows_HK_GLM_Prob2 <- Shows_HK_GLM_Prob1 * 100
Shows_HK_GLM_Prob2
## (Intercept)
## -80.7707464
## Traditional_Political_News_Programs_GLM
## -3.3577664
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 11.1600506
## Expressly_Political_Entertainment_Programs_GLM
## 27.7569245
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## -0.1675915
## Apolitical_Entertainment_Programs_GLM
## -4.4777000
## Race2
## -0.2787001
## Race3
## -40.8839046
## Race4
## 88.1795115
## Race5
## -21.6278663
## Race6
## 13.2493477
## Partisanship
## 11.2078167
## Income
## 2.6228314
## Age
## 9.3769630
## Gender2
## -24.2271647
## Gender3
## 210.6921672
## Education
## 13.9522747
tab_df(Shows_HK_GLM_Prob2, file = "Shows_HK_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-80.77
|
-3.36
|
11.16
|
27.76
|
-0.17
|
-4.48
|
-0.28
|
-40.88
|
88.18
|
-21.63
|
13.25
|
11.21
|
2.62
|
9.38
|
-24.23
|
210.69
|
13.95
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
Opinion_HK_GLM_GG <- ggpredict(Shows_HK_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_HK_GLM_P <- plot(Opinion_HK_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent knew Which\n Party had the most seats in the House") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Entertainment_HK_GLM_GG <- ggpredict(Shows_HK_GLM, terms = "Expressly_Political_Entertainment_Programs_GLM")
Entertainment_HK_GLM_P <- plot(Entertainment_HK_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent knew Which\n Party had the most seats in the House") + xlab ("Number of Political Entertainment Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_HK_GLM_GG <- ggpredict(All_HK_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_HK_GLM_P<- plot(All_HK_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent knew Which\n Party had the most seats in the House") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(Opinion_HK_GLM_P, Entertainment_HK_GLM_P, All_HK_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_SK_GLM_C <- glm(Senate_K ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_SK_GLM_C)
##
## Call:
## glm(formula = Senate_K ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0880 -1.2980 0.7467 0.8982 1.5089
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.404417 0.234533 -5.988 2.12e-09 ***
## All_Programs_GLM 0.005346 0.008114 0.659 0.509953
## Race2 0.162614 0.131186 1.240 0.215134
## Race3 -0.260210 0.209743 -1.241 0.214748
## Race4 0.114036 0.579134 0.197 0.843899
## Race5 0.221799 0.129145 1.717 0.085898 .
## Race6 0.377861 0.205784 1.836 0.066329 .
## Partisanship 0.120561 0.035829 3.365 0.000766 ***
## Income 0.016613 0.005250 3.164 0.001554 **
## Age 0.082880 0.011468 7.227 4.93e-13 ***
## Gender2 -0.292448 0.076487 -3.824 0.000132 ***
## Gender3 13.004059 185.469343 0.070 0.944103
## Education 0.090409 0.018268 4.949 7.46e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4288.9 on 3404 degrees of freedom
## Residual deviance: 4130.4 on 3392 degrees of freedom
## (865 observations deleted due to missingness)
## AIC: 4156.4
##
## Number of Fisher Scoring iterations: 12
stargazer(All_SK_GLM_C, type = "html", out = "All_SK_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Senate_K</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.005</td></tr>
## <tr><td style="text-align:left"></td><td>(0.008)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.163</td></tr>
## <tr><td style="text-align:left"></td><td>(0.131)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.260</td></tr>
## <tr><td style="text-align:left"></td><td>(0.210)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.114</td></tr>
## <tr><td style="text-align:left"></td><td>(0.579)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.222<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.129)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.378<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.206)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.121<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.036)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.017<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.083<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.011)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.292<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.076)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>13.004</td></tr>
## <tr><td style="text-align:left"></td><td>(185.469)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.090<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.018)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-1.404<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.235)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,405</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-2,065.180</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>4,156.360</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_SK_GLM_C_EXP <- exp(coef(All_SK_GLM_C))
All_SK_GLM_C_Prob1 <- All_SK_GLM_C_EXP - 1
All_SK_GLM_C_Prob2 <- All_SK_GLM_C_Prob1 * 100
All_SK_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -7.544897e+01 5.360513e-01 1.765829e+01 -2.291104e+01
## Race4 Race5 Race6 Partisanship
## 1.207927e+01 2.483203e+01 4.591598e+01 1.281297e+01
## Income Age Gender2 Gender3
## 1.675136e+00 8.641191e+00 -2.535657e+01 4.442116e+07
## Education
## 9.462187e+00
tab_df(All_SK_GLM_C_Prob2, file = "All_SK_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-75.45
|
0.54
|
17.66
|
-22.91
|
12.08
|
24.83
|
45.92
|
12.81
|
1.68
|
8.64
|
-25.36
|
44421163.95
|
9.46
|
Shows_SK_GLM <- glm(Senate_K ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_SK_GLM)
##
## Call:
## glm(formula = Senate_K ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1130 -1.2840 0.7316 0.9060 1.5611
##
## Coefficients:
## Estimate
## (Intercept) -1.293825
## Traditional_Political_News_Programs_GLM -0.060582
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.109179
## Expressly_Political_Entertainment_Programs_GLM 0.072148
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.013314
## Apolitical_Entertainment_Programs_GLM -0.029851
## Race2 0.138485
## Race3 -0.248504
## Race4 0.139358
## Race5 0.203985
## Race6 0.363756
## Partisanship 0.113299
## Income 0.016034
## Age 0.081283
## Gender2 -0.272996
## Gender3 12.975684
## Education 0.083594
## Std. Error
## (Intercept) 0.242727
## Traditional_Political_News_Programs_GLM 0.023269
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.025788
## Expressly_Political_Entertainment_Programs_GLM 0.055763
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.029359
## Apolitical_Entertainment_Programs_GLM 0.026542
## Race2 0.131850
## Race3 0.210015
## Race4 0.582147
## Race5 0.130100
## Race6 0.206603
## Partisanship 0.036097
## Income 0.005306
## Age 0.012473
## Gender2 0.076820
## Gender3 186.143004
## Education 0.018638
## z value
## (Intercept) -5.330
## Traditional_Political_News_Programs_GLM -2.604
## Entertainment_or_Opinion_Political_News_Programs_GLM 4.234
## Expressly_Political_Entertainment_Programs_GLM 1.294
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.453
## Apolitical_Entertainment_Programs_GLM -1.125
## Race2 1.050
## Race3 -1.183
## Race4 0.239
## Race5 1.568
## Race6 1.761
## Partisanship 3.139
## Income 3.022
## Age 6.517
## Gender2 -3.554
## Gender3 0.070
## Education 4.485
## Pr(>|z|)
## (Intercept) 9.80e-08 ***
## Traditional_Political_News_Programs_GLM 0.00923 **
## Entertainment_or_Opinion_Political_News_Programs_GLM 2.30e-05 ***
## Expressly_Political_Entertainment_Programs_GLM 0.19573
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.65020
## Apolitical_Entertainment_Programs_GLM 0.26074
## Race2 0.29357
## Race3 0.23670
## Race4 0.81081
## Race5 0.11690
## Race6 0.07830 .
## Partisanship 0.00170 **
## Income 0.00251 **
## Age 7.18e-11 ***
## Gender2 0.00038 ***
## Gender3 0.94443
## Education 7.29e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4288.9 on 3404 degrees of freedom
## Residual deviance: 4109.2 on 3388 degrees of freedom
## (865 observations deleted due to missingness)
## AIC: 4143.2
##
## Number of Fisher Scoring iterations: 12
stargazer(Shows_SK_GLM, type = "html", out = "Shows_SK_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Senate_K</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>-0.061<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.023)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.109<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.026)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.072</td></tr>
## <tr><td style="text-align:left"></td><td>(0.056)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>-0.013</td></tr>
## <tr><td style="text-align:left"></td><td>(0.029)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.030</td></tr>
## <tr><td style="text-align:left"></td><td>(0.027)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.138</td></tr>
## <tr><td style="text-align:left"></td><td>(0.132)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.249</td></tr>
## <tr><td style="text-align:left"></td><td>(0.210)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.139</td></tr>
## <tr><td style="text-align:left"></td><td>(0.582)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.204</td></tr>
## <tr><td style="text-align:left"></td><td>(0.130)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.364<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.207)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.113<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.036)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.016<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.081<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.273<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.077)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>12.976</td></tr>
## <tr><td style="text-align:left"></td><td>(186.143)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.084<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.019)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-1.294<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.243)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,405</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-2,054.583</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>4,143.166</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_SK_GLM_EXP <- exp(coef(Shows_SK_GLM))
Shows_SK_GLM_Prob1 <- Shows_SK_GLM_EXP - 1
Shows_SK_GLM_Prob2 <- Shows_SK_GLM_Prob1 * 100
Shows_SK_GLM_Prob2
## (Intercept)
## -7.257801e+01
## Traditional_Political_News_Programs_GLM
## -5.878357e+00
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 1.153621e+01
## Expressly_Political_Entertainment_Programs_GLM
## 7.481442e+00
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## -1.322539e+00
## Apolitical_Entertainment_Programs_GLM
## -2.940965e+00
## Race2
## 1.485322e+01
## Race3
## -2.200332e+01
## Race4
## 1.495361e+01
## Race5
## 2.262796e+01
## Race6
## 4.387225e+01
## Partisanship
## 1.199664e+01
## Income
## 1.616322e+00
## Age
## 8.467831e+00
## Gender2
## -2.389045e+01
## Gender3
## 4.317846e+07
## Education
## 8.718707e+00
tab_df(Shows_SK_GLM_Prob2, file = "Shows_SK_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-72.58
|
-5.88
|
11.54
|
7.48
|
-1.32
|
-2.94
|
14.85
|
-22.00
|
14.95
|
22.63
|
43.87
|
12.00
|
1.62
|
8.47
|
-23.89
|
43178458.73
|
8.72
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_SK_GLM_GG <-ggpredict(Shows_SK_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_SK_GLM_P <- plot(News_SK_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent Knew Which\n party had the most seats in the Senate") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_SK_GLM_GG <- ggpredict(Shows_SK_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_SK_GLM_P <- plot(Opinion_SK_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent Knew Which\n party had the most seats in the Senate") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(News_SK_GLM_P, Opinion_SK_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
### Creates a logistic regression model for all 6 groups of shows against all 10 dependent variables with control variables then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_TW_GLM_C <- polr(Trust_W ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(All_TW_GLM_C)))
## Value Std. Error t value
## All_Programs_GLM 0.0155808529 0.006761077 2.30449271
## Race2 0.5698857013 0.112525806 5.06448897
## Race3 0.7405223021 0.183691222 4.03134289
## Race4 0.0849349503 0.476234442 0.17834693
## Race5 0.8700543822 0.108628979 8.00941322
## Race6 0.1961634346 0.168775625 1.16227349
## Partisanship 0.1637838633 0.030616847 5.34946874
## Income 0.0008635154 0.004447287 0.19416675
## Age -0.0108507428 0.009684581 -1.12041430
## Gender2 0.1656073378 0.063945985 2.58980039
## Gender3 -0.0405311685 0.666998076 -0.06076654
## Education 0.0186407270 0.015396996 1.21067299
## 1|2 -1.0129352409 0.201933532 -5.01618146
## 2|3 1.1835661464 0.201223483 5.88184903
## 3|4 3.0512077148 0.207330630 14.71662783
## 4|5 5.3252871025 0.248327743 21.44459187
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value Std. Error t value p value
## All_Programs_GLM 0.0155808529 0.006761077 2.30449271 2.119500e-02
## Race2 0.5698857013 0.112525806 5.06448897 4.094973e-07
## Race3 0.7405223021 0.183691222 4.03134289 5.545906e-05
## Race4 0.0849349503 0.476234442 0.17834693 8.584505e-01
## Race5 0.8700543822 0.108628979 8.00941322 1.152570e-15
## Race6 0.1961634346 0.168775625 1.16227349 2.451244e-01
## Partisanship 0.1637838633 0.030616847 5.34946874 8.821280e-08
## Income 0.0008635154 0.004447287 0.19416675 8.460453e-01
## Age -0.0108507428 0.009684581 -1.12041430 2.625373e-01
## Gender2 0.1656073378 0.063945985 2.58980039 9.603160e-03
## Gender3 -0.0405311685 0.666998076 -0.06076654 9.515451e-01
## Education 0.0186407270 0.015396996 1.21067299 2.260208e-01
## 1|2 -1.0129352409 0.201933532 -5.01618146 5.270855e-07
## 2|3 1.1835661464 0.201223483 5.88184903 4.057083e-09
## 3|4 3.0512077148 0.207330630 14.71662783 5.041823e-49
## 4|5 5.3252871025 0.248327743 21.44459187 5.129420e-102
stargazer(All_TW_GLM_C, type = "html", out = "All_TW_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Trust_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.016<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.570<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.113)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>0.741<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.184)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.085</td></tr>
## <tr><td style="text-align:left"></td><td>(0.476)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.870<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.196</td></tr>
## <tr><td style="text-align:left"></td><td>(0.169)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.164<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.031)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.001</td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.011</td></tr>
## <tr><td style="text-align:left"></td><td>(0.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.166<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.041</td></tr>
## <tr><td style="text-align:left"></td><td>(0.667)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.019</td></tr>
## <tr><td style="text-align:left"></td><td>(0.015)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,481</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_TW_GLM_C_EXP <- exp(coef(All_TW_GLM_C))
All_TW_GLM_C_Prob1 <-All_TW_GLM_C_EXP - 1
All_TW_GLM_C_Prob2 <- All_TW_GLM_C_Prob1 * 100
All_TW_GLM_C_Prob2
## All_Programs_GLM Race2 Race3 Race4
## 1.57028673 76.80649523 109.70305119 8.86462482
## Race5 Race6 Partisanship Income
## 138.70406625 21.67257441 17.79596872 0.08638883
## Age Gender2 Gender3 Education
## -1.07920858 18.01096263 -3.97207665 1.88155499
tab_df(All_TW_GLM_C_Prob2, file = "All_TW_sj1")
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
1.57
|
76.81
|
109.70
|
8.86
|
138.70
|
21.67
|
17.80
|
0.09
|
-1.08
|
18.01
|
-3.97
|
1.88
|
Shows_TW_GLM <- polr(Trust_W ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(Shows_TW_GLM)))
## Value
## Traditional_Political_News_Programs_GLM 0.085803721
## Entertainment_or_Opinion_Political_News_Programs_GLM -0.073341353
## Expressly_Political_Entertainment_Programs_GLM 0.052230539
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.025301966
## Apolitical_Entertainment_Programs_GLM 0.044172972
## Race2 0.569340682
## Race3 0.707951364
## Race4 0.053198516
## Race5 0.875848427
## Race6 0.203137515
## Partisanship 0.166830919
## Income 0.000339318
## Age -0.005798193
## Gender2 0.148371921
## Gender3 -0.053566931
## Education 0.018181176
## 1|2 -1.005548812
## 2|3 1.204588797
## 3|4 3.080855551
## 4|5 5.355245192
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019203032
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020260579
## Expressly_Political_Entertainment_Programs_GLM 0.045531182
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024396181
## Apolitical_Entertainment_Programs_GLM 0.022261875
## Race2 0.113123580
## Race3 0.183843411
## Race4 0.479949529
## Race5 0.109478933
## Race6 0.169023748
## Partisanship 0.030719997
## Income 0.004474029
## Age 0.010517698
## Gender2 0.064141084
## Gender3 0.665554841
## Education 0.015660902
## 1|2 0.208486781
## 2|3 0.207864552
## 3|4 0.213821101
## 4|5 0.253757019
## t value
## Traditional_Political_News_Programs_GLM 4.46823818
## Entertainment_or_Opinion_Political_News_Programs_GLM -3.61990411
## Expressly_Political_Entertainment_Programs_GLM 1.14713779
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -1.03712815
## Apolitical_Entertainment_Programs_GLM 1.98424309
## Race2 5.03290899
## Race3 3.85083892
## Race4 0.11084190
## Race5 8.00015495
## Race6 1.20182825
## Partisanship 5.43069441
## Income 0.07584172
## Age -0.55127966
## Gender2 2.31321195
## Gender3 -0.08048462
## Education 1.16092776
## 1|2 -4.82308186
## 2|3 5.79506600
## 3|4 14.40856652
## 4|5 21.10383077
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value
## Traditional_Political_News_Programs_GLM 0.085803721
## Entertainment_or_Opinion_Political_News_Programs_GLM -0.073341353
## Expressly_Political_Entertainment_Programs_GLM 0.052230539
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -0.025301966
## Apolitical_Entertainment_Programs_GLM 0.044172972
## Race2 0.569340682
## Race3 0.707951364
## Race4 0.053198516
## Race5 0.875848427
## Race6 0.203137515
## Partisanship 0.166830919
## Income 0.000339318
## Age -0.005798193
## Gender2 0.148371921
## Gender3 -0.053566931
## Education 0.018181176
## 1|2 -1.005548812
## 2|3 1.204588797
## 3|4 3.080855551
## 4|5 5.355245192
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019203032
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020260579
## Expressly_Political_Entertainment_Programs_GLM 0.045531182
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024396181
## Apolitical_Entertainment_Programs_GLM 0.022261875
## Race2 0.113123580
## Race3 0.183843411
## Race4 0.479949529
## Race5 0.109478933
## Race6 0.169023748
## Partisanship 0.030719997
## Income 0.004474029
## Age 0.010517698
## Gender2 0.064141084
## Gender3 0.665554841
## Education 0.015660902
## 1|2 0.208486781
## 2|3 0.207864552
## 3|4 0.213821101
## 4|5 0.253757019
## t value
## Traditional_Political_News_Programs_GLM 4.46823818
## Entertainment_or_Opinion_Political_News_Programs_GLM -3.61990411
## Expressly_Political_Entertainment_Programs_GLM 1.14713779
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM -1.03712815
## Apolitical_Entertainment_Programs_GLM 1.98424309
## Race2 5.03290899
## Race3 3.85083892
## Race4 0.11084190
## Race5 8.00015495
## Race6 1.20182825
## Partisanship 5.43069441
## Income 0.07584172
## Age -0.55127966
## Gender2 2.31321195
## Gender3 -0.08048462
## Education 1.16092776
## 1|2 -4.82308186
## 2|3 5.79506600
## 3|4 14.40856652
## 4|5 21.10383077
## p value
## Traditional_Political_News_Programs_GLM 7.886646e-06
## Entertainment_or_Opinion_Political_News_Programs_GLM 2.947122e-04
## Expressly_Political_Entertainment_Programs_GLM 2.513247e-01
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 2.996761e-01
## Apolitical_Entertainment_Programs_GLM 4.722875e-02
## Race2 4.830925e-07
## Race3 1.177139e-04
## Race4 9.117417e-01
## Race5 1.242627e-15
## Race6 2.294301e-01
## Partisanship 5.613519e-08
## Income 9.395450e-01
## Age 5.814420e-01
## Gender2 2.071099e-02
## Gender3 9.358518e-01
## Education 2.456713e-01
## 1|2 1.413569e-06
## 2|3 6.829432e-09
## 3|4 4.570993e-47
## 4|5 7.334644e-99
stargazer(Shows_TW_GLM, type = "html", out = "Shows_TW_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Trust_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.086<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.019)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>-0.073<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.052</td></tr>
## <tr><td style="text-align:left"></td><td>(0.046)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>-0.025</td></tr>
## <tr><td style="text-align:left"></td><td>(0.024)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>0.044<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.022)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.569<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.113)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>0.708<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.184)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.053</td></tr>
## <tr><td style="text-align:left"></td><td>(0.480)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.876<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.109)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.203</td></tr>
## <tr><td style="text-align:left"></td><td>(0.169)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.167<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.031)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.0003</td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.006</td></tr>
## <tr><td style="text-align:left"></td><td>(0.011)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.148<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.054</td></tr>
## <tr><td style="text-align:left"></td><td>(0.666)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.018</td></tr>
## <tr><td style="text-align:left"></td><td>(0.016)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,481</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_TW_GLM_EXP <- exp(coef(Shows_TW_GLM))
Shows_TW_GLM_Prob1 <- Shows_TW_GLM_EXP - 1
Shows_TW_GLM_Prob2 <- Shows_TW_GLM_Prob1 * 100
Shows_TW_GLM_Prob2
## Traditional_Political_News_Programs_GLM
## 8.95924429
## Entertainment_or_Opinion_Political_News_Programs_GLM
## -7.07164375
## Expressly_Political_Entertainment_Programs_GLM
## 5.36186150
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## -2.49845536
## Apolitical_Entertainment_Programs_GLM
## 4.51631233
## Race2
## 76.71015859
## Race3
## 102.98286158
## Race4
## 5.46389871
## Race5
## 140.09114284
## Race6
## 22.52409455
## Partisanship
## 18.15544697
## Income
## 0.03393756
## Age
## -0.57814156
## Gender2
## 15.99442233
## Gender3
## -5.21575016
## Education
## 1.83474597
tab_df(Shows_TW_GLM_Prob2, file = "Shows_TW_GLM_sj1")
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
8.96
|
-7.07
|
5.36
|
-2.50
|
4.52
|
76.71
|
102.98
|
5.46
|
140.09
|
22.52
|
18.16
|
0.03
|
-0.58
|
15.99
|
-5.22
|
1.83
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_TW_GLM_GG <-ggpredict(Shows_TW_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_TW_GLM_P <- plot(News_TW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Trusts Washington More") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
News_TW_GLM_P
Opinion_TW_GLM_GG <- ggpredict(Shows_TW_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_TW_GLM_P <- plot(Opinion_TW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Trusts Washington More") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_TW_GLM_P
Apolitical_TW_GLM_GG <- ggpredict(Shows_TW_GLM, terms = "Apolitical_Entertainment_Programs_GLM")
Apolitical_TW_GLM_P <- plot(Apolitical_TW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Trusts Washington More") + xlab ("Number of Apolitical Entertainment Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
Apolitical_TW_GLM_P
All_TW_GLM_GG <- ggpredict(All_TW_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_TW_GLM_P <- plot(All_TW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Trusts Washington More") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_TW_GLM_P
### Creates a logistic regression model for all 6 groups of shows against all 10 dependent variables with control variables then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_GC_GLM_C <- polr(Government_C ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(All_GC_GLM_C)))
## Value Std. Error t value
## All_Programs_GLM 0.015070639 0.006750427 2.2325461
## Race2 0.351199576 0.111094841 3.1612591
## Race3 0.368983618 0.181502348 2.0329413
## Race4 -0.684777321 0.480325017 -1.4256541
## Race5 0.557353866 0.109500604 5.0899616
## Race6 -0.117099881 0.169574967 -0.6905493
## Partisanship 0.145321928 0.030372168 4.7847071
## Income 0.008169532 0.004429398 1.8443887
## Age 0.051958232 0.009589995 5.4179621
## Gender2 -0.212563560 0.063722566 -3.3357658
## Gender3 0.069972456 0.679652590 0.1029533
## Education 0.091541719 0.015297854 5.9839581
## 1|2 -1.345193610 0.211014121 -6.3748985
## 2|3 1.372905460 0.197302397 6.9583821
## 3|4 2.925864287 0.202438441 14.4531062
## 4|5 7.282710582 0.303216282 24.0182042
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value Std. Error t value p value
## All_Programs_GLM 0.015070639 0.006750427 2.2325461 2.557889e-02
## Race2 0.351199576 0.111094841 3.1612591 1.570887e-03
## Race3 0.368983618 0.181502348 2.0329413 4.205845e-02
## Race4 -0.684777321 0.480325017 -1.4256541 1.539682e-01
## Race5 0.557353866 0.109500604 5.0899616 3.581361e-07
## Race6 -0.117099881 0.169574967 -0.6905493 4.898488e-01
## Partisanship 0.145321928 0.030372168 4.7847071 1.712368e-06
## Income 0.008169532 0.004429398 1.8443887 6.512651e-02
## Age 0.051958232 0.009589995 5.4179621 6.028217e-08
## Gender2 -0.212563560 0.063722566 -3.3357658 8.506478e-04
## Gender3 0.069972456 0.679652590 0.1029533 9.180001e-01
## Education 0.091541719 0.015297854 5.9839581 2.177793e-09
## 1|2 -1.345193610 0.211014121 -6.3748985 1.830841e-10
## 2|3 1.372905460 0.197302397 6.9583821 3.442027e-12
## 3|4 2.925864287 0.202438441 14.4531062 2.396304e-47
## 4|5 7.282710582 0.303216282 24.0182042 1.794831e-127
stargazer(All_GC_GLM_C, type = "html", out = "All_GC_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Government_C</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.015<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.351<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.111)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>0.369<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.182)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>-0.685</td></tr>
## <tr><td style="text-align:left"></td><td>(0.480)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.557<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.110)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>-0.117</td></tr>
## <tr><td style="text-align:left"></td><td>(0.170)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.145<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.030)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.008<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.052<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.213<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.070</td></tr>
## <tr><td style="text-align:left"></td><td>(0.680)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.092<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.015)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,468</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_GC_GLM_C_EXP <- exp(coef(All_GC_GLM_C))
All_GC_GLM_C_Prob1 <-All_GC_GLM_C_EXP - 1
All_GC_GLM_C_Prob2 <- All_GC_GLM_C_Prob1 * 100
All_GC_GLM_C_Prob2
## All_Programs_GLM Race2 Race3 Race4
## 1.5184774 42.0770850 44.6263911 -49.5797507
## Race5 Race6 Partisanship Income
## 74.6046110 -11.0503655 15.6411791 0.8202993
## Age Gender2 Gender3 Education
## 5.3331746 -19.1491074 7.2478640 9.5862494
tab_df(All_GC_GLM_C_Prob2, file = "All_GC_sj1")
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
1.52
|
42.08
|
44.63
|
-49.58
|
74.60
|
-11.05
|
15.64
|
0.82
|
5.33
|
-19.15
|
7.25
|
9.59
|
Shows_GC_GLM <- polr(Government_C ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, Hess = TRUE)
(ctable <- coef(summary(Shows_GC_GLM)))
## Value
## Traditional_Political_News_Programs_GLM 0.0793570265
## Entertainment_or_Opinion_Political_News_Programs_GLM -0.0377938497
## Expressly_Political_Entertainment_Programs_GLM -0.0068571792
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.0131206160
## Apolitical_Entertainment_Programs_GLM -0.0008423052
## Race2 0.3504501313
## Race3 0.3387433496
## Race4 -0.7310955164
## Race5 0.5466095048
## Race6 -0.1214582419
## Partisanship 0.1509113613
## Income 0.0087251188
## Age 0.0467801537
## Gender2 -0.2256180111
## Gender3 0.0522444750
## Education 0.0931017249
## 1|2 -1.3734098235
## 2|3 1.3478110510
## 3|4 2.9059420091
## 4|5 7.2689143618
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019143646
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020316670
## Expressly_Political_Entertainment_Programs_GLM 0.045881630
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024299756
## Apolitical_Entertainment_Programs_GLM 0.022253108
## Race2 0.111648843
## Race3 0.182080853
## Race4 0.480610845
## Race5 0.110072962
## Race6 0.170074017
## Partisanship 0.030498336
## Income 0.004461236
## Age 0.010433370
## Gender2 0.063934731
## Gender3 0.676468420
## Education 0.015543516
## 1|2 0.217587210
## 2|3 0.204372430
## 3|4 0.209293558
## 4|5 0.307814306
## t value
## Traditional_Political_News_Programs_GLM 4.14534539
## Entertainment_or_Opinion_Political_News_Programs_GLM -1.86023842
## Expressly_Political_Entertainment_Programs_GLM -0.14945370
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.53994846
## Apolitical_Entertainment_Programs_GLM -0.03785113
## Race2 3.13886039
## Race3 1.86040072
## Race4 -1.52117982
## Race5 4.96588351
## Race6 -0.71414931
## Partisanship 4.94818342
## Income 1.95576268
## Age 4.48370496
## Gender2 -3.52888030
## Gender3 0.07723121
## Education 5.98974693
## 1|2 -6.31199704
## 2|3 6.59487705
## 3|4 13.88452674
## 4|5 23.61460859
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))
## Value
## Traditional_Political_News_Programs_GLM 0.0793570265
## Entertainment_or_Opinion_Political_News_Programs_GLM -0.0377938497
## Expressly_Political_Entertainment_Programs_GLM -0.0068571792
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.0131206160
## Apolitical_Entertainment_Programs_GLM -0.0008423052
## Race2 0.3504501313
## Race3 0.3387433496
## Race4 -0.7310955164
## Race5 0.5466095048
## Race6 -0.1214582419
## Partisanship 0.1509113613
## Income 0.0087251188
## Age 0.0467801537
## Gender2 -0.2256180111
## Gender3 0.0522444750
## Education 0.0931017249
## 1|2 -1.3734098235
## 2|3 1.3478110510
## 3|4 2.9059420091
## 4|5 7.2689143618
## Std. Error
## Traditional_Political_News_Programs_GLM 0.019143646
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.020316670
## Expressly_Political_Entertainment_Programs_GLM 0.045881630
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.024299756
## Apolitical_Entertainment_Programs_GLM 0.022253108
## Race2 0.111648843
## Race3 0.182080853
## Race4 0.480610845
## Race5 0.110072962
## Race6 0.170074017
## Partisanship 0.030498336
## Income 0.004461236
## Age 0.010433370
## Gender2 0.063934731
## Gender3 0.676468420
## Education 0.015543516
## 1|2 0.217587210
## 2|3 0.204372430
## 3|4 0.209293558
## 4|5 0.307814306
## t value
## Traditional_Political_News_Programs_GLM 4.14534539
## Entertainment_or_Opinion_Political_News_Programs_GLM -1.86023842
## Expressly_Political_Entertainment_Programs_GLM -0.14945370
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.53994846
## Apolitical_Entertainment_Programs_GLM -0.03785113
## Race2 3.13886039
## Race3 1.86040072
## Race4 -1.52117982
## Race5 4.96588351
## Race6 -0.71414931
## Partisanship 4.94818342
## Income 1.95576268
## Age 4.48370496
## Gender2 -3.52888030
## Gender3 0.07723121
## Education 5.98974693
## 1|2 -6.31199704
## 2|3 6.59487705
## 3|4 13.88452674
## 4|5 23.61460859
## p value
## Traditional_Political_News_Programs_GLM 3.393019e-05
## Entertainment_or_Opinion_Political_News_Programs_GLM 6.285180e-02
## Expressly_Political_Entertainment_Programs_GLM 8.811956e-01
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 5.892326e-01
## Apolitical_Entertainment_Programs_GLM 9.698064e-01
## Race2 1.696062e-03
## Race3 6.282885e-02
## Race4 1.282147e-01
## Race5 6.838900e-07
## Race6 4.751349e-01
## Partisanship 7.490930e-07
## Income 5.049312e-02
## Age 7.335807e-06
## Gender2 4.173218e-04
## Gender3 9.384396e-01
## Education 2.101678e-09
## 1|2 2.754575e-10
## 2|3 4.256084e-11
## 3|4 7.861792e-44
## 4|5 2.728235e-123
stargazer(Shows_GC_GLM, type = "html", out = "Shows_GC_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Government_C</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.079<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.019)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>-0.038<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>-0.007</td></tr>
## <tr><td style="text-align:left"></td><td>(0.046)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>0.013</td></tr>
## <tr><td style="text-align:left"></td><td>(0.024)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>-0.001</td></tr>
## <tr><td style="text-align:left"></td><td>(0.022)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.350<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.112)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>0.339<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.182)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>-0.731</td></tr>
## <tr><td style="text-align:left"></td><td>(0.481)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>0.547<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.110)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>-0.121</td></tr>
## <tr><td style="text-align:left"></td><td>(0.170)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.151<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.030)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.009<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>0.047<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>-0.226<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.064)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>0.052</td></tr>
## <tr><td style="text-align:left"></td><td>(0.676)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.093<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.016)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,468</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_GC_GLM_EXP <- exp(coef(Shows_GC_GLM))
Shows_GC_GLM_Prob1 <- Shows_GC_GLM_EXP - 1
Shows_GC_GLM_Prob2 <- Shows_GC_GLM_Prob1 * 100
Shows_GC_GLM_Prob2
## Traditional_Political_News_Programs_GLM
## 8.25907667
## Entertainment_or_Opinion_Political_News_Programs_GLM
## -3.70885751
## Expressly_Political_Entertainment_Programs_GLM
## -0.68337224
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## 1.32070690
## Apolitical_Entertainment_Programs_GLM
## -0.08419506
## Race2
## 41.97064591
## Race3
## 40.31831715
## Race4
## -51.86186600
## Race5
## 72.73863829
## Race6
## -11.43719653
## Partisanship
## 16.28935761
## Income
## 0.87632936
## Age
## 4.78916087
## Gender2
## -20.19771206
## Gender3
## 5.36332981
## Education
## 9.75733801
tab_df(Shows_GC_GLM_Prob2, file = "Shows_GC_GLM_sj1")
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
8.26
|
-3.71
|
-0.68
|
1.32
|
-0.08
|
41.97
|
40.32
|
-51.86
|
72.74
|
-11.44
|
16.29
|
0.88
|
4.79
|
-20.20
|
5.36
|
9.76
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_GC_GLM_GG <-ggpredict(Shows_GC_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_GC_GLM_P <- plot(News_GC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent Thinks\n the Government is less corrupt") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
News_GC_GLM_P
Opinion_GC_GLM_GG <- ggpredict(Shows_TW_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_GC_GLM_P <- plot(Opinion_GC_GLM_GG) + ggtitle(" ") + ylab ("PProbablity that the Respondent Thinks\n the Government is less corrupt") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_GC_GLM_P
All_GC_GLM_GG <- ggpredict(All_GC_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_GC_GLM_P <- plot(All_GC_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent Thinks\n the Government is less corrupt") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_GC_GLM_P
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_CW_GLM_C <- glm(Community_W ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_CW_GLM_C)
##
## Call:
## glm(formula = Community_W ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6551 -0.9124 -0.7135 1.2434 2.4371
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.716462 0.271383 -13.695 < 2e-16 ***
## All_Programs_GLM 0.045213 0.008548 5.289 1.23e-07 ***
## Race2 0.158267 0.140169 1.129 0.25885
## Race3 -0.660497 0.267635 -2.468 0.01359 *
## Race4 0.458200 0.612811 0.748 0.45464
## Race5 -0.034100 0.144019 -0.237 0.81283
## Race6 0.279579 0.207523 1.347 0.17791
## Partisanship 0.091698 0.039776 2.305 0.02114 *
## Income 0.005965 0.005699 1.047 0.29526
## Age -0.016609 0.012407 -1.339 0.18068
## Gender2 0.263173 0.082136 3.204 0.00135 **
## Gender3 1.951235 0.848794 2.299 0.02151 *
## Education 0.202095 0.020740 9.744 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3805.9 on 3000 degrees of freedom
## Residual deviance: 3613.0 on 2988 degrees of freedom
## (1269 observations deleted due to missingness)
## AIC: 3639
##
## Number of Fisher Scoring iterations: 4
stargazer(All_CW_GLM_C, type = "html", out = "All_CW_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Community_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.045<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.158</td></tr>
## <tr><td style="text-align:left"></td><td>(0.140)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.660<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.268)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.458</td></tr>
## <tr><td style="text-align:left"></td><td>(0.613)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.034</td></tr>
## <tr><td style="text-align:left"></td><td>(0.144)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.280</td></tr>
## <tr><td style="text-align:left"></td><td>(0.208)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.092<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.040)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.006</td></tr>
## <tr><td style="text-align:left"></td><td>(0.006)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.017</td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.263<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.082)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>1.951<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.849)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.202<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.021)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-3.716<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.271)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,001</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,806.493</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,638.985</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_CW_GLM_C_EXP <- exp(coef(All_CW_GLM_C))
All_CW_GLM_C_Prob1 <- All_CW_GLM_C_EXP - 1
All_CW_GLM_C_Prob2 <- All_CW_GLM_C_Prob1 * 100
All_CW_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -97.5680138 4.6250957 17.1478785 -48.3405457
## Race4 Race5 Race6 Partisanship
## 58.1224459 -3.3525437 32.2573216 9.6034187
## Income Age Gender2 Gender3
## 0.5982808 -1.6471494 30.1051690 603.7371670
## Education
## 22.3963743
tab_df(All_CW_GLM_C_Prob2, file = "All_CW_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-97.57
|
4.63
|
17.15
|
-48.34
|
58.12
|
-3.35
|
32.26
|
9.60
|
0.60
|
-1.65
|
30.11
|
603.74
|
22.40
|
Shows_CW_GLM <- glm(Community_W ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_CW_GLM)
##
## Call:
## glm(formula = Community_W ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6717 -0.9114 -0.7127 1.2460 2.4131
##
## Coefficients:
## Estimate
## (Intercept) -3.665970
## Traditional_Political_News_Programs_GLM 0.055020
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.048222
## Expressly_Political_Entertainment_Programs_GLM 0.059830
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.015119
## Apolitical_Entertainment_Programs_GLM 0.044735
## Race2 0.147885
## Race3 -0.667388
## Race4 0.444287
## Race5 -0.042746
## Race6 0.275458
## Partisanship 0.089092
## Income 0.005497
## Age -0.016501
## Gender2 0.265623
## Gender3 1.949501
## Education 0.199365
## Std. Error
## (Intercept) 0.280281
## Traditional_Political_News_Programs_GLM 0.023954
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.025239
## Expressly_Political_Entertainment_Programs_GLM 0.057128
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.031343
## Apolitical_Entertainment_Programs_GLM 0.028129
## Race2 0.140674
## Race3 0.267822
## Race4 0.613592
## Race5 0.144600
## Race6 0.207788
## Partisanship 0.039928
## Income 0.005735
## Age 0.013546
## Gender2 0.082427
## Gender3 0.848535
## Education 0.021079
## z value
## (Intercept) -13.080
## Traditional_Political_News_Programs_GLM 2.297
## Entertainment_or_Opinion_Political_News_Programs_GLM 1.911
## Expressly_Political_Entertainment_Programs_GLM 1.047
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.482
## Apolitical_Entertainment_Programs_GLM 1.590
## Race2 1.051
## Race3 -2.492
## Race4 0.724
## Race5 -0.296
## Race6 1.326
## Partisanship 2.231
## Income 0.958
## Age -1.218
## Gender2 3.223
## Gender3 2.297
## Education 9.458
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Traditional_Political_News_Programs_GLM 0.02163 *
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.05606 .
## Expressly_Political_Entertainment_Programs_GLM 0.29497
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.62953
## Apolitical_Entertainment_Programs_GLM 0.11175
## Race2 0.29314
## Race3 0.01271 *
## Race4 0.46902
## Race5 0.76753
## Race6 0.18495
## Partisanship 0.02566 *
## Income 0.33782
## Age 0.22317
## Gender2 0.00127 **
## Gender3 0.02159 *
## Education < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3805.9 on 3000 degrees of freedom
## Residual deviance: 3611.8 on 2984 degrees of freedom
## (1269 observations deleted due to missingness)
## AIC: 3645.8
##
## Number of Fisher Scoring iterations: 4
stargazer(Shows_CW_GLM, type = "html", out = "Shows_CW_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Community_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.055<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.024)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.048<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.025)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.060</td></tr>
## <tr><td style="text-align:left"></td><td>(0.057)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>0.015</td></tr>
## <tr><td style="text-align:left"></td><td>(0.031)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>0.045</td></tr>
## <tr><td style="text-align:left"></td><td>(0.028)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>0.148</td></tr>
## <tr><td style="text-align:left"></td><td>(0.141)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.667<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.268)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.444</td></tr>
## <tr><td style="text-align:left"></td><td>(0.614)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.043</td></tr>
## <tr><td style="text-align:left"></td><td>(0.145)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.275</td></tr>
## <tr><td style="text-align:left"></td><td>(0.208)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.089<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.040)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.005</td></tr>
## <tr><td style="text-align:left"></td><td>(0.006)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.017</td></tr>
## <tr><td style="text-align:left"></td><td>(0.014)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.266<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.082)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>1.950<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.849)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.199<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.021)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-3.666<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.280)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,001</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,805.918</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,645.836</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_CW_GLM_EXP <- exp(coef(Shows_CW_GLM))
Shows_CW_GLM_Prob1 <- Shows_CW_GLM_EXP - 1
Shows_CW_GLM_Prob2 <- Shows_CW_GLM_Prob1 * 100
Shows_CW_GLM_Prob2
## (Intercept)
## -97.442064
## Traditional_Political_News_Programs_GLM
## 5.656143
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 4.940316
## Expressly_Political_Entertainment_Programs_GLM
## 6.165570
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## 1.523405
## Apolitical_Entertainment_Programs_GLM
## 4.575083
## Race2
## 15.937922
## Race3
## -48.695297
## Race4
## 55.937728
## Race5
## -4.184497
## Race6
## 31.713375
## Partisanship
## 9.318110
## Income
## 0.551231
## Age
## -1.636603
## Gender2
## 30.424363
## Gender3
## 602.518160
## Education
## 22.062720
tab_df(Shows_CW_GLM_Prob2, file = "Shows_CW_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-97.44
|
5.66
|
4.94
|
6.17
|
1.52
|
4.58
|
15.94
|
-48.70
|
55.94
|
-4.18
|
31.71
|
9.32
|
0.55
|
-1.64
|
30.42
|
602.52
|
22.06
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_CW_GLM_GG <-ggpredict(Shows_CW_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_CW_GLM_P <- plot(News_CW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Did community work") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
Opinion_CW_GLM_GG <- ggpredict(Shows_CW_GLM, terms = "Entertainment_or_Opinion_Political_News_Programs_GLM")
Opinion_CW_GLM_P <- plot(Opinion_CW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Did community work") + xlab ("Number of Opinion Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_CW_GLM_GG <- ggpredict(All_CW_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_CW_GLM_P<- plot(All_CW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Did community work") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(News_CW_GLM_P, Opinion_CW_GLM_P, All_CW_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
### Creates a logistic regression model for all 5 groups of shows against all 10 dependent variables with control variables compared to the control group then summarizes the data in a table and then exponentiates the coefficients and then turns them into a percentage ###
All_VW_GLM_C <- glm(Volunteer_W ~ All_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(All_VW_GLM_C)
##
## Call:
## glm(formula = Volunteer_W ~ All_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6637 -1.0569 -0.7926 1.1584 2.1646
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.831588 0.248203 -11.408 < 2e-16 ***
## All_Programs_GLM 0.031056 0.008191 3.792 0.00015 ***
## Race2 -0.016972 0.134330 -0.126 0.89946
## Race3 -0.433811 0.233730 -1.856 0.06345 .
## Race4 0.548247 0.566259 0.968 0.33295
## Race5 -0.221138 0.134503 -1.644 0.10015
## Race6 0.079121 0.201096 0.393 0.69399
## Partisanship 0.073251 0.037182 1.970 0.04883 *
## Income 0.017318 0.005370 3.225 0.00126 **
## Age -0.019526 0.011642 -1.677 0.09351 .
## Gender2 0.174215 0.077351 2.252 0.02431 *
## Gender3 -0.496491 0.844593 -0.588 0.55664
## Education 0.177553 0.019196 9.250 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4127.1 on 3001 degrees of freedom
## Residual deviance: 3938.8 on 2989 degrees of freedom
## (1268 observations deleted due to missingness)
## AIC: 3964.8
##
## Number of Fisher Scoring iterations: 4
stargazer(All_VW_GLM_C, type = "html", out = "All_VW_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Volunteer_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">All_Programs_GLM</td><td>0.031<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.017</td></tr>
## <tr><td style="text-align:left"></td><td>(0.134)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.434<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.234)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.548</td></tr>
## <tr><td style="text-align:left"></td><td>(0.566)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.221</td></tr>
## <tr><td style="text-align:left"></td><td>(0.135)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.079</td></tr>
## <tr><td style="text-align:left"></td><td>(0.201)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.073<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.037)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.017<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.020<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.012)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.174<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.077)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.496</td></tr>
## <tr><td style="text-align:left"></td><td>(0.845)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.178<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.019)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-2.832<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.248)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,002</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,969.396</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,964.792</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
All_VW_GLM_C_EXP <- exp(coef(All_VW_GLM_C))
All_VW_GLM_C_Prob1 <- All_VW_GLM_C_EXP - 1
All_VW_GLM_C_Prob2 <- All_VW_GLM_C_Prob1 * 100
All_VW_GLM_C_Prob2
## (Intercept) All_Programs_GLM Race2 Race3
## -94.108080 3.154357 -1.682894 -35.196547
## Race4 Race5 Race6 Partisanship
## 73.021751 -19.839366 8.233558 7.600076
## Income Age Gender2 Gender3
## 1.746919 -1.933626 19.031119 -39.133751
## Education
## 19.429097
tab_df(All_VW_GLM_C_Prob2, file = "All_VW_sj1")
|
X.Intercept.
|
All_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-94.11
|
3.15
|
-1.68
|
-35.20
|
73.02
|
-19.84
|
8.23
|
7.60
|
1.75
|
-1.93
|
19.03
|
-39.13
|
19.43
|
Shows_VW_GLM <- glm(Volunteer_W ~ Traditional_Political_News_Programs_GLM + Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM + Apolitical_Entertainment_Programs_GLM + Race + Partisanship + Income + Age + Gender + Education, data = anes_clean, family = "binomial")
summary(Shows_VW_GLM)
##
## Call:
## glm(formula = Volunteer_W ~ Traditional_Political_News_Programs_GLM +
## Entertainment_or_Opinion_Political_News_Programs_GLM + Expressly_Political_Entertainment_Programs_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM +
## Apolitical_Entertainment_Programs_GLM + Race + Partisanship +
## Income + Age + Gender + Education, family = "binomial", data = anes_clean)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6430 -1.0545 -0.7953 1.1577 2.1616
##
## Coefficients:
## Estimate
## (Intercept) -2.793715
## Traditional_Political_News_Programs_GLM 0.054037
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.021051
## Expressly_Political_Entertainment_Programs_GLM 0.028101
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.001739
## Apolitical_Entertainment_Programs_GLM 0.033107
## Race2 -0.026312
## Race3 -0.450192
## Race4 0.522174
## Race5 -0.229996
## Race6 0.075999
## Partisanship 0.071706
## Income 0.017028
## Age -0.020170
## Gender2 0.173728
## Gender3 -0.489839
## Education 0.176215
## Std. Error
## (Intercept) 0.256550
## Traditional_Political_News_Programs_GLM 0.022976
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.024226
## Expressly_Political_Entertainment_Programs_GLM 0.055389
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.029631
## Apolitical_Entertainment_Programs_GLM 0.026870
## Race2 0.134811
## Race3 0.234011
## Race4 0.566821
## Race5 0.135073
## Race6 0.201400
## Partisanship 0.037334
## Income 0.005408
## Age 0.012685
## Gender2 0.077611
## Gender3 0.844608
## Education 0.019521
## z value
## (Intercept) -10.890
## Traditional_Political_News_Programs_GLM 2.352
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.869
## Expressly_Political_Entertainment_Programs_GLM 0.507
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.059
## Apolitical_Entertainment_Programs_GLM 1.232
## Race2 -0.195
## Race3 -1.924
## Race4 0.921
## Race5 -1.703
## Race6 0.377
## Partisanship 1.921
## Income 3.149
## Age -1.590
## Gender2 2.238
## Gender3 -0.580
## Education 9.027
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Traditional_Political_News_Programs_GLM 0.01868 *
## Entertainment_or_Opinion_Political_News_Programs_GLM 0.38487
## Expressly_Political_Entertainment_Programs_GLM 0.61192
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM 0.95320
## Apolitical_Entertainment_Programs_GLM 0.21790
## Race2 0.84525
## Race3 0.05438 .
## Race4 0.35693
## Race5 0.08862 .
## Race6 0.70591
## Partisanship 0.05477 .
## Income 0.00164 **
## Age 0.11183
## Gender2 0.02519 *
## Gender3 0.56194
## Education < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4127.1 on 3001 degrees of freedom
## Residual deviance: 3936.9 on 2985 degrees of freedom
## (1268 observations deleted due to missingness)
## AIC: 3970.9
##
## Number of Fisher Scoring iterations: 4
stargazer(Shows_VW_GLM, type = "html", out = "Shows_VW_GLM_star1")
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>Volunteer_W</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Traditional_Political_News_Programs_GLM</td><td>0.054<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.023)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_or_Opinion_Political_News_Programs_GLM</td><td>0.021</td></tr>
## <tr><td style="text-align:left"></td><td>(0.024)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Expressly_Political_Entertainment_Programs_GLM</td><td>0.028</td></tr>
## <tr><td style="text-align:left"></td><td>(0.055)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM</td><td>0.002</td></tr>
## <tr><td style="text-align:left"></td><td>(0.030)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Apolitical_Entertainment_Programs_GLM</td><td>0.033</td></tr>
## <tr><td style="text-align:left"></td><td>(0.027)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race2</td><td>-0.026</td></tr>
## <tr><td style="text-align:left"></td><td>(0.135)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race3</td><td>-0.450<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.234)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race4</td><td>0.522</td></tr>
## <tr><td style="text-align:left"></td><td>(0.567)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race5</td><td>-0.230<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.135)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Race6</td><td>0.076</td></tr>
## <tr><td style="text-align:left"></td><td>(0.201)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Partisanship</td><td>0.072<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.037)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Income</td><td>0.017<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Age</td><td>-0.020</td></tr>
## <tr><td style="text-align:left"></td><td>(0.013)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender2</td><td>0.174<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.078)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Gender3</td><td>-0.490</td></tr>
## <tr><td style="text-align:left"></td><td>(0.845)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Education</td><td>0.176<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.020)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-2.794<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.257)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>3,002</td></tr>
## <tr><td style="text-align:left">Log Likelihood</td><td>-1,968.434</td></tr>
## <tr><td style="text-align:left">Akaike Inf. Crit.</td><td>3,970.868</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>
Shows_VW_GLM_EXP <- exp(coef(Shows_VW_GLM))
Shows_VW_GLM_Prob1 <- Shows_VW_GLM_EXP - 1
Shows_VW_GLM_Prob2 <- Shows_VW_GLM_Prob1 * 100
Shows_VW_GLM_Prob2
## (Intercept)
## -93.8806563
## Traditional_Political_News_Programs_GLM
## 5.5523920
## Entertainment_or_Opinion_Political_News_Programs_GLM
## 2.1274593
## Expressly_Political_Entertainment_Programs_GLM
## 2.8499695
## Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
## 0.1740571
## Apolitical_Entertainment_Programs_GLM
## 3.3661376
## Race2
## -2.5969158
## Race3
## -36.2494137
## Race4
## 68.5688526
## Race5
## -20.5463078
## Race6
## 7.8961225
## Partisanship
## 7.4339907
## Income
## 1.7173525
## Age
## -1.9967784
## Gender2
## 18.9732398
## Gender3
## -38.7275254
## Education
## 19.2694165
tab_df(Shows_VW_GLM_Prob2, file = "Shows_VW_GLM_sj1")
|
X.Intercept.
|
Traditional_Political_News_Programs_GLM
|
Entertainment_or_Opinion_Political_News_Programs_GLM
|
Expressly_Political_Entertainment_Programs_GLM
|
Entertainment_Programs_that_Focus_on_a_salient_Political_Issue_GLM
|
Apolitical_Entertainment_Programs_GLM
|
Race2
|
Race3
|
Race4
|
Race5
|
Race6
|
Partisanship
|
Income
|
Age
|
Gender2
|
Gender3
|
Education
|
|
-93.88
|
5.55
|
2.13
|
2.85
|
0.17
|
3.37
|
-2.60
|
-36.25
|
68.57
|
-20.55
|
7.90
|
7.43
|
1.72
|
-2.00
|
18.97
|
-38.73
|
19.27
|
### Creates Probability Prediction Plots for the Statistically Significant Results ###
News_VW_GLM_GG <-ggpredict(Shows_VW_GLM, terms = "Traditional_Political_News_Programs_GLM")
News_VW_GLM_P <- plot(News_VW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Did volunteer work") + xlab ("Number of News Shows Watched") + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
All_VW_GLM_GG <- ggpredict(All_VW_GLM_C, terms = "All_Programs_GLM")
## Data were 'prettified'. Consider using `terms="All_Programs_GLM [all]"` to get smooth plots.
All_VW_GLM_P <- plot(All_VW_GLM_GG) + ggtitle(" ") + ylab ("Probablity that the Respondent\n Did volunteer work") + xlab ("Number of Shows Watched") + theme(panel.grid = element_blank(),plot.background = element_rect(fill = "white", color = "grey", size = 1))
grid.arrange(News_VW_GLM_P, All_VW_GLM_P) + theme(panel.grid = element_blank(), plot.background = element_rect(fill = "white", color = "grey", size = 1))
## NULL
TV_Ranked <- read.csv("C:/Users/Owner/Downloads/TV Ranked - Sheet1.csv") %>% as.data.frame()
TV_Ranked <- subset(TV_Ranked, select = -c(X,X.1))
TV_Rankedf <- as.data.frame(t(TV_Ranked))
krippalpha(TV_Rankedf)
##
## Krippendorff's alpha
##
## alpha coders units level
## 0.713 2 48 nominal
##
## Bootstrapped alpha
## Alpha Std. Error 2.5 % 97.5 % Boot. technique Bootstraps
## NA NA NA NA Krippendorff NA
## NA NA NA NA nonparametric NA
##
## P(alpha > alpha_min):
## alpha_min krippendorff nonparametric
## 0.90 NA NA
## 0.80 NA NA
## 0.70 NA NA
## 0.67 NA NA
## 0.60 NA NA
## 0.50 NA NA