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 ----------------------