How TV Affected Turnout and Registration in the 2016 Election
For my election data science final project my project partner and I chose to look at the effect that watching TV programs had on turnout in the 2016 election. To do this we used the ANES 2016 survey specifically its series of questions on what programs did the participant watch. We matched this up with the survey’s question on whether they voted in the 2016 election. I predicted that shows that were focused on politics would have a greater affect on turnout than shows that were apolitical. We broke the shows up into 5 categories: traditional news programs, opinion news or talk shows, entertainment shows that are expressly political, shows that are not expressly political but focus on a salient political issue (in every instance that issue is criminal justice), and shows that are apolitical. We ran a linear regression for each group but did not group the shows allowing us to see how each show affected turnout, all the variables together, each category grouped, and all the categories grouped together. Below is the full code.
Libraries
library(ggplot2)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(anesr)
library(coefplot)## Warning: package 'coefplot' was built under R version 4.0.3Data
data("timeseries_2016")
anes16 <- timeseries_2016
TV <- anes16 %>% select(V161364, V161365, V161366, V161367, V161368, V161369, V161370, V161371, V161372, V161373, V161374, V161375, V161376, V161377, V161378, V161379, V161380, V161381, V161382, V161383, V161384, V161385, V161386, V161387, V161388, V161389, V161390, V161391, V161392, V161393, V161394, V161395, V161396, V161397, V161398, V161399, V161400, V161401, V161402, V161403, V161404, V161405, V161406, V161407, V161408, V161409, V161410, V161411, V162034)
Did_You_register <- anes16 %>% select(V161011)
Did_You_Vote <- anes16 %>% select(V162034)Data Grouped
Traditional_Political_News_Programs <- TV %>% select(V161364, V161367, V161380, V161384, V161388, V161390, V161396, V161399, V161405)
Entertainment_or_Opinion_Political_News_Programs <- TV %>% select(V161365, V161370, V161371, V161372, V161375, V161379, V161381, V161382, V161386,V161391, V161393, V161400, V161403, V161404, V161409)
Entertainment_Programs_that_are_Expressly_Political <- TV %>% select(V161385, V161389, V161402, V161406,V161411)
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue <- TV %>% select(V161366, V161368, V161374, V161377, V161387, V161392, V161397, V161401)
Entertainment_Programs_with_little_to_No_Political_Content <- TV %>% select(V161369, V161373, V161376, V161378, V161383, V161394, V161395, V161398, V161407, V161410)
Twenty_Twenty <- TV %>% select (V161364) %>% unlist()
All_In_with_Chris_Hayes <- TV %>% select (V161365) %>% unlist()
The_Blacklist <- TV %>% select (V161366) %>% unlist()
CBS_Evening_News_with_Scott_Pelley <- TV %>% select (V161367) %>% unlist()
Criminal_Minds <- TV %>% select (V161368) %>% unlist()
Empire <- TV %>% select (V161369) %>% unlist()
Hannity <- TV %>% select (V161370) %>% unlist()
Jimmy_Kimmel_Live <- TV %>% select (V161371) %>% unlist()
The_Kelly_File <- TV %>% select (V161372) %>% unlist()
Modern_Family <- TV %>% select (V161373) %>% unlist()
NCIS <- TV %>% select (V161374) %>% unlist()
The_Nightly_Show_with_Larry_Wilmore <- TV %>% select (V161375) %>% unlist()
Sunday_Night_Football <- TV %>% select (V161376) %>% unlist()
Scorpion <- TV %>% select (V161377) %>% unlist()
The_Simpsons <- TV %>% select (V161378) %>% unlist()
Today <- TV %>% select (V161379) %>% unlist()
Sixty_Minutes <- TV %>% select (V161380) %>% unlist()
Anderson_Cooper_Three_Hundred_and_Sixty <- TV %>% select (V161381) %>% unlist()
CBS_This_Morning <- TV %>% select (V161382) %>% unlist()
Dancing_with_the_Stars <- TV %>% select (V161383) %>% unlist()
Face_the_Nation <- TV %>% select (V161384) %>% unlist()
House_of_Cards <- TV %>% select (V161385) %>% unlist()
Hardball_with_Chris_Matthews <- TV %>% select (V161386) %>% unlist()
Judge_Judy <- TV %>% select (V161387) %>% unlist()
Meet_the_Press <- TV %>% select (V161388) %>% unlist()
Game_of_Thrones <- TV %>% select (V161389) %>% unlist()
NBC_Nightly_News_with_Lester_Holt <- TV %>% select (V161390) %>% unlist()
On_the_Record_with_Greta_Van_Susteren <- TV %>% select (V161391) %>% unlist()
Daredevil <- TV %>% select (V161392) %>% unlist()
The_Rachel_Maddow_Show <- TV %>% select (V161393) %>% unlist()
Shark_Tank <- TV %>% select (V161394) %>% unlist()
The_Voice <- TV %>% select(V161395) %>% unlist()
ABC_World_News_with_David_Muir <- TV %>% select (V161396) %>% unlist()
Blue_bloods <- TV %>% select (V161397) %>% unlist()
Conan <- TV %>% select (V161398) %>% unlist()
Dateline_NBC <- TV %>% select (V161399) %>% unlist()
Good_Morning_America <- TV %>% select (V161400) %>% unlist()
Hawaii_Five_O <- TV %>% select (V161401) %>% unlist()
Madam_Secretary <- TV %>% select (V161402) %>% unlist()
Nancy_Grace <- TV %>% select (V161403) %>% unlist()
Erin_Burnett_OutFront <- TV %>% select (V161404) %>% unlist()
PBS_News_Hour <- TV %>% select (V161405) %>% unlist()
Scandal <- TV %>% select (V161406) %>% unlist()
The_Big_Bang_Theory <- TV %>% select (V161407) %>% unlist()
The_Late_Show_with_Stephen_Colbert <- TV %>% select(V161408) %>% unlist()
The_O_Reilly_Factor <- TV %>% select (V161409) %>% unlist()
The_Tonight_Show_Starring_Jimmy_Fallon <- TV %>% select(V161410) %>% unlist()
Alpha_House <- TV %>% select(V161411) %>% unlist()Regression for Traditional Political News Programs and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Traditional_Political_News_Programs_Moded_vote <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Traditional_Political_News_Programs_Moded_vote_lm <- lm(vote ~ 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 , data = anes_clean)
summary(Traditional_Political_News_Programs_Moded_vote_lm)##
## Call:
## lm(formula = vote ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99346 0.01256 0.01387 0.01620 0.03215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.986125 0.002497 394.916 <2e-16 ***
## Twenty_Twenty -0.012407 0.006985 -1.776 0.0758 .
## CBS_Evening_News_with_Scott_Pelley -0.002323 0.006870 -0.338 0.7353
## Sixty_Minutes -0.001106 0.006263 -0.177 0.8598
## Face_the_Nation 0.007194 0.008645 0.832 0.4054
## Meet_the_Press -0.001906 0.008149 -0.234 0.8151
## NBC_Nightly_News_with_Lester_Holt 0.000358 0.006304 0.057 0.9547
## ABC_World_News_with_David_Muir -0.002442 0.006451 -0.378 0.7051
## Dateline_NBC 0.009295 0.006513 1.427 0.1537
## PBS_News_Hour 0.002024 0.007026 0.288 0.7733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1188 on 2720 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.002092, Adjusted R-squared: -0.00121
## F-statistic: 0.6335 on 9 and 2720 DF, p-value: 0.7692coefplot::coefplot(Traditional_Political_News_Programs_Moded_vote_lm)
Regression for Entertainment or Opinion Political News Programs and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_or_Opinion_Political_News_Programs_Moded_vote <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_lm <- lm(vote ~ 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 , data = anes_clean)
summary(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_lm)##
## Call:
## lm(formula = vote ~ 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, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99420 0.01009 0.01502 0.01581 0.05137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.984983 0.002870 343.204 <2e-16
## All_In_with_Chris_Hayes 0.010817 0.014365 0.753 0.4515
## Hannity 0.004842 0.009778 0.495 0.6205
## Jimmy_Kimmel_Live -0.013379 0.007707 -1.736 0.0827
## The_Kelly_File -0.005798 0.010049 -0.577 0.5640
## The_Nightly_Show_with_Larry_Wilmore -0.011037 0.014019 -0.787 0.4312
## Today 0.008840 0.006327 1.397 0.1625
## Anderson_Cooper_Three_Hundred_and_Sixty 0.004924 0.007310 0.674 0.5006
## CBS_This_Morning -0.004403 0.006562 -0.671 0.5024
## Hardball_with_Chris_Matthews -0.006582 0.011108 -0.593 0.5535
## On_the_Record_with_Greta_Van_Susteren -0.003124 0.010439 -0.299 0.7647
## The_Rachel_Maddow_Show 0.008883 0.010386 0.855 0.3925
## Good_Morning_America -0.006726 0.006057 -1.110 0.2670
## Nancy_Grace 0.005936 0.010851 0.547 0.5844
## Erin_Burnett_OutFront -0.003848 0.012282 -0.313 0.7541
## The_O_Reilly_Factor 0.009213 0.008934 1.031 0.3026
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1188 on 2714 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.00437, Adjusted R-squared: -0.001133
## F-statistic: 0.7941 on 15 and 2714 DF, p-value: 0.6855coefplot::coefplot(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_lm)
### Regression for Entertainment Programs that are Expressly Political and Turnout ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_Expressly_Political_Moded_vote <- as.data.frame(House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_lm <- lm(vote ~ House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House , data = anes_clean)
summary(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_lm)##
## Call:
## lm(formula = vote ~ House_of_Cards + Game_of_Thrones + Madam_Secretary +
## Scandal + Alpha_House, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.00082 0.01457 0.01457 0.01457 0.03094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9854341 0.0025930 380.032 <2e-16 ***
## House_of_Cards -0.0033592 0.0084258 -0.399 0.6902
## Game_of_Thrones -0.0076701 0.0071404 -1.074 0.2828
## Madam_Secretary -0.0006225 0.0076568 -0.081 0.9352
## Scandal 0.0153883 0.0083026 1.853 0.0639 .
## Alpha_House -0.0053473 0.0124206 -0.431 0.6669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1187 on 2724 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.001953, Adjusted R-squared: 0.0001213
## F-statistic: 1.066 on 5 and 2724 DF, p-value: 0.3771coefplot::coefplot(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_lm)
Regresion for Entertainment Programs that are not Expressly Political but Focus on a Salient Political issue and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote <- as.data.frame( The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_lm <- lm(vote ~ The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O, data = anes_clean)
summary(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_lm)##
## Call:
## lm(formula = vote ~ The_Blacklist + Criminal_Minds + NCIS + Scorpion +
## Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99434 0.01312 0.01479 0.01479 0.03306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9852146 0.0025579 385.171 <2e-16 ***
## The_Blacklist 0.0025467 0.0075855 0.336 0.737
## Criminal_Minds 0.0039816 0.0066164 0.602 0.547
## NCIS -0.0003444 0.0067856 -0.051 0.960
## Scorpion -0.0118022 0.0093471 -1.263 0.207
## Judge_Judy -0.0061318 0.0069481 -0.883 0.378
## Daredevil 0.0063661 0.0098564 0.646 0.518
## Blue_bloods 0.0051421 0.0075359 0.682 0.495
## Hawaii_Five_O -0.0013254 0.0091833 -0.144 0.885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1188 on 2721 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.001334, Adjusted R-squared: -0.001603
## F-statistic: 0.4542 on 8 and 2721 DF, p-value: 0.8885coefplot::coefplot(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_lm)
Regresion for Entertainment Programs with little to No Political Content and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_lm <- lm(vote ~ 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, data = anes_clean)
summary(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_lm)##
## Call:
## lm(formula = vote ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99580 0.00878 0.01596 0.01975 0.05268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9802456 0.0029046 337.475 < 2e-16
## Empire -0.0014625 0.0086926 -0.168 0.86640
## Modern_Family 0.0046404 0.0066628 0.696 0.48619
## Sunday_Night_Football 0.0037926 0.0050503 0.751 0.45273
## The_Simpsons 0.0030919 0.0098142 0.315 0.75275
## Dancing_with_the_Stars -0.0009274 0.0068528 -0.135 0.89236
## Shark_Tank -0.0020727 0.0065149 -0.318 0.75040
## The_Voice 0.0104126 0.0064250 1.621 0.10521
## Conan -0.0308494 0.0110720 -2.786 0.00537
## The_Big_Bang_Theory 0.0092566 0.0055350 1.672 0.09456
## The_Tonight_Show_Starring_Jimmy_Fallon 0.0013532 0.0065185 0.208 0.83556
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1186 on 2719 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.005242, Adjusted R-squared: 0.001584
## F-statistic: 1.433 on 10 and 2719 DF, p-value: 0.1592coefplot::coefplot(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_lm)
Regression for All Variables and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
All_Variables_Moded_vote <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_vote_lm <- lm(vote ~ 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, data = anes_clean)
summary(All_Variables_Moded_vote_lm)##
## Call:
## lm(formula = vote ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99759 0.00492 0.01618 0.02042 0.07468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.816e-01 3.688e-03 266.134 < 2e-16
## Twenty_Twenty -1.279e-02 7.297e-03 -1.753 0.07971
## CBS_Evening_News_with_Scott_Pelley 3.207e-04 7.404e-03 0.043 0.96546
## Sixty_Minutes -4.663e-04 6.427e-03 -0.073 0.94216
## Face_the_Nation 5.142e-03 9.039e-03 0.569 0.56949
## Meet_the_Press -4.506e-03 8.691e-03 -0.519 0.60415
## NBC_Nightly_News_with_Lester_Holt -1.907e-03 6.660e-03 -0.286 0.77466
## ABC_World_News_with_David_Muir 6.189e-05 6.970e-03 0.009 0.99292
## Dateline_NBC 7.106e-03 6.772e-03 1.049 0.29413
## PBS_News_Hour 2.804e-03 7.687e-03 0.365 0.71534
## All_In_with_Chris_Hayes 1.465e-02 1.465e-02 1.000 0.31736
## Hannity 6.404e-03 9.886e-03 0.648 0.51714
## Jimmy_Kimmel_Live -1.031e-02 8.571e-03 -1.203 0.22911
## The_Kelly_File -5.903e-03 1.012e-02 -0.583 0.55994
## The_Nightly_Show_with_Larry_Wilmore 8.035e-03 1.642e-02 0.489 0.62455
## Today 7.708e-03 6.855e-03 1.124 0.26098
## Anderson_Cooper_Three_Hundred_and_Sixty 4.274e-03 7.535e-03 0.567 0.57062
## CBS_This_Morning -3.739e-03 7.163e-03 -0.522 0.60168
## Hardball_with_Chris_Matthews -7.576e-03 1.143e-02 -0.663 0.50746
## On_the_Record_with_Greta_Van_Susteren 6.052e-04 1.065e-02 0.057 0.95470
## The_Rachel_Maddow_Show 1.091e-02 1.054e-02 1.035 0.30080
## Good_Morning_America -5.637e-03 6.501e-03 -0.867 0.38597
## Nancy_Grace 1.089e-02 1.190e-02 0.915 0.36041
## Erin_Burnett_OutFront 1.395e-03 1.316e-02 0.106 0.91555
## The_O_Reilly_Factor 9.940e-03 9.049e-03 1.099 0.27207
## House_of_Cards -6.126e-03 8.829e-03 -0.694 0.48780
## Game_of_Thrones -7.548e-03 7.509e-03 -1.005 0.31487
## Madam_Secretary 4.601e-04 8.983e-03 0.051 0.95915
## Scandal 1.867e-02 9.214e-03 2.026 0.04285
## Alpha_House -2.093e-02 2.311e-02 -0.905 0.36529
## The_Blacklist 7.062e-04 8.134e-03 0.087 0.93082
## Criminal_Minds 2.961e-03 6.849e-03 0.432 0.66551
## NCIS -3.286e-03 6.945e-03 -0.473 0.63619
## Scorpion -1.085e-02 9.884e-03 -1.098 0.27240
## Judge_Judy -6.338e-03 7.742e-03 -0.819 0.41307
## Daredevil 1.403e-02 1.354e-02 1.036 0.30014
## Blue_bloods 3.095e-03 7.857e-03 0.394 0.69370
## Hawaii_Five_O -1.805e-03 9.437e-03 -0.191 0.84836
## Empire -9.613e-03 9.941e-03 -0.967 0.33361
## Modern_Family 4.567e-03 6.840e-03 0.668 0.50439
## Sunday_Night_Football 2.050e-03 5.217e-03 0.393 0.69438
## The_Simpsons -1.109e-03 1.071e-02 -0.104 0.91757
## Dancing_with_the_Stars -1.511e-03 7.291e-03 -0.207 0.83581
## Shark_Tank -1.554e-03 6.796e-03 -0.229 0.81921
## The_Voice 8.190e-03 6.679e-03 1.226 0.22021
## Conan -3.638e-02 1.313e-02 -2.770 0.00564
## The_Big_Bang_Theory 1.090e-02 5.821e-03 1.872 0.06136
## The_Tonight_Show_Starring_Jimmy_Fallon 1.772e-03 7.033e-03 0.252 0.80105
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1188 on 2682 degrees of freedom
## (1540 observations deleted due to missingness)
## Multiple R-squared: 0.01544, Adjusted R-squared: -0.001816
## F-statistic: 0.8947 on 47 and 2682 DF, p-value: 0.6763coefplot::coefplot (All_Variables_Moded_vote_lm)
Regression for Traditional Political News Programs and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Traditional_Political_News_Programs_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Traditional_Political_News_Programs_Moded_vote_grouped_lm <- lm(vote ~ Traditional_Political_News_Programs_Moded_vote_grouped , data = anes_clean)
summary(Traditional_Political_News_Programs_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Traditional_Political_News_Programs_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98659 0.01341 0.01395 0.01503 0.01826
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.9865925 0.0032349
## Traditional_Political_News_Programs_Moded_vote_grouped -0.0005392 0.0012025
## t value Pr(>|t|)
## (Intercept) 304.986 <2e-16 ***
## Traditional_Political_News_Programs_Moded_vote_grouped -0.448 0.654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 8.265e-05, Adjusted R-squared: -0.0003283
## F-statistic: 0.2011 on 1 and 2433 DF, p-value: 0.6539coefplot::coefplot(Traditional_Political_News_Programs_Moded_vote_grouped_lm)
Regression for Entertainment or Opinion Political News Programs and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm <- lm(vote ~Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped , data = anes_clean)
summary(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99250 0.01300 0.01520 0.01631 0.01631
##
## Coefficients:
## Estimate
## (Intercept) 0.983694
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.001101
## Std. Error
## (Intercept) 0.003305
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.001287
## t value
## (Intercept) 297.631
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.855
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.392
##
## (Intercept) ***
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 0.0003006, Adjusted R-squared: -0.0001103
## F-statistic: 0.7316 on 1 and 2433 DF, p-value: 0.3924coefplot::coefplot(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm)
### Regression for Entertainment Programs that are Expressly Political and Turnout Grouped ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped <- as.data.frame (House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm <- lm (vote ~ Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped , data = anes_clean)
summary(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98666 0.01334 0.01334 0.01561 0.02243
##
## Coefficients:
## Estimate
## (Intercept) 0.986661
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped -0.002274
## Std. Error
## (Intercept) 0.002827
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped 0.003238
## t value
## (Intercept) 348.991
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped -0.702
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped 0.483
##
## (Intercept) ***
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 0.0002026, Adjusted R-squared: -0.0002083
## F-statistic: 0.4931 on 1 and 2433 DF, p-value: 0.4826coefplot::coefplot(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm)
Regresion for Entertainment Programs that are not Expressly Political but Focus on a Salient Political issue and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped <- as.data.frame(The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm <- lm(vote ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped, data = anes_clean)
summary(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98632 0.01368 0.01368 0.01500 0.01830
##
## Coefficients:
## Estimate
## (Intercept) 0.9863202
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped -0.0006598
## Std. Error
## (Intercept) 0.0030112
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped 0.0017127
## t value
## (Intercept) 327.552
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped -0.385
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped 0.7
##
## (Intercept) ***
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 6.099e-05, Adjusted R-squared: -0.00035
## F-statistic: 0.1484 on 1 and 2433 DF, p-value: 0.7001coefplot::coefplot(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm)
Regresion for Entertainment Programs with little to No Political Content and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm <- lm(vote ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped , data = anes_clean)
summary(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98622 0.01414 0.01450 0.01468 0.01468
##
## Coefficients:
## Estimate
## (Intercept) 0.9853232
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.0001796
## Std. Error
## (Intercept) 0.0035571
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.0015487
## t value
## (Intercept) 277.000
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.116
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.908
##
## (Intercept) ***
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.527e-06, Adjusted R-squared: -0.0004055
## F-statistic: 0.01345 on 1 and 2433 DF, p-value: 0.9077coefplot::coefplot(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm)
Regression for All Variables and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
All_Variables_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_vote_grouped_lm <- lm(vote ~ All_Variables_Moded_vote_grouped , data = anes_clean)
summary(All_Variables_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ All_Variables_Moded_vote_grouped, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98575 0.01430 0.01436 0.01444 0.01481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.858e-01 4.143e-03 237.937 <2e-16 ***
## All_Variables_Moded_vote_grouped -1.883e-05 4.996e-04 -0.038 0.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.84e-07, Adjusted R-squared: -0.0004104
## F-statistic: 0.001421 on 1 and 2433 DF, p-value: 0.9699coefplot::coefplot (All_Variables_Moded_vote_grouped_lm)
Regression for All Grouped Variables Grouped and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
All_Variables_Moded_vote_grouped_grouped <- as.data.frame(
Traditional_Political_News_Programs_Moded_vote_grouped +
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped +
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped +
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped +
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped
)%>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_vote_grouped_grouped_lm <- lm(vote ~ All_Variables_Moded_vote_grouped_grouped , data = anes_clean)
summary (All_Variables_Moded_vote_grouped_grouped_lm)##
## Call:
## lm(formula = vote ~ All_Variables_Moded_vote_grouped_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98575 0.01430 0.01436 0.01444 0.01481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.858e-01 4.143e-03 237.937 <2e-16
## All_Variables_Moded_vote_grouped_grouped -1.883e-05 4.996e-04 -0.038 0.97
##
## (Intercept) ***
## All_Variables_Moded_vote_grouped_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.84e-07, Adjusted R-squared: -0.0004104
## F-statistic: 0.001421 on 1 and 2433 DF, p-value: 0.9699coefplot::coefplot (All_Variables_Moded_vote_grouped_grouped_lm)
As you can see by running the code only 4 shows had a significant effect on turnout at the 5% level, Twenty-Twenty, Scandal, Conan, and the Big Bang Theory. Furthermore of the 4 shows 2 were apolitical which went against my prediction. As it stands I can only speculate on why these shows had an effect and the others did not. My initial thoughts have to do with the amount of advertising dollars that campaigns put towards these shows, I will have to follow up at some point and look at the effect on turnout per dollar per person each show has, but that will have to be for another project down the line.
This project is a work in progress and is far from complete. My Partner and I still need to go in and control for other variables such as education, political interest etc. We also have to clean up the code as it is a bit messy and the formatting on many of the plots are not nice to look at.
In my follow up post I will be going over the effect that these shows had on registration in 2016.
This post will be very similar to my last. This post will cover the code and my findings for how the TV shows from the ANES 2016 survey affected registration in 2016. From my last post you will remember that very few shows had an affect on turnout. I predict that since registration is an easier action to preform the shows that had an affect on turnout will just have a more pronounced affect on registration with perhaps a few more shows having a significant affect on registration. What I mean by registration is whether or not the respondent was registered to vote in 2016 just as a clarification. Below is the complete code. The grouping and regression methods were the same for here aswell.
Libraries
library(ggplot2)
library(tidyverse)
library(anesr)
library(coefplot)Data
data("timeseries_2016")
anes16 <- timeseries_2016
TV <- anes16 %>% select(V161364, V161365, V161366, V161367, V161368, V161369, V161370, V161371, V161372, V161373, V161374, V161375, V161376, V161377, V161378, V161379, V161380, V161381, V161382, V161383, V161384, V161385, V161386, V161387, V161388, V161389, V161390, V161391, V161392, V161393, V161394, V161395, V161396, V161397, V161398, V161399, V161400, V161401, V161402, V161403, V161404, V161405, V161406, V161407, V161408, V161409, V161410, V161411, V162034)
Did_You_register <- anes16 %>% select(V161011)
Did_You_Vote <- anes16 %>% select(V162034)
TV_Cleaned <- read.csv("C:/Users/Owner/Downloads/TV Cleaned - Sheet1.csv")Data Grouped
Traditional_Political_News_Programs <- TV %>% select(V161364, V161367, V161380, V161384, V161388, V161390, V161396, V161399, V161405)
Entertainment_or_Opinion_Political_News_Programs <- TV %>% select(V161365, V161370, V161371, V161372, V161375, V161379, V161381, V161382, V161386,V161391, V161393, V161400, V161403, V161404, V161409)
Entertainment_Programs_that_are_Expressly_Political <- TV %>% select(V161385, V161389, V161402, V161406,V161411)
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue <- TV %>% select(V161366, V161368, V161374, V161377, V161387, V161392, V161397, V161401)
Entertainment_Programs_with_little_to_No_Political_Content <- TV %>% select(V161369, V161373, V161376, V161378, V161383, V161394, V161395, V161398, V161407, V161410)
Twenty_Twenty <- TV %>% select (V161364) %>% unlist()
All_In_with_Chris_Hayes <- TV %>% select (V161365) %>% unlist()
The_Blacklist <- TV %>% select (V161366) %>% unlist()
CBS_Evening_News_with_Scott_Pelley <- TV %>% select (V161367) %>% unlist()
Criminal_Minds <- TV %>% select (V161368) %>% unlist()
Empire <- TV %>% select (V161369) %>% unlist()
Hannity <- TV %>% select (V161370) %>% unlist()
Jimmy_Kimmel_Live <- TV %>% select (V161371) %>% unlist()
The_Kelly_File <- TV %>% select (V161372) %>% unlist()
Modern_Family <- TV %>% select (V161373) %>% unlist()
NCIS <- TV %>% select (V161374) %>% unlist()
The_Nightly_Show_with_Larry_Wilmore <- TV %>% select (V161375) %>% unlist()
Sunday_Night_Football <- TV %>% select (V161376) %>% unlist()
Scorpion <- TV %>% select (V161377) %>% unlist()
The_Simpsons <- TV %>% select (V161378) %>% unlist()
Today <- TV %>% select (V161379) %>% unlist()
Sixty_Minutes <- TV %>% select (V161380) %>% unlist()
Anderson_Cooper_Three_Hundred_and_Sixty <- TV %>% select (V161381) %>% unlist()
CBS_This_Morning <- TV %>% select (V161382) %>% unlist()
Dancing_with_the_Stars <- TV %>% select (V161383) %>% unlist()
Face_the_Nation <- TV %>% select (V161384) %>% unlist()
House_of_Cards <- TV %>% select (V161385) %>% unlist()
Hardball_with_Chris_Matthews <- TV %>% select (V161386) %>% unlist()
Judge_Judy <- TV %>% select (V161387) %>% unlist()
Meet_the_Press <- TV %>% select (V161388) %>% unlist()
Game_of_Thrones <- TV %>% select (V161389) %>% unlist()
NBC_Nightly_News_with_Lester_Holt <- TV %>% select (V161390) %>% unlist()
On_the_Record_with_Greta_Van_Susteren <- TV %>% select (V161391) %>% unlist()
Daredevil <- TV %>% select (V161392) %>% unlist()
The_Rachel_Maddow_Show <- TV %>% select (V161393) %>% unlist()
Shark_Tank <- TV %>% select (V161394) %>% unlist()
The_Voice <- TV %>% select(V161395) %>% unlist()
ABC_World_News_with_David_Muir <- TV %>% select (V161396) %>% unlist()
Blue_bloods <- TV %>% select (V161397) %>% unlist()
Conan <- TV %>% select (V161398) %>% unlist()
Dateline_NBC <- TV %>% select (V161399) %>% unlist()
Good_Morning_America <- TV %>% select (V161400) %>% unlist()
Hawaii_Five_O <- TV %>% select (V161401) %>% unlist()
Madam_Secretary <- TV %>% select (V161402) %>% unlist()
Nancy_Grace <- TV %>% select (V161403) %>% unlist()
Erin_Burnett_OutFront <- TV %>% select (V161404) %>% unlist()
PBS_News_Hour <- TV %>% select (V161405) %>% unlist()
Scandal <- TV %>% select (V161406) %>% unlist()
The_Big_Bang_Theory <- TV %>% select (V161407) %>% unlist()
The_Late_Show_with_Stephen_Colbert <- TV %>% select(V161408) %>% unlist()
The_O_Reilly_Factor <- TV %>% select (V161409) %>% unlist()
The_Tonight_Show_Starring_Jimmy_Fallon <- TV %>% select(V161410) %>% unlist()
Alpha_House <- TV %>% select(V161411) %>% unlist()Regression for Traditional Political News Programs and Registration Status
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Traditional_Political_News_Programs_Moded_register <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Traditional_Political_News_Programs_Moded_register_lm <- lm(register ~ 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 , data = anes_clean)
summary(Traditional_Political_News_Programs_Moded_register_lm)##
## Call:
## lm(formula = register ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93896 0.09838 0.11395 0.11481 0.16332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.886049 0.005740 154.370 < 2e-16 ***
## Twenty_Twenty -0.026446 0.016029 -1.650 0.09906 .
## CBS_Evening_News_with_Scott_Pelley 0.001472 0.015907 0.093 0.92629
## Sixty_Minutes 0.040931 0.014374 2.848 0.00443 **
## Face_the_Nation -0.004791 0.020027 -0.239 0.81094
## Meet_the_Press 0.014095 0.018872 0.747 0.45519
## NBC_Nightly_News_with_Lester_Holt -0.015514 0.014593 -1.063 0.28779
## ABC_World_News_with_David_Muir -0.001669 0.014827 -0.113 0.91041
## Dateline_NBC -0.002117 0.014918 -0.142 0.88716
## PBS_News_Hour -0.005097 0.016524 -0.308 0.75774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3141 on 3647 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.002958, Adjusted R-squared: 0.0004978
## F-statistic: 1.202 on 9 and 3647 DF, p-value: 0.2886coefplot::coefplot(Traditional_Political_News_Programs_Moded_register_lm)
### Regression for Entertainment or Opinion Political News Programs and Registration Status ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_or_Opinion_Political_News_Programs_Moded_register <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_or_Opinion_Political_News_Programs_Moded_register_lm <- lm(register ~ 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 , data = anes_clean)
summary(Entertainment_or_Opinion_Political_News_Programs_Moded_register_lm)##
## Call:
## lm(formula = register ~ 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, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.96175 0.08864 0.11676 0.11789 0.20624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.882114 0.006541 134.869 <2e-16
## All_In_with_Chris_Hayes 0.028958 0.033518 0.864 0.3877
## Hannity 0.032190 0.022788 1.413 0.1579
## Jimmy_Kimmel_Live -0.027391 0.017865 -1.533 0.1253
## The_Kelly_File 0.004372 0.023671 0.185 0.8535
## The_Nightly_Show_with_Larry_Wilmore 0.008414 0.033326 0.252 0.8007
## Today 0.002668 0.014701 0.182 0.8560
## Anderson_Cooper_Three_Hundred_and_Sixty 0.017612 0.016934 1.040 0.2984
## CBS_This_Morning 0.007104 0.015093 0.471 0.6379
## Hardball_with_Chris_Matthews -0.013880 0.026748 -0.519 0.6038
## On_the_Record_with_Greta_Van_Susteren 0.006993 0.024061 0.291 0.7714
## The_Rachel_Maddow_Show 0.026575 0.025232 1.053 0.2923
## Good_Morning_America 0.001130 0.013801 0.082 0.9347
## Nancy_Grace -0.058901 0.024809 -2.374 0.0176
## Erin_Burnett_OutFront -0.055302 0.029593 -1.869 0.0617
## The_O_Reilly_Factor 0.018471 0.021134 0.874 0.3822
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3139 on 3641 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.006216, Adjusted R-squared: 0.002122
## F-statistic: 1.518 on 15 and 3641 DF, p-value: 0.08976coefplot::coefplot(Entertainment_or_Opinion_Political_News_Programs_Moded_register_lm)
### Regression for Entertainment Programs that are Expressly Political and Registration Staus ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_that_are_Expressly_Political_Moded_register <- as.data.frame(House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_Expressly_Political_Moded_register_lm <- lm(register ~ House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House , data = anes_clean)
summary(Entertainment_Programs_that_are_Expressly_Political_Moded_register_lm)##
## Call:
## lm(formula = register ~ House_of_Cards + Game_of_Thrones + Madam_Secretary +
## Scandal + Alpha_House, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9735 0.1156 0.1166 0.1166 0.2078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.883379 0.005889 150.015 <2e-16 ***
## House_of_Cards -0.001518 0.019672 -0.077 0.9385
## Game_of_Thrones -0.003651 0.016716 -0.218 0.8271
## Madam_Secretary 0.090127 0.018025 5.000 6e-07 ***
## Scandal -0.025282 0.019136 -1.321 0.1865
## Alpha_House -0.060735 0.028730 -2.114 0.0346 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3133 on 3651 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.006813, Adjusted R-squared: 0.005453
## F-statistic: 5.009 on 5 and 3651 DF, p-value: 0.0001412coefplot::coefplot(Entertainment_Programs_that_are_Expressly_Political_Moded_register_lm)
### Regresion for Entertainment Programs that are not Expressly Political but Focus on a salient Political issue and Registration Status ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register <- as.data.frame( The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_lm <- lm(register ~ The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O, data = anes_clean)
summary(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_lm)##
## Call:
## lm(formula = register ~ The_Blacklist + Criminal_Minds + NCIS +
## Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.94945 0.08446 0.11471 0.11475 0.22492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.885293 0.005809 152.410 < 2e-16 ***
## The_Blacklist 0.028843 0.017229 1.674 0.094204 .
## Criminal_Minds -0.054525 0.015175 -3.593 0.000331 ***
## NCIS 0.033909 0.015429 2.198 0.028029 *
## Scorpion 0.007680 0.021326 0.360 0.718757
## Judge_Judy -0.011240 0.015910 -0.706 0.479966
## Daredevil -0.044447 0.022140 -2.007 0.044772 *
## Blue_bloods 0.030245 0.017401 1.738 0.082279 .
## Hawaii_Five_O 0.009572 0.020639 0.464 0.642847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3134 on 3648 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.007559, Adjusted R-squared: 0.005383
## F-statistic: 3.473 on 8 and 3648 DF, p-value: 0.0005339coefplot::coefplot(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_lm)
Regresion for Entertainment Programs with little to No Political Content and Registration Status
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_with_little_to_No_Political_Content_moded_register <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_with_little_to_No_Political_Content_moded_register_lm <- lm(register ~ 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, data = anes_clean)
summary(Entertainment_Programs_with_little_to_No_Political_Content_moded_register_lm)##
## Call:
## lm(formula = register ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93569 0.09702 0.11303 0.11530 0.17226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8847039 0.0065567 134.931 <2e-16
## Empire -0.0009329 0.0197878 -0.047 0.9624
## Modern_Family -0.0053083 0.0154238 -0.344 0.7307
## Sunday_Night_Football 0.0109999 0.0116888 0.941 0.3467
## The_Simpsons -0.0272794 0.0221814 -1.230 0.2188
## Dancing_with_the_Stars 0.0263595 0.0157774 1.671 0.0949
## Shark_Tank -0.0008137 0.0154739 -0.053 0.9581
## The_Voice -0.0282772 0.0149315 -1.894 0.0583
## Conan 0.0038973 0.0245951 0.158 0.8741
## The_Big_Bang_Theory 0.0136240 0.0129009 1.056 0.2910
## The_Tonight_Show_Starring_Jimmy_Fallon 0.0054670 0.0150780 0.363 0.7169
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3143 on 3646 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.002263, Adjusted R-squared: -0.0004739
## F-statistic: 0.8268 on 10 and 3646 DF, p-value: 0.6027coefplot::coefplot(Entertainment_Programs_with_little_to_No_Political_Content_moded_register_lm)
Regression for All Variables and Registration Status
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
All_Variables_Moded_register <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_register_lm <- lm(register ~ 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, data = anes_clean)
summary(All_Variables_Moded_register_lm)##
## Call:
## lm(formula = register ~ 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,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.00930 0.06459 0.11238 0.12648 0.27803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.754e-01 8.243e-03 106.208 < 2e-16
## Twenty_Twenty -1.716e-02 1.657e-02 -1.036 0.30049
## CBS_Evening_News_with_Scott_Pelley 3.701e-03 1.692e-02 0.219 0.82687
## Sixty_Minutes 3.343e-02 1.467e-02 2.278 0.02276
## Face_the_Nation 3.682e-03 2.097e-02 0.176 0.86061
## Meet_the_Press 5.092e-03 1.996e-02 0.255 0.79862
## NBC_Nightly_News_with_Lester_Holt -1.361e-02 1.532e-02 -0.888 0.37451
## ABC_World_News_with_David_Muir 8.684e-03 1.595e-02 0.544 0.58613
## Dateline_NBC 1.031e-02 1.541e-02 0.669 0.50358
## PBS_News_Hour 2.221e-03 1.791e-02 0.124 0.90129
## All_In_with_Chris_Hayes 3.678e-02 3.417e-02 1.076 0.28183
## Hannity 3.455e-02 2.290e-02 1.509 0.13143
## Jimmy_Kimmel_Live -1.497e-02 1.979e-02 -0.756 0.44952
## The_Kelly_File 4.806e-03 2.373e-02 0.203 0.83951
## The_Nightly_Show_with_Larry_Wilmore 3.946e-02 3.854e-02 1.024 0.30601
## Today 3.063e-03 1.580e-02 0.194 0.84624
## Anderson_Cooper_Three_Hundred_and_Sixty 9.546e-03 1.744e-02 0.547 0.58421
## CBS_This_Morning -4.693e-03 1.626e-02 -0.289 0.77283
## Hardball_with_Chris_Matthews -2.033e-02 2.732e-02 -0.744 0.45683
## On_the_Record_with_Greta_Van_Susteren 1.034e-02 2.437e-02 0.424 0.67138
## The_Rachel_Maddow_Show 2.623e-02 2.554e-02 1.027 0.30442
## Good_Morning_America 2.280e-03 1.473e-02 0.155 0.87698
## Nancy_Grace -3.706e-02 2.694e-02 -1.376 0.16892
## Erin_Burnett_OutFront -4.853e-02 3.151e-02 -1.540 0.12354
## The_O_Reilly_Factor 9.792e-03 2.129e-02 0.460 0.64560
## House_of_Cards -3.973e-03 2.047e-02 -0.194 0.84614
## Game_of_Thrones 5.555e-03 1.753e-02 0.317 0.75134
## Madam_Secretary 5.581e-02 2.071e-02 2.695 0.00707
## Scandal -4.094e-03 2.118e-02 -0.193 0.84672
## Alpha_House -6.627e-02 5.383e-02 -1.231 0.21838
## The_Blacklist 2.730e-02 1.851e-02 1.475 0.14039
## Criminal_Minds -3.869e-02 1.563e-02 -2.476 0.01335
## NCIS 2.617e-02 1.566e-02 1.671 0.09474
## Scorpion 5.940e-03 2.269e-02 0.262 0.79350
## Judge_Judy -2.770e-06 1.763e-02 0.000 0.99987
## Daredevil 4.541e-03 2.981e-02 0.152 0.87893
## Blue_bloods 2.131e-02 1.803e-02 1.182 0.23730
## Hawaii_Five_O 1.194e-02 2.118e-02 0.564 0.57307
## Empire -1.353e-02 2.238e-02 -0.604 0.54566
## Modern_Family -5.098e-03 1.570e-02 -0.325 0.74549
## Sunday_Night_Football -1.901e-03 1.194e-02 -0.159 0.87358
## The_Simpsons -4.008e-02 2.376e-02 -1.687 0.09175
## Dancing_with_the_Stars 8.083e-03 1.659e-02 0.487 0.62606
## Shark_Tank -9.177e-03 1.595e-02 -0.575 0.56510
## The_Voice -3.988e-02 1.533e-02 -2.601 0.00934
## Conan -3.357e-02 2.882e-02 -1.165 0.24419
## The_Big_Bang_Theory 6.567e-04 1.342e-02 0.049 0.96098
## The_Tonight_Show_Starring_Jimmy_Fallon -1.235e-03 1.613e-02 -0.077 0.93900
##
## (Intercept) ***
## 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
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3127 on 3609 degrees of freedom
## (613 observations deleted due to missingness)
## Multiple R-squared: 0.02254, Adjusted R-squared: 0.009808
## F-statistic: 1.77 on 47 and 3609 DF, p-value: 0.000986coefplot::coefplot (All_Variables_Moded_register_lm)
Regression for Traditional Political News Programs and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Traditional_Political_News_Programs_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Traditional_Political_News_Programs_Moded_vote_grouped_lm <- lm(vote ~ Traditional_Political_News_Programs_Moded_vote_grouped , data = anes_clean)
summary(Traditional_Political_News_Programs_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Traditional_Political_News_Programs_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98659 0.01341 0.01395 0.01503 0.01826
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.9865925 0.0032349
## Traditional_Political_News_Programs_Moded_vote_grouped -0.0005392 0.0012025
## t value Pr(>|t|)
## (Intercept) 304.986 <2e-16 ***
## Traditional_Political_News_Programs_Moded_vote_grouped -0.448 0.654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 8.265e-05, Adjusted R-squared: -0.0003283
## F-statistic: 0.2011 on 1 and 2433 DF, p-value: 0.6539coefplot::coefplot(Traditional_Political_News_Programs_Moded_vote_grouped_lm)
Regression for Entertainment or Opinion Political News Programs and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm <- lm(vote ~Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped , data = anes_clean)
summary(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.99250 0.01300 0.01520 0.01631 0.01631
##
## Coefficients:
## Estimate
## (Intercept) 0.983694
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.001101
## Std. Error
## (Intercept) 0.003305
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.001287
## t value
## (Intercept) 297.631
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.855
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped 0.392
##
## (Intercept) ***
## Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 0.0003006, Adjusted R-squared: -0.0001103
## F-statistic: 0.7316 on 1 and 2433 DF, p-value: 0.3924coefplot::coefplot(Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped_lm)
### Regression for Entertainment Programs that are Expressly Political and Turnout Grouped ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped <- as.data.frame (House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm <- lm (vote ~ Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped , data = anes_clean)
summary(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98666 0.01334 0.01334 0.01561 0.02243
##
## Coefficients:
## Estimate
## (Intercept) 0.986661
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped -0.002274
## Std. Error
## (Intercept) 0.002827
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped 0.003238
## t value
## (Intercept) 348.991
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped -0.702
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped 0.483
##
## (Intercept) ***
## Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 0.0002026, Adjusted R-squared: -0.0002083
## F-statistic: 0.4931 on 1 and 2433 DF, p-value: 0.4826coefplot::coefplot(Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped_lm)
Regresion for Entertainment Programs that are not Expressly Political but Focus on a Salient Political issue and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped <- as.data.frame(The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm <- lm(vote ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped, data = anes_clean)
summary(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98632 0.01368 0.01368 0.01500 0.01830
##
## Coefficients:
## Estimate
## (Intercept) 0.9863202
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped -0.0006598
## Std. Error
## (Intercept) 0.0030112
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped 0.0017127
## t value
## (Intercept) 327.552
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped -0.385
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped 0.7
##
## (Intercept) ***
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 6.099e-05, Adjusted R-squared: -0.00035
## F-statistic: 0.1484 on 1 and 2433 DF, p-value: 0.7001coefplot::coefplot(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped_lm)
Regresion for Entertainment Programs with little to No Political Content and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm <- lm(vote ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped , data = anes_clean)
summary(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98622 0.01414 0.01450 0.01468 0.01468
##
## Coefficients:
## Estimate
## (Intercept) 0.9853232
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.0001796
## Std. Error
## (Intercept) 0.0035571
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.0015487
## t value
## (Intercept) 277.000
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.116
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped 0.908
##
## (Intercept) ***
## Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.527e-06, Adjusted R-squared: -0.0004055
## F-statistic: 0.01345 on 1 and 2433 DF, p-value: 0.9077coefplot::coefplot(Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped_lm)
Regression for All Variables and Turnout Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
All_Variables_Moded_vote_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_vote_grouped_lm <- lm(vote ~ All_Variables_Moded_vote_grouped , data = anes_clean)
summary(All_Variables_Moded_vote_grouped_lm)##
## Call:
## lm(formula = vote ~ All_Variables_Moded_vote_grouped, data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98575 0.01430 0.01436 0.01444 0.01481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.858e-01 4.143e-03 237.937 <2e-16 ***
## All_Variables_Moded_vote_grouped -1.883e-05 4.996e-04 -0.038 0.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.84e-07, Adjusted R-squared: -0.0004104
## F-statistic: 0.001421 on 1 and 2433 DF, p-value: 0.9699coefplot::coefplot (All_Variables_Moded_vote_grouped_lm)
Regression for All Grouped Variables Grouped and Turnout
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(vote = case_when(
V162034 == 2 ~ 0,
V162034 == 1 ~ 1))
All_Variables_Moded_vote_grouped_grouped <- as.data.frame(
Traditional_Political_News_Programs_Moded_vote_grouped +
Entertainment_or_Opinion_Political_News_Programs_Moded_vote_grouped +
Entertainment_Programs_that_are_Expressly_Political_Moded_vote_grouped +
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_vote_grouped +
Entertainment_Programs_with_little_to_No_Political_Content_moded_vote_grouped
)%>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_vote_grouped_grouped_lm <- lm(vote ~ All_Variables_Moded_vote_grouped_grouped , data = anes_clean)
summary (All_Variables_Moded_vote_grouped_grouped_lm)##
## Call:
## lm(formula = vote ~ All_Variables_Moded_vote_grouped_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.98575 0.01430 0.01436 0.01444 0.01481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.858e-01 4.143e-03 237.937 <2e-16
## All_Variables_Moded_vote_grouped_grouped -1.883e-05 4.996e-04 -0.038 0.97
##
## (Intercept) ***
## All_Variables_Moded_vote_grouped_grouped
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1191 on 2433 degrees of freedom
## (1835 observations deleted due to missingness)
## Multiple R-squared: 5.84e-07, Adjusted R-squared: -0.0004104
## F-statistic: 0.001421 on 1 and 2433 DF, p-value: 0.9699coefplot::coefplot (All_Variables_Moded_vote_grouped_grouped_lm)
Regression for Traditional Political News Programs and Registration Status Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Traditional_Political_News_Programs_Moded_register_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Traditional_Political_News_Programs_Moded_register_grouped_lm <- lm(register ~ Traditional_Political_News_Programs_Moded_register_grouped, data = anes_clean)
summary(Traditional_Political_News_Programs_Moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ Traditional_Political_News_Programs_Moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.92766 0.09575 0.10746 0.11917 0.11917
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.880834 0.007305
## Traditional_Political_News_Programs_Moded_register_grouped 0.005853 0.002735
## t value Pr(>|t|)
## (Intercept) 120.58 <2e-16 ***
## Traditional_Political_News_Programs_Moded_register_grouped 2.14 0.0324 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3113 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.001415, Adjusted R-squared: 0.001106
## F-statistic: 4.58 on 1 and 3233 DF, p-value: 0.03242coefplot::coefplot(Traditional_Political_News_Programs_Moded_register_grouped_lm)
### Regression for Entertainment or Opinion Political News Programs and Registration Status Grouped ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped_lm <- lm (register ~ Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped , data = anes_clean)
summary(Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95622 0.09268 0.10665 0.12062 0.12062
##
## Coefficients:
## Estimate
## (Intercept) 0.879375
## Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped 0.006986
## Std. Error
## (Intercept) 0.007415
## Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped 0.002959
## t value
## (Intercept) 118.595
## Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped 2.361
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped 0.0183
##
## (Intercept) ***
## Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3112 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.001721, Adjusted R-squared: 0.001413
## F-statistic: 5.575 on 1 and 3233 DF, p-value: 0.01828coefplot::coefplot(Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped_lm)
### Regression for Entertainment Programs that are Expressly Political and Registration Staus Grouped ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped <- as.data.frame(House_of_Cards + Game_of_Thrones + Madam_Secretary + Scandal + Alpha_House) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped_lm <- lm(register ~ Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped , data = anes_clean)
summary(Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9232 0.1018 0.1143 0.1143 0.1143
##
## Coefficients:
## Estimate
## (Intercept) 0.885731
## Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped 0.012490
## Std. Error
## (Intercept) 0.006368
## Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped 0.007443
## t value
## (Intercept) 139.090
## Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped 1.678
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped 0.0934
##
## (Intercept) ***
## Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3114 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.0008703, Adjusted R-squared: 0.0005612
## F-statistic: 2.816 on 1 and 3233 DF, p-value: 0.09342coefplot::coefplot(Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped_lm)
### Regresion for Entertainment Programs that are not Expressly Political but Focus on a salient Political issue and Registration Status Grouped ###
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped <- as.data.frame( The_Blacklist + Criminal_Minds + NCIS + Scorpion + Judge_Judy + Daredevil + Blue_bloods + Hawaii_Five_O) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped_lm <- lm(register ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped , data = anes_clean)
summary(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95431 0.08808 0.10928 0.11988 0.11988
##
## Coefficients:
## Estimate
## (Intercept) 0.880124
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped 0.010598
## Std. Error
## (Intercept) 0.006836
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped 0.003926
## t value
## (Intercept) 128.745
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped 2.699
## Pr(>|t|)
## (Intercept) < 2e-16
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped 0.00699
##
## (Intercept) ***
## Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3111 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.002249, Adjusted R-squared: 0.00194
## F-statistic: 7.286 on 1 and 3233 DF, p-value: 0.006986coefplot::coefplot(Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped_lm)
Regresion for Entertainment Programs with little to No Political Content and Registration Status Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped_lm <- lm(register ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped , anes_clean)
summary(Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90413 0.09587 0.10364 0.11142 0.16582
##
## Coefficients:
## Estimate
## (Intercept) 0.904128
## Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped -0.007772
## Std. Error
## (Intercept) 0.007939
## Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped 0.003456
## t value
## (Intercept) 113.878
## Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped -2.249
## Pr(>|t|)
## (Intercept) <2e-16
## Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped 0.0246
##
## (Intercept) ***
## Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3113 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.001562, Adjusted R-squared: 0.001254
## F-statistic: 5.059 on 1 and 3233 DF, p-value: 0.02456coefplot::coefplot(Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped_lm)
Regression for All Variables and Registration Status Grouped
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
All_Variables_Moded_register_grouped <- 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) %>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_register_grouped_lm <- lm(register ~ All_Variables_Moded_register_grouped , data = anes_clean)
summary(All_Variables_Moded_register_grouped_lm)##
## Call:
## lm(formula = register ~ All_Variables_Moded_register_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93789 0.09823 0.10786 0.11749 0.12472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.875281 0.009328 93.833 <2e-16 ***
## All_Variables_Moded_register_grouped 0.002408 0.001143 2.106 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3113 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.00137, Adjusted R-squared: 0.001061
## F-statistic: 4.436 on 1 and 3233 DF, p-value: 0.03527coefplot::coefplot (All_Variables_Moded_register_grouped_lm)
Regression for All Variables Grouped Grouped Registration Status
clean <- function(x){ifelse (x < 0, NA, x)}
anes_clean <- anes16 %>%
mutate(across (everything(), clean)) %>%
mutate(register = case_when(
V161011 == 2 ~ 0,
V161011 == 1 ~ 1))
All_Variables_Moded_register_grouped_grouped <- as.data.frame (Traditional_Political_News_Programs_Moded_register_grouped +
Entertainment_or_Opinion_Political_News_Programs_Moded_register_grouped +
Entertainment_Programs_that_are_Expressly_Political_Moded_register_grouped +
Entertainment_Programs_that_are_not_Expressly_Political_but_Focus_on_a_salient_Political_issue_Moded_register_grouped +
Entertainment_Programs_with_little_to_No_Political_Content_moded_register_grouped)%>%
rename(count = 1) %>%
mutate(count = ifelse(count <0, NA, count)) %>% unlist()
All_Variables_Moded_register_grouped_grouped_lm <- lm(register ~ All_Variables_Moded_register_grouped_grouped , data = anes_clean)
summary(All_Variables_Moded_register_grouped_grouped_lm)##
## Call:
## lm(formula = register ~ All_Variables_Moded_register_grouped_grouped,
## data = anes_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93789 0.09823 0.10786 0.11749 0.12472
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.875281 0.009328 93.833
## All_Variables_Moded_register_grouped_grouped 0.002408 0.001143 2.106
## Pr(>|t|)
## (Intercept) <2e-16 ***
## All_Variables_Moded_register_grouped_grouped 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3113 on 3233 degrees of freedom
## (1035 observations deleted due to missingness)
## Multiple R-squared: 0.00137, Adjusted R-squared: 0.001061
## F-statistic: 4.436 on 1 and 3233 DF, p-value: 0.03527coefplot::coefplot (All_Variables_Moded_register_grouped_grouped_lm)
Just like with my last post my prediction was dead wrong. In fact these shows had a very different affect on registration that they did on turnout. Way more shows individually had a significant (at the 5% level) affect on registration than turnout, and furthermore each group of shows had a significant effect on registration. This is very different than with turnout where none of the groups together or alone had an affect on turnout. Even without controlling for other variables it seems that these shows have an impact on whether watchers are registered to vote. It is not clear why that is the case or why they have such a stronger affect on registration than on turnout. This could have interesting implications for campaigns it could perhaps mean that campaigns should use TV ad buys to focus on registering watchers rather than turnout if advertising is a factor in the affect these shows had on registration it could be that is the shows themselves and political advertising has no impact on the effect these shows have on registration. I think that my future project that will look at the affect on turnout per dollar per person could also apply here excpet it would be affect on registration per dollar per person. I would also like to do a project where I look at the effect that streamed shows without have on turnout and registration versus the effect that shows without ads have on turnout and registration to see whether advertising is a factor at all in the affect these shows (or others) have on turnout and registration. Again this is a work in progress we still need to control for other factors, clean up the code and graphs, and this was done with an unsophisticated linear regression model by sudents without strong backgrounds in statistics.