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cat(rep('=', 80),
'\n\n',
'OUTPUT FROM: shorts/05_clean_shorts_data.R',
'\n\n',
sep = ''
)
## Extremizing Sequences and Minimum Wage Opinions
## Data collected May 2024 via MTurk/CloudResearch
## Analysis for the Extremizing Sequences Experiment
## Preamble ----------------------------
library(tidyverse)
library(janitor)
library(lubridate)
library(stargazer)
library(broom)
# create a folder for the shorts intermediate data
dir.create("../results/intermediate data/shorts/", recursive = TRUE, showWarnings = FALSE)
# SURVEY DATA (FROM QUALTRICS)
a <- read_csv("../data/shorts/ytrecs_surveys_may2024.csv")[-c(1,2),] %>%
clean_names() # 1315 obs.
# DATE FILTER
a <- a %>% filter(start_date >= '2024-05-28') # 1032 obs.
# ATTENTION CHECK -- 932 obs.
a <- a %>% filter(a$q81 == "Quick and easy")
a <- a %>% filter(a$q82 == "wikiHow")
a <- a %>% filter(is.na(video_link) == FALSE) ## failed respondents don't have a valid link
# SURVEY TIME (ALL)
a <- a %>% mutate(start_date = as_datetime(start_date),
end_date = as_datetime(end_date),
survey_time = as.numeric(end_date-start_date))
summary(a$survey_time) # 5.5 mins to 74 mins (median 34 mins)
# DEMOGRAPHICS -------------------------------------------------
# GENDER, EDUCATION, INCOME
a <- a %>%
mutate(female = ifelse(gender == "Woman", 1, 0),
male = ifelse(gender == "Man", 1, 0),
black = ifelse(str_detect(race_ethnicity, "Black"), 1, 0),
white = ifelse(str_detect(race_ethnicity, "White"), 1, 0),
college = ifelse(str_detect(highest_education, "college ") | str_detect(highest_education, "Post"), 1, 0),
income_gt50k = ifelse(income %in% names(table(a$income))[c(2,3,5,10,11,12,13)], 1, 0)
)
a$income_gt50k[is.na(a$income)] <- NA
# PID
a <- a %>%
mutate(pid = case_when(pid1=="Democrat" ~ -1,
pid1=="Republican" ~ 1,
pid4=="Closer to the Republican Party" ~ 1,
pid4=="Closer to the Democratic Party" ~ -1,
pid4=="Neither" ~ 0))
tabyl(a,pid)
# IDEO
a <- a %>%
mutate(ideo = case_when(ideo1=="Liberal" ~ -1,
ideo1=="Conservative" ~ 1,
ideo4=="Closer to conservatives" ~ 1,
ideo4=="Closer to liberals" ~ -1,
ideo4=="Neither" ~ 0))
tabyl(a,ideo)
# AGE
a$age <- 2024-as.numeric(a$year_born)
# AGE CATEGORIES: 18-29; 30-44; 45-64; 65+
a <- a %>%
mutate(age_cat = case_when(age>=18 & age<=29 ~ "18-29",
age>=30 & age<=44 ~ "30-44",
age>=45 & age<=64 ~ "45-64",
age>=65 ~ "65+"
))
a <- a %>%
fastDummies::dummy_cols(select_columns = "age_cat",remove_selected_columns = F)
# POLITICAL INTEREST AND YOUTUBE FREQUENCY RECODING
a <- a %>%
mutate(pol_interest = dplyr::recode(political_interest,"Extremely interested"=5,"Very interested"=4,"Somewhat interested"=3,"Not very interested"=2,"Not at all interested"=1),
freq_youtube = dplyr::recode(youtube_time,"More than 3 hours per day"=6,"2–3 hours per day"=5,"1–2 hours per day"=4,"31–59 minutes per day"=3,"10–30 minutes per day"=2,"Less than 10 minutes per day"=1,"None"=0)
)
# SUMMARY TABLE FOR DEMOGRAPHICS
summary_tab <- a %>%
dplyr::summarize(female = mean(female,na.rm=T),
white = mean(white,na.rm=T),
black = mean(black,na.rm=T),
age1829 = mean(`age_cat_18-29`,na.rm=T),
age3044 = mean(`age_cat_30-44`,na.rm=T),
age4564 = mean(`age_cat_45-64`,na.rm=T),
age65p = mean(`age_cat_65+`,na.rm=T),
college = mean(college,na.rm=T),
income_gt50k = mean(income_gt50k,na.rm=T),
democrat = mean(pid==-1,na.rm=T),
republican = mean(pid==1,na.rm=T))
summary_tab <- pivot_longer(summary_tab,
cols=c(female,
white,
black,
age1829,
age3044,
age4564,
age65p,
college,
income_gt50k,
democrat,
republican),
names_to = "outcome",values_to = "survey_avg")
outcome_labels <- data.frame(outcome_pretty = c("Female",
"White",
"Black",
"Age 18-29",
"Age 30-44",
"Age 45-64",
"Age 65+",
"College educated",
"Income >$50k",
"Democrat",
"Republican"),
outcome = c("female",
"white",
"black",
"age1829",
"age3044",
"age4564",
"age65p",
"college",
"income_gt50k",
"democrat",
"republican"))
summary_tab$outcome_pretty <- outcome_labels$outcome_pretty[match(summary_tab$outcome,outcome_labels$outcome)]
summary_tab <- summary_tab %>%
mutate(outcome_pretty = factor(outcome_pretty,levels = c("Republican",
"Democrat",
"Income >$50k",
"College educated",
"Age 65+",
"Age 45-64",
"Age 30-44",
"Age 18-29",
"Female",
"Black",
"White"),ordered=T))
# DEMOGRAPHICS DESCRIPTIVE FIGURE
(descrip_fig <- ggplot(summary_tab) +
geom_point(aes(y=outcome_pretty,x=survey_avg)) +
geom_text(aes(y=outcome_pretty,x=survey_avg,label=paste0(round(100*survey_avg,0),"%")),nudge_x = 0.1) +
scale_y_discrete("") +
scale_x_continuous("",labels=scales::percent_format(),limits=c(0,1)) +
theme_bw()
)
ggsave(descrip_fig,filename = "../results/shorts_demographics.pdf",height=5,width=4)
### DEMOGRAPHICS DONE ###
#### OUTCOMES ####
##### POLICY OPINIONS #####
# convert to numeric unit scale:
a <- a %>%
mutate( # higher = more conservative or anti-min wage
minwage15_pre = dplyr::recode(minwage15_pre,"Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
rtwa_v1_pre = dplyr::recode(rtwa_v1_pre, "Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
rtwa_v2_pre = dplyr::recode(rtwa_v2_pre, "Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
mw_support_pre = dplyr::recode(mw_support_pre, "Strongly oppose raising the minimum wage"=4,"Somewhat oppose raising the minimum wage"=3,"Neither support nor oppose raising the minimum wage"=2,"Somewhat support raising the minimum wage"=1,"Strongly support raising the minimum wage"=0)/4,
minwage_howhigh_pre = dplyr::recode(minwage_howhigh_pre, "Much lower than the current level"=4,"Somewhat lower than the current level"=3,"About the current level"=2,"Somewhat higher than the current level"=1,"Much higher than the current level"=0)/4,
mw_help_pre_1 = dplyr::recode(mw_help_pre_1, "10"=9,"9"=8,"8"=7,"7"=6,"6"=5,"5"=4,"4"=3,"3"=2,"2"=1,"1"=0)/9,
mw_restrict_pre_1 = dplyr::recode(mw_restrict_pre_1, "1"=9,"2"=8,"3"=7,"4"=6,"5"=5,"6"=4,"7"=3,"8"=2,"9"=1,"10"=0)/9,
minwage_text_r_pre = (25-as.numeric(minwage_text_pre))/25,
)
a$minwage_text_r_pre[as.numeric(a$minwage_text_pre)>25] <- NA
a <- a %>%
rowwise() %>%
mutate(mw_index_pre = mean(c(minwage15_pre, rtwa_v1_pre,
rtwa_v2_pre, mw_support_pre,
minwage_howhigh_pre, mw_help_pre_1,
mw_restrict_pre_1, minwage_text_r_pre), na.rm=T)) %>%
ungroup()
# CRONBACH'S ALPHA
index_fa <- psych::alpha(select(a, minwage15_pre, rtwa_v1_pre,
rtwa_v2_pre, mw_support_pre, minwage_howhigh_pre,
mw_help_pre_1, mw_restrict_pre_1, minwage_text_r_pre), check.keys = TRUE)
write.csv(data.frame(cor(select(a, minwage15_pre, rtwa_v1_pre, rtwa_v2_pre,
mw_support_pre, minwage_howhigh_pre, mw_help_pre_1,
mw_restrict_pre_1, minwage_text_r_pre), use = "complete.obs")),
row.names = T,file = "../results/cormat_mwindex_w1.csv")
# CORRELATION PLOT PRE-MINIMUM WAGE OPINION
pdf("corrplot_mwindex_w1.pdf")
w1_corrplot <- corrplot::corrplot(cor(select(a, minwage15_pre, rtwa_v1_pre, rtwa_v2_pre,
mw_support_pre, minwage_howhigh_pre, mw_help_pre_1,
mw_restrict_pre_1, minwage_text_r_pre),
use = "complete.obs"),method = "shade")
dev.off()
(alpha <- index_fa$total["raw_alpha"]) # 0.9407615
writeLines(as.character(round(alpha,2)),con = "../results/outcomes_alpha_w1_mturk.tex",sep = "%")
tabyl(a,mw_index_pre)
##### MEDIA TRUST #####
a <- a %>%
mutate( # higher = more trusting
trust_majornews = dplyr::recode(info_trust_1,"A lot"=3,"Some"=2,"Not too much"=1,"Not at all"=0)/3,
trust_localnews = dplyr::recode(info_trust_2,"A lot"=3,"Some"=2,"Not too much"=1,"Not at all"=0)/3,
trust_social = dplyr::recode(info_trust_3,"A lot"=3,"Some"=2,"Not too much"=1,"Not at all"=0)/3,
trust_youtube = dplyr::recode(info_trust_4,"A lot"=3,"Some"=2,"Not too much"=1,"Not at all"=0)/3,
fabricate_majornews = dplyr::recode(mainstream_fakenews,"Never"=4,"Once in a while"=3,"About half the time"=2,"Most of the time"=1,"All the time"=0)/4,
fabricate_youtube = dplyr::recode(youtube_fakenews,"Never"=4,"Once in a while"=3,"About half the time"=2,"Most of the time"=1,"All the time"=0)/4
) %>%
rowwise() %>%
mutate(media_trust = mean(trust_majornews,trust_localnews,fabricate_majornews,na.rm=T)) %>%
ungroup()
media_trust_fa <- psych::alpha(select(a, trust_majornews,trust_localnews,fabricate_majornews),
check.keys = TRUE)
(alpha <- media_trust_fa$total["raw_alpha"]) #. 0.7698292
##### AFFECTIVE POLARIZATION #####
a %>%
group_by(pid) %>%
summarize(mean_2=mean(as.numeric(political_lead_feels_2),na.rm=T), # Trump
mean_5=mean(as.numeric(political_lead_feels_5),na.rm=T), # Biden
mean_11=mean(as.numeric(political_lead_feels_11),na.rm=T), # dems
mean_12=mean(as.numeric(political_lead_feels_12),na.rm=T)) # reps
a <- a %>%
mutate( # higher = more trusting
smart_dems = dplyr::recode(democrat_smart, "Extremely"=4,"Very"=3,"Somewhat"=2,"A little"=1,"Not at all"=0)/4,
smart_reps = dplyr::recode(republican_smart, "Extremely"=4,"Very"=3,"Somewhat"=2,"A little"=1,"Not at all"=0)/4,
comfort_dems = dplyr::recode(democrat_friends,"Extremely comfortable"=3,"Somewhat comfortable"=2,"Not too comfortable"=1,"Not at all comfortable"=0)/3,
comfort_reps = dplyr::recode(republican_friends,"Extremely comfortable"=3,"Somewhat comfortable"=2,"Not too comfortable"=1,"Not at all comfortable"=0)/3,
affpol_smart = case_when(
pid==-1 ~ smart_dems-smart_reps,
pid==1 ~ smart_reps-smart_dems
),
affpol_comfort = case_when(
pid==-1 ~ comfort_dems-comfort_reps,
pid==1 ~ comfort_reps-comfort_dems
)
)
# Create a new variable 'thirds' based on attributes
a$thirds <- ifelse(!is.na(a$liberals_do) & is.na(a$moderates_do) & is.na(a$conservatives_do), 1,
ifelse(is.na(a$liberals_do) & !is.na(a$moderates_do) & is.na(a$conservatives_do), 2,
ifelse(is.na(a$liberals_do) & is.na(a$moderates_do) & !is.na(a$conservatives_do), 3, NA)))
tabyl(a$thirds)
#### OUTCOMES ####
##### POLICY OPINIONS ######
# convert to numeric unit scale:
a <- a %>%
mutate( # higher = more pro-gun
minwage15 = dplyr::recode(minwage15,"Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
rtwa_v1 = dplyr::recode(rtwa_v1_updated, "Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
rtwa_v2 = dplyr::recode(rtwa_v2_updated, "Strongly oppose"=4,"Somewhat oppose"=3,"Neither support nor oppose"=2,"Somewhat support"=1,"Strongly support"=0)/4,
mw_support = dplyr::recode(mw_support, "Strongly oppose raising the minimum wage"=4,"Somewhat oppose raising the minimum wage"=3,"Neither support nor oppose raising the minimum wage"=2,"Somewhat support raising the minimum wage"=1,"Strongly support raising the minimum wage"=0)/4,
minwage_howhigh = dplyr::recode(minwage_howhigh, "Much lower than the current level"=4,"Somewhat lower than the current level"=3,"About the current level"=2,"Somewhat higher than the current level"=1,"Much higher than the current level"=0)/4,
mw_help_1 = dplyr::recode(mw_help_1, "10"=9,"9"=8,"8"=7,"7"=6,"6"=5,"5"=4,"4"=3,"3"=2,"2"=1,"1"=0)/9,
mw_restrict_1 = dplyr::recode(mw_restrict_1, "1"=9,"2"=8,"3"=7,"4"=6,"5"=5,"6"=4,"7"=3,"8"=2,"9"=1,"10"=0)/9,
minwage_text_r = (25-as.numeric(minwage_text))/25,
)
a$minwage_text_r[as.numeric(a$minwage_text)>25] <- NA
a <- a %>%
rowwise() %>%
mutate(mw_index = mean(c(minwage15, rtwa_v1, rtwa_v2, mw_support, minwage_howhigh,
mw_help_1, mw_restrict_1, minwage_text_r), na.rm=T)) %>%
ungroup()
# CRONBACH-S ALPHA
index_fa <- psych::alpha(select(a, minwage15, rtwa_v1, rtwa_v2, mw_support, minwage_howhigh,
mw_help_1, mw_restrict_1, minwage_text_r), check.keys = T)
write.csv(data.frame(cor(select(a, minwage15, rtwa_v1, rtwa_v2, mw_support, minwage_howhigh,
mw_help_1, mw_restrict_1, minwage_text_r), use = "complete.obs")),
row.names = T,file = "../results/cormat_mw_index_w2.csv")
pdf("corrplot_mwindex_w2.pdf")
a_corrplot <- corrplot::corrplot(cor(select(a, minwage15, rtwa_v1, rtwa_v2, mw_support,
minwage_howhigh, mw_help_1, mw_restrict_1, minwage_text_r),
use = "complete.obs"),method = "shade")
dev.off()
(alpha <- index_fa$total["raw_alpha"]) # 0.9582061
### SURVEY PREPROCESSING DONE ###
## YTRECS SESSION DATA -------------------------------------------------------
ytrecs <- read_rds("../data/shorts/ytrecs_sessions_may2024.rds") %>%
clean_names() %>%
as_tibble()
## EXTRACTING TOPICID AND URLID
a <- a %>%
ungroup() %>%
mutate(
topic_id = str_extract(video_link, "topicid=([a-z]{2}[1-6])") %>% str_replace("topicid=", ""),
urlid = str_extract(video_link, "id=(mt_\\d+)") %>% str_replace("id=", "")
)
## USING THE FIRST SESSION AS THE VALID ONE IF A PERSON HAS MULTIPLE ATTEMPTS
ytrecs <- ytrecs %>%
group_by(topic_id, urlid) %>%
mutate(dupes = n(),
first_session = ifelse(row_number() == 1, 1, 0)
) %>%
filter(first_session == 1) # using the first session as valid one
a <- left_join(a, ytrecs,by=c("topic_id","urlid"))
## EXTRACTING TREATMENT ARM
extract_treatmentarm <- function(url) {
pattern <- "topicid=([a-z]{2})" #[a-z]{2}[1-6]
match <- str_match(url, pattern)
if (!is.na(match[2])) {
return(match[2])
} else {
return(NA)
}
}
# APPLY THE FUNCTION TO THE VIDEO_LINK COLUMN
a <- a %>%
rowwise() %>%
mutate(treatment_arm = extract_treatmentarm(video_link)) %>%
ungroup()
write_csv(a, "../results/intermediate data/shorts/qualtrics_w12_clean_ytrecs_may2024.csv")
rm(list = ls())
### PREPROCESSING DONE ----------------------
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