diff --git "a/11/replication_package/Replication/ReplicationScript/ChangingHearts_GublerEtAl_Jul2021.R" "b/11/replication_package/Replication/ReplicationScript/ChangingHearts_GublerEtAl_Jul2021.R" new file mode 100644--- /dev/null +++ "b/11/replication_package/Replication/ReplicationScript/ChangingHearts_GublerEtAl_Jul2021.R" @@ -0,0 +1,2476 @@ + +## The final version of this script was edited in 7 July 2021 + +## To run it, you need to change the setwd() command on line 305 of +## the script to reference the replication folder on your local machine + +################################################################################ +##### Preparation +################################################################################ + +######################################## +#### Packages +######################################## + +## Load packages (install them first if they are not yet installed) +library(ggplot2) +library(scales) +library(reshape2) +library(data.table) +library(car) +library(psych) +library(apsrtable) +library(foreign) +library(haven) +library(cowplot) +library(lmtest) +library(ggforce) +library(RItools) +library(interflex) +library(stargazer) +library(GGally) + +######################################## +#### Settings +######################################## + +## Set R options +options(digits = 2, width = 80, dev = "pdf", scipen = 8) + +## Set ggplot options +theme_new <- theme_set(theme_bw(base_family = "serif", base_size = 10)) +theme_new <- theme_update( + axis.title.x = element_text(vjust = -0.5), + axis.title.y = element_text(vjust = 1.25, angle = 90) +) + +######################################## +#### Session Info +######################################## + +## This is a printout from the sessionInfo() command, which notes the system, +## version of R, and version of the packages used to generate our results: + +## sessionInfo() + +## R version 4.1.0 (2021-05-18) +## Platform: x86_64-apple-darwin17.0 (64-bit) +## Running under: macOS Big Sur 10.16 + +## Matrix products: default +## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib +## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib + +## locale: +## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 + +## attached base packages: +## [1] stats graphics grDevices utils datasets methods base + +## other attached packages: +## [1] GGally_2.1.2 stargazer_5.2.2 interflex_1.2.6 RItools_0.1-17 +## [5] SparseM_1.81 ggforce_0.3.3 lmtest_0.9-38 zoo_1.8-9 +## [9] cowplot_1.1.1 haven_2.4.1 foreign_0.8-81 apsrtable_0.8-8 +## [13] psych_2.1.6 car_3.0-11 carData_3.0-4 data.table_1.14.0 +## [17] reshape2_1.4.4 scales_1.1.1 ggplot2_3.3.5 + +## loaded via a namespace (and not attached): +## [1] nlme_3.1-152 svd_0.5 doParallel_1.0.16 +## [4] RColorBrewer_1.1-2 tools_4.1.0 utf8_1.2.1 +## [7] R6_2.5.0 DBI_1.1.1 mgcv_1.8-36 +## [10] colorspace_2.0-2 withr_2.4.2 tidyselect_1.1.1 +## [13] gridExtra_2.3 mnormt_2.0.2 curl_4.3.2 +## [16] compiler_4.1.0 sandwich_3.0-1 mvtnorm_1.1-2 +## [19] Lmoments_1.3-1 stringr_1.4.0 digest_0.6.27 +## [22] rio_0.5.27 pkgconfig_2.0.3 parallelly_1.26.1 +## [25] rlang_0.4.11 readxl_1.3.1 gridGraphics_0.5-1 +## [28] farver_2.1.0 generics_0.1.0 dplyr_1.0.7 +## [31] ModelMetrics_1.2.2.2 zip_2.2.0 magrittr_2.0.1 +## [34] ggplotify_0.0.7 Formula_1.2-4 Matrix_1.3-4 +## [37] Rcpp_1.0.6 munsell_0.5.0 fansi_0.5.0 +## [40] abind_1.4-5 lifecycle_1.0.0 stringi_1.6.2 +## [43] pROC_1.17.0.1 MASS_7.3-54 plyr_1.8.6 +## [46] grid_4.1.0 parallel_4.1.0 listenv_0.8.0 +## [49] forcats_0.5.1 crayon_1.4.1 lattice_0.20-44 +## [52] splines_4.1.0 hms_1.1.0 tmvnsim_1.0-2 +## [55] pillar_1.6.1 pcse_1.9.1.1 codetools_0.2-18 +## [58] glue_1.4.2 BiocManager_1.30.16 AER_1.2-9 +## [61] vctrs_0.3.8 tweenr_1.0.2 foreach_1.5.1 +## [64] cellranger_1.1.0 gtable_0.3.0 purrr_0.3.4 +## [67] polyclip_1.10-0 reshape_0.8.8 future_1.21.0 +## [70] assertthat_0.2.1 openxlsx_4.2.4 xtable_1.8-4 +## [73] lfe_2.8-6 survival_3.2-11 tibble_3.1.2 +## [76] iterators_1.0.13 rvcheck_0.1.8 globals_0.14.0 +## [79] ellipsis_0.3.2 + +######################################## +#### Functions +######################################## + +## A function to standardize variables on a 0 to 1 scale +zero_to_one <- function(x, na.rm = T) +{ + (x - min(x, na.rm = T)) / (max(x, na.rm = T) - min(x,na.rm = T)) +} + +## Two functions to format numbers +format_int <- function(x) +{ + formatC(x, digits = 0, format = "f", big.mark = ",") +} +format_dec <- function(x) +{ + formatC(x, digits = 2, format = "f", big.mark = ",") +} + +## Function to calculate Cronbach's Alpha values for the first study +cbfunc <- function(data){ + require(psych) + attach(data) + cbvars <- list( + possec = data.frame(i_admire, i_love), + negsec = data.frame(i_resent, i_shame), + pospri = data.frame(i_excite, i_plea), + negpri = data.frame(i_fear, i_anger), + icb_pre = data.frame(m_less, m_learn, m_suffer), + diss = data.frame(d_uncom, d_uneasy, d_bother, d_tense, d_concern), + icb_post = data.frame(gji_violence, gji_lazy, gji_work, gji_honest, + gji_victim), + emp = data.frame(e_sym, e_moved, e_com, e_warm, e_soft, e_tender), + law = data.frame(law_english, law_tuition, law_welfare, law_hire), + bills = data.frame(st8_hb116, st8_hb469, st8_hb466), + harm = data.frame(law_english, law_tuition, law_welfare, law_hire, + immig_opinion_reverse, arizona_law, st8_hb497), + help = data.frame(st8_hb116, st8_hb469, st8_hb466) + ) + detach(data) + cbvalues <- sapply( + names(cbvars), + function(x){ + psych::alpha(cbvars[[x]])$total$raw_alpha + }) + names(cbvalues) <- names(cbvars) + return(cbvalues) +} + +## Marginal Effects function +TwowayME.f <- function(M, X, Z, level) +{ + ## A function to generate 2-way marginal effects plots in R. + ## Written by Joshua Gubler ~ http://scholar.byu.edu/jgubler; originally + ## based on Stata code from Joel Selway and Brambor, Clark, and Golder. + ## Last modified: 12 October 2014 (Move to ggplot2 and reduce output to + ## values rather than graph - David Romney) + ## Variables must be in the following order: y = x z (control variables + ## here) xz. The model can include as many control variables as you need. + ## M = an object of type "lm," "glm," or other estimation -- i.e. the object + ## that contains the regression estimation you seek to plot. + ## X = the variable whose effect on Y you seek to plot + ## Z = the moderating variable (will be positioned on the X-axis of the + ## plot) + ## xlab = label for x-axis (in quotes) + ## ylab = label for y-axis (in quotes) + ## level = to set the confidence level. Two options (don't put these in + ## quotes): 95, 90. If you do not specify either option, the confidence + ## intervals will not be correct. + ## Example: TwowayME.f(estimation.lm, ses, edu, "Education levels", + ## "Effect of SES on Civil War", 90) + + S <- summary(M) + N <- c(1:20) + + ## 20 equally-spaced values for the moderating variable + zmin <- rep(min(Z, na.rm = TRUE), 20) + zmax <- rep(max(Z, na.rm = TRUE), 20) + Znew <- (((N - 1) / (20 - 1)) * (zmax - zmin)) + zmin + + ## Grab elements of coefficient and vcov matrix + H <- head(S$coefficients, 3) + T <- tail(S$coefficients, 1) + b <- rbind(H, T) + Vcov <- vcov(M) + Vcov <- as.data.frame(Vcov) + Vcov1 <- Vcov[, c(1:3)] + Vcov2 <- Vcov[, -c(0:0 - length(Vcov))] + Vcov <- cbind(Vcov1, Vcov2) + Vh <- head(Vcov, 3) + Vt <- tail(Vcov, 1) + V <- rbind(Vh, Vt) + b1 <- b[2, 1] + b2 <- b[3, 1] + b3 <- b[4, 1] + varb1 <- V[2, 2] + varb2 <- V[3, 3] + varb3 <- V[4, 4] + covb1b3 <- V[4, 2] + covb2b3 <- V[4, 3] + + ## Calculate ME values + conb <- b1 + b3 * Znew + + ## Calculate standard errors when W = 0, W = 1, W = 2, W = 3, and when W = 4 + conse <- sqrt(varb1 + varb3 * (Znew^2) + 2 * covb1b3 * Znew) + + ## Upper and lower CIs + ci <- NA + ci[level == 95] <- qnorm(0.975) + ci[level == 90] <- qnorm(0.95) + a = ci * conse + upper = conb + a + lower = conb - a + + ## Return values + return(data.frame(Znew, conb, upper, lower)) +} + + +################################################################################ +##### Load and clean the study 1 data +################################################################################ + +## Some initial pre-processing of the data was done to remove all identifying +## variables and rename some other variables - the code used to do this can be +## found in this section (commented out so that it doesn't run as this has already +## been done to the included data file) + +## Note that a few of the variable names/values used in the code below contain +## acronyms that would be identifying, these have been marked out with +## asterisks + +## ## Clear workspace +## rm(list = ls()) + +## ## Packages +## library(foreign) +## library(data.table) + +## ## Load data +## my_file <- "original_data.dta" +## stud01 <- data.table(read.dta(my_file)) + +## ## Remove variables containing personal information +## remove <- c( +## ## Deleted here are the response id, response set, name, email, ip address, +## ## status, start date, and end date +## "V1", "V2", "V3", "V5", "V6", "V7", "V8", "V9", + +## ## Next we delete a bunch of variables that match respondents to specific +## ## addresses, precincts, or other identifying information (many of these are +## ## variables related to geocoding the respondents, information from the *** +## ## database, or information on elected officials), as well as some +## ## variables that we do not use in our analysis +## "county", "precinct", "cd_new", "ld_new", "sd_new", "cdold", "ld_old", +## "sd_old", "phone", "address", "city", "zip", "address_id", "std_priadr", +## "std_fullstreetaddr", "std_city", "std_state", "std_postalcode", "std_zip", +## "stdzip4", "std_crt", "std_dpbc", "std_lot", "std_lotord", "std_achkdi", +## "std_errstt", "std_rectyp", "std_dpvftn", "std_dpvstt", "std_county", +## "matchtype", "longitude", "latitude", "geocodequalitytype", "matchscore", +## "attended", "county_alt", "state_alte", "state_dele", "precinct_c", +## "secretary", "election_j", "county_del", "vice_chair", "treasurer", +## "caucus", "dup", "rand", "sr_id", "std_secadr", "***accesscode", "***", +## "website", "called", "termend", "electoaccessc", "title", "orgphone", +## "population", "class", "electedofficial", "lacking", "former", "comments", +## "religion", "rel_activity", "***_*******", "other_lang", "*******_lang", +## "*******_area", "DO_Q_Q5", "DO_Q_Q18", "DO_Q_Q16", "DO_Q_Q12", +## "DO_Q_Q20", "DO_Q_Q22", "DO_Q_Q14", "DO_BR_FL_20", "respondent_comments" +## ) +## stud01[, (remove) := NULL] + +## ## Rename variables (uses a .csv with the old and new names saved in it) +## var_names <- "var_names.csv" +## var_names <- fread(var_names) +## setnames(stud01, old = var_names$old_names, new = var_names$new_names) + +## ## Remove a respondent who was not part of the three main samples +## stud01 <- stud01[sample != "****", ] +## ## Was 5,812 respondents, now 5,811 + +## ## Additionally, the names of some variables/values were altered +## setnames(stud01, +## c("****_hb497", "****_hb116", "****_hb469", "****_hb466", "****_news"), +## c("st8_hb497", "st8_hb116", "st8_hb469", "st8_hb466", "st8_news")) +## stud01[, sample := plyr::mapvalues(sample, "***", "Voter")] + +## ## Save data +## setwd("Data") +## save(stud01, file = "stud01_deID.RData") +## write.csv(stud01, file = "stud01_deID.csv", row.names = FALSE) +## setwd("..") + + +###################### +##Load and clean Study 1 data ## +###################### + +## Change this to your working directory +#setwd("~/Dropbox/GKMR/PapersPresentations/2020Submission/Replication") +setwd("/Users/jrg27/Dropbox/Karpowitz_Monson_Project/CognitiveDissonance/PapersPresentations/2020Submission/Replication") +##Load data +load("Data/stud01_deID.RData") + +## Add sample dummy variables +old <- c("Caucus", "Delegate", "Voter", "Elected Official") +new <- c("caucus_dummy", "delegate_dummy", "voter_dummy", "elect_dummy") +stud01[, (new) := lapply(old, function(x) {as.numeric(stud01$sample == x)})] +stud01[, activist_dummy := as.numeric(caucus_dummy == 1 + | delegate_dummy == 1)] +rm(old, new) + +## Fix treatment variables +vars <- c("treatment1", "treatment2", "treatment3", "treatment4") +stud01[, (vars) := lapply(1:4, + function(x) {as.numeric(stud01$treatment == x)})] +rm(vars) + +## Creating new numeric variables for some of the variables +stud01[, partyidnum := as.numeric(partyid)] +stud01[partyid == "Other" | partyid == "Don't know", partyidnum := NA] # Those + # who said "Other" or "Don't know" are + # marked as NAs +stud01[, ideologynum := as.numeric(ideology)] +stud01[ideology == "Don't know", ideologynum := 3] # Those who said "Don't know" + # are marked as "Neither, middle of the + # road" +stud01[, year_born := as.numeric(as.character(year_born))] +stud01[, age := 2012 - year_born] +stud01[, gendernum := ifelse(gender == "Male", 1, 0)] +stud01[, incomenum := as.numeric(income)] +stud01[, educationnum := as.numeric(education)] + +## Changing some existing variables to numeric +var <- c("m_less", "m_learn", "m_suffer", "gji_violence", "gji_lazy", + "gji_work", "gji_honest", "gji_victim", "gji_opport", "gji_right", + "law_english", "law_tuition", "law_welfare", "law_hire", + "immig_opinion", "arizona_law", "st8_hb497", "st8_hb116", + "st8_hb469", "st8_hb466") +stud01[, (var) := lapply(stud01[, var, with = FALSE], as.numeric)] +rm(var) + +## Reverse-coding some variables +stud01[, m_learn := abs(m_learn - 8)] +stud01[, immig_opinion_reverse := abs(immig_opinion - 5)] +stud01[, st8_hb116_reverse := abs(st8_hb116 - 6)] +stud01[, st8_hb469_reverse := abs(st8_hb469 - 6)] +stud01[, st8_hb466_reverse := abs(st8_hb466 - 6)] +stud01[, ideologynum := abs(ideologynum - 6)] + +## Standardizing variables +var <- c("partyidnum", "ideologynum", "i_admire", "i_love", + "i_resent", "i_shame", "i_excite", "i_plea", "i_fear", "i_anger", + "m_less", "m_learn", "m_suffer", "d_uncom", "d_uneasy", "d_bother", + "d_tense", "d_concern", "gji_violence", "gji_lazy", "gji_work", + "gji_honest", "gji_victim", "gji_opport", "gji_right", "e_sym", + "e_moved", "e_com", "e_warm", "e_soft", "e_tender", "law_english", + "law_tuition", "law_welfare", "law_hire", "immig_opinion", + "arizona_law", "st8_hb497", "st8_hb116", "st8_hb469", "st8_hb466", + "immig_opinion_reverse", "st8_hb116_reverse", "st8_hb469_reverse", + "st8_hb466_reverse") +## Remove zeros that aren't supposed to be there +stud01[, (var) := lapply(stud01[, var, with = FALSE], function(x) { + x[x == 0] <- NA + return(x) +})] +## Apply standardizing function +stud01[, (var) := lapply(stud01[, var, with = FALSE], zero_to_one)] +rm(var) + +## Index variables +stud01[, possec := rowMeans(.SD, na.rm = TRUE), .SDcols = c("i_admire", "i_love")] +stud01[, negsec := rowMeans(.SD, na.rm = TRUE), .SDcols = c("i_resent", "i_shame")] +stud01[, pospri := rowMeans(.SD, na.rm = TRUE), .SDcols = c("i_excite", "i_plea")] +stud01[, negpri := rowMeans(.SD, na.rm = TRUE), .SDcols = c("i_fear", "i_anger")] +stud01[, icb_pre := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("m_less", "m_learn", "m_suffer")] +stud01[, diss := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("d_uncom", "d_uneasy", "d_bother", "d_tense", "d_concern")] +stud01[, icb_post := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("gji_violence", "gji_lazy", "gji_work", "gji_honest", + "gji_victim")] +stud01[, emp := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("e_sym", "e_moved", "e_com", "e_warm", "e_soft", "e_tender")] +stud01[, law := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("law_english", "law_tuition", "law_welfare", "law_hire")] +stud01[, bills := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("st8_hb116", "st8_hb469", "st8_hb466")] +stud01[, harm := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("law_english", "law_tuition", "law_welfare", "law_hire", + "immig_opinion_reverse", "arizona_law", "st8_hb497")] +stud01[, help := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("st8_hb116", "st8_hb469", "st8_hb466")] + +## Add two dichotomous variables +stud01[emp <= 0.5, emp_d := 0] +stud01[emp > 0.5, emp_d := 1] +stud01[icb_pre <= 0.5, icb_pre_d := 0] +stud01[icb_pre > 0.5, icb_pre_d := 1] + +######################################## +#### Clean out certain respondents (record n-size at each step) +######################################## + +## Save copy of original data and size +dat_1 <- copy(stud01) +n_1 <- nrow(dat_1) + +## Remove duplicates +dat_2 <- stud01 <- stud01[!duplicated(identifier), ] +n_2 <- nrow(dat_2) + +## Remove non-whites +values <- names(table(stud01$ethnicity)) +drops <- values[!(values == "White / Caucasian")] +dat_3 <- stud01 <- stud01[!(ethnicity %in% drops), ] +n_3 <- nrow(dat_3) +rm(values, drops) + +## Remove those who responded "No" or didn't respond to the manipulation +## question +dat_4 <- stud01 <- stud01[!(is.na(stud01$vidscreen)), ] +dat_4 <- stud01 <- stud01[stud01$vidscreen == "Yes", ] +n_4 <- nrow(dat_4) + +## Remove those who didn't finish the survey +dat_5 <- stud01 <- stud01[finished == 1, ] +n_5 <- nrow(dat_5) + +## Summary of those dropped +dropped <- data.table( + " " = c("Full dataset", "Duplicates", "Non-whites", + "Didn't pass manipulation check", + "Didn't finish"), + "Removed" = c(0, n_1 - n_2, n_2 - n_3, n_3 - n_4, n_4 - n_5), + "Subjects Remaining" = c(n_1, n_2, n_3, n_4, n_5) +) + +## Other summaries +stud01[, .N, by = sample] +stud01[, .N, by = treatment] +with(stud01, table(sample, treatment)) + +## Calculate Cronbach's alpha values +Exp1CB <- list( + all = cbfunc(stud01), + caucus = cbfunc(stud01[caucus_dummy == 1, ]), + delegate = cbfunc(stud01[delegate_dummy == 1, ]), + voter = cbfunc(stud01[voter_dummy == 1, ]), + elect = cbfunc(stud01[elect_dummy == 1, ]) + ) + +## Treatment names +stud01$treat.names <- NA +stud01$treat.names[stud01$treatment==1] <- "Humanization" +stud01$treat.names[stud01$treatment==2] <- "Information" +stud01$treat.names[stud01$treatment==3] <- "Combined" +stud01$treat.names[stud01$treatment==4] <- "Control" + +###Code for samples: + +## Voters numbers +invited <- 5513 +completed <- nrow(dat_2[voter_dummy == 1 & finished == 1, ]) +responserate <- format_dec(completed / invited * 100) +invited <- format_int(invited) +completed <- format_int(completed) +analysis <- format_int(nrow(stud01[voter_dummy == 1, ])) + +## Activist numbers +invited <- 2517 + 25711 +completed <- nrow(dat_2[activist_dummy == 1 & finished == 1, ]) +responserate <- format_dec(completed / invited * 100) +invited <- format_int(invited) +completed <- format_int(completed) +analysis <- format_int(nrow(stud01[activist_dummy == 1, ])) + +## Elected officials numbers +invited <- 1714 +completed <- nrow(dat_2[elect_dummy == 1 & finished == 1, ]) +responserate <- format_dec(completed / invited * 100) +invited <- format_int(invited) +completed <- format_int(completed) +analysis <- format_int(nrow(stud01[elect_dummy == 1, ])) + +## Total number +total <- format_int(nrow(stud01)) + + + +################################################################################ +##### To load and clean Study 2 data +################################################################################ + +## As in Study 1, we begin with code (commented out) to remove identifying or unused +## variables from our dataset. This has already been done prior to this replication file. + +## ## Clear workspace +## rm(list = ls()) + +## ## Packages +## library(foreign) +## library(data.table) + +## ## Read in the data +## stud02 <- read_dta("Data/stud02_deID.dta") +## stud02 <- as.data.table(stud02) + +## ## Remove variables +## remove <- c( +## "ResponseID", "ResponseSet", "IPAddress", "StartDate", "EndDate", +## "RecipientLastName", "RecipientFirstName", "RecipientEmail", +## "ExternalDataReference", "Status", "Voter_ID", "order", "quartile", +## "random", "Q1", "Q2", "Q3", "Q4", "Q5_1", "Q6_1", "Q7_1", "Q8_1", "Q9_1", +## "Q10_1", "Q11_1", "Q12_1", "Q13_1", "Q14_1", "Q15_1", "Q16_1", "Q17_1", +## "Q18_1", "Q19_1", "Q20_1", "Q21_1", "Q22_1", "Q23_1", "Q24_1", "Q26", +## "Q27", "Q28", "Q29", "Q30", "Q31", "Q32", "Q33", "Q34", "Q37", "Q39", +## "Q45", "RO_BL_Hispanic_Pictures", "RO_BL_Positive_Hispanic", +## "RO_BL_Dissonance", "RO_BL_Policy_Attitudes", "DO_Q_Q5", "DO_Q_Q6", +## "DO_Q_Q7", "DO_Q_Q8", "DO_Q_Q9", "DO_Q_Q10", "DO_Q_Q11", "DO_Q_Q12", +## "DO_Q_Q13", "DO_Q_Q14", "DO_Q_Q15", "DO_Q_Q16", "DO_Q_Q17", "DO_Q_Q25", +## "DO_Q_Q30", "DO_Q_Q31", "DO_Q_Q32", "DO_Q_Q33", "DO_Q_Q35", "DO_Q_Q36", +## "DO_Q_Q38", "DO_Q_Q40", "DO_Q_Q41", "DO_Q_Q43", "DO_Q_Q44", +## "LocationLatitude", "LocationLongitude", "LocationAccuracy", "V3", "V4", +## "Email", "V6", "V7", "V8", "V9", "V10", "Middle_Name", "PartyReg", +## "County_ID", "House_Number", "Direction_Prefix", "Street", +## "Direction_Suffix", "City", "Zip", "Street_Type", "Phone", "Unit_Type", +## "Unit_Number", "Name_Suffix", "House_Number_Suffix", "w1_1", "w1_2", +## "w1_3", "w1_5", "w1_7", "w1_8_1", "w1_8_2", "w1_8_3", "w1_8_4", "w1_9", +## "w1_10_1", "w1_10_2", "w1_10_3", "w1_10_4", "w1_11", "w1_12_1", "w1_12_2", +## "w1_12_3", "w1_12_4", "w1_13", "w1_14_1", "w1_14_2", "w1_14_3", "w1_14_4", +## "w1_15", "w1_16_1", "w1_16_2", "w1_16_3", "w1_16_4", "w1_17", "w1_18_1", +## "w1_18_2", "w1_18_3", "w1_18_4", "w1_19", "w1_20_1", "w1_20_2", "w1_20_3", +## "w1_20_4", "w1_21", "w1_22_1", "w1_22_2", "w1_22_3", "w1_22_4", "w1_23", +## "w1_24_1", "w1_24_2", "w1_24_3", "w1_24_4", "w1_25", "w1_26_1", "w1_26_2", +## "w1_26_3", "w1_26_4", "w1_27", "w1_28_1", "w1_28_2", "w1_28_3", "w1_28_4", +## "w1_29", "w1_30_1", "w1_30_2", "w1_30_3", "w1_30_4", "w1_31", "w1_32_1", +## "w1_32_2", "w1_32_3", "w1_32_4", "w1_33", "w1_34_1", "w1_34_2", "w1_34_3", +## "w1_34_4", "w1_35", "w1_36_1", "w1_36_2", "w1_36_3", "w1_36_4", "w1_37", +## "w1_38_1", "w1_38_2", "w1_38_3", "w1_38_4", "w1_39", "w1_40_1", "w1_40_2", +## "w1_40_3", "w1_40_4", "w1_41", "w1_42_1", "w1_42_2", "w1_42_3", "w1_42_4", +## "w1_43", "w1_44_1", "w1_44_2", "w1_44_3", "w1_44_4", "w1_45", "w1_46", +## "w1_49", "w1_50_1", "w1_50_2", "w1_50_3", "w1_50_4", "w1_50_5", "w1_51_1", +## "w1_51_2", "w1_51_3", "w1_51_4", "w1_52_1", "w1_52_2", "w1_52_3", +## "w1_52_4", "w1_53_1", "w1_53_2", "w1_53_3", "w1_53_4", "w1_54_1", +## "w1_54_2", "w1_54_3", "w1_54_4", "w1_55", "w1_59", "w1_60", "w1_61", +## "w1_62", "LastName", "FirstName", "_merge" +## ) +## stud02[, (remove) := NULL] + +## ## Save data +## setwd("Data") +## save(stud02, file = "stud02_deID.RData") +## write.csv(stud02, file = "stud02_deID.csv", row.names = FALSE) +## setwd("..") + +############ +## Load Data +############ + +load("Data/stud02_deID.RData") + +## Treatment conditions +myvars <- c("RO_BR_FL_238", "RO_BR_FL_268", "RO_BR_FL_265", "RO_BR_FL_262") +stud02[apply(stud02[, ..myvars], 1, function(x) any(x == "Positive Legal", na.rm = TRUE)), + condition := 0] +stud02[apply(stud02[, ..myvars], 1, function(x) any(x == "Positive Illegal", na.rm = TRUE)), + condition := 1] + +## Humanization Measures and Index +myvars <- c("post_hum1", "post_hum2") +setnames(stud02, paste0("Q25_", 1:8), paste0("post_hum", 1:8)) +setnames(stud02, grep("^hum", names(stud02), value = TRUE), + paste0("pre_", grep("^hum", names(stud02), value = TRUE))) +tmpcb <- psych::alpha(stud02[, ..myvars]) +stud02[, post_hum_measure := tmpcb$scores] +stud02[, post_hum_measure := (post_hum_measure - 1) / (7 - 1)] +## Alpha +tmpcb$total$raw_alpha + +## Dissonance Measures and Index +setnames(stud02, c("Q35_1", "Q35_4", "Q35_5", "Q35_7", "Q35_8", "Q36_3", + "Q36_4", "Q36_5", "Q36_8", "Q36_9"), + paste0("diss", 1:10)) +myvars <- c("diss1", "diss2", "diss5", "diss6", "diss7") +tmpcb <- psych::alpha(stud02[, ..myvars]) +stud02[, diss_measure := tmpcb$scores] +stud02[, diss_measure := (diss_measure - 1) / (7 - 1)] +my_med <- median(stud02$diss_measure, na.rm = TRUE) +stud02[, diss_hi := as.integer(!(diss_measure <= my_med))] +stud02[is.na(diss_measure), diss_hi := NA] +my_med <- median(stud01$diss[stud01$treatment == 4], na.rm = TRUE) +stud02[, diss_hi_alt := as.integer(!(diss_measure <= my_med))] +stud02[is.na(diss_measure), diss_hi_alt := NA] +## Alpha +tmpcb$total$raw_alpha + +## Empathy Measures and Index +setnames(stud02, paste0("Q38_", 1:6), paste0("emp", 1:6)) +tmpcb <- psych::alpha(stud02[, paste0("emp", 1:6), with = FALSE]) +stud02[, emp_index := tmpcb$scores] +stud02[, emp_index01 := (emp_index - 1) / (7 - 1)] +## Alpha +tmpcb$total$raw_alpha + +## Policy Measures and Index +oldvars <- c("Q40_1", "Q40_2", "Q41_14", "Q41_21", "Q41_22", "Q41_16", "Q41_20", + "Q42", "Q43_6", "Q43_7", "Q44_1", "Q44_2", "Q44_3", "Q44_4") +newvars <- c("pol1a", "pol1b", "pol2a", "pol2b", "pol2c", "pol2d", "pol2e", + "pol3", "pol4a", "pol4b", "pol5a", "pol5b", "pol5c", "pol5d") +setnames(stud02, oldvars, newvars) +stud02[, pol1b_rev := abs(pol1b - 6)] +stud02[, pol3_rev := abs(pol3 - 5)] +myvars <- c("pol1b_rev", "pol3_rev", "pol4a", "pol4b", "pol5a", "pol5b", "pol5c", "pol5d") +stud02[, pol3_gohome := 0] +stud02[pol3 == 1 | pol3 == 2, pol3_gohome := 1] +stud02[is.na(pol3), pol3_gohome := NA] +tmpcb <- psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE) +stud02[, policy_harm := tmpcb$scores] +stud02[, policy_harm := (policy_harm - 1) / (6.4 - 1)] +## Alpha +tmpcb$total$raw_alpha + +## Antipathy Measures and Index +stud02[, icb8_rev := abs(icb8 - 8)] +myvars <- paste0("icb", c(1:6, 8:10)) +myvars[7] <- "icb8_rev" +tmpcb <- psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE) +stud02[, icb_measure := tmpcb$scores] +## Alpha +tmpcb$total$raw_alpha + +## Fix the hi_icb measure +stud02[, hi_icb := as.integer(!(icb_measure < 4))] +stud02[is.na(icb_measure), hi_icb := NA] + +## Standardize some measures +stud02[, icb_measure := zero_to_one(icb_measure)] +stud02[, partyid := zero_to_one(partyid)] + +## Change gender measure to dichotomous +stud02[, gender := gender - 1] + +## Remove non-whites +stud02 <- stud02[!(stud02$ethnicity %in% c(1:4, 6:8)), ] + +## Remove people who never received a treatment assignment in wave 2 +stud02 <- stud02[!is.na(condition)] + +################################################################################ +##### To load and clean Pilot Data (presented in the appendix) +################################################################################ + +## ## As the previous studies, this code, now commented out, keeps only the necessary variables from the +## ## pretest dataset + +## ## Clear workspace +## rm(list = ls()) + +## ## Packages +## library(haven) + +## ## Load data +## predf <- read_dta("../R&RFiles/All_Variables_2011_***_Student_Cognitive_Dissonance.dta") +## predf <- subset(predf, vidscreen==1) +## predf <- as.data.table(predf) + +## ## Remove variables containing personal information +## remove <- c( +## "responseid", "responseset", "name", "identifier", "email", "ipaddress", +## "status", "startdate", "enddate", "finished", "treatment", "Home_State", +## "Home_Country", "Utah_or_not_", "Q70", "welcome", "govapprove", +## "legapprove", "t_illegal", "t_poly", "t_black", "t_mormons", "t_catholics", +## "t_gays", "treatment1", "treatment2", "treatment3", "treatment4", +## "treatment5", "treatment6", "treatment7", "treatment8", "treatment0", +## "vid_firstclick", "vid_lastclick", "vid_pagesubmit", "vid_clickcount", +## "vidscreen", "e_sym", "e_moved", "e_com", "e_warm", "e_soft", "e_tender", +## "i_admire", "i_love", "i_resent", "i_shame", "i_excite", "i_plea", +## "i_fear", "i_anger", "i_admire_0", "i_love_0", "i_resent_0", "i_shame_0", +## "i_excite_0", "i_plea_0", "i_fear_0", "i_anger_0", "infra_firstclick", +## "infra_lastclick", "infra_pagesubmit", "infra_clickcount", "d_uncom", +## "d_angry", "d_shame", "d_uneasy", "d_friend", "d_disgust", "d_emba", +## "d_bother", "d_firstclick", "d_lastclick", "d_pagesubmit", "d_clickcount", +## "d_opti", "d_annoy", "d_tense", "d_disa", "d_happy", "d_ener", "d_concern", +## "d_good", "d2_firstclick", "d2_lastclick", "d2_pagesubmit", +## "d2_clickcount", "gji_right", "gji_firstclick", "gji_lastclick", +## "gji_pagesubmit", "gji_clickcount", "law_english", "law_tuition", +## "law_welfare", "law_hire", "law_firstclick", "law_lastclick", +## "law_pagesubmit", "law_clickcount", "immig_opinion", "arizona_law", +## "utah_hb497", "utah_hb116", "utah_hb469", "utah_hb466", "utah_news", +## "mani_check", "Q38", "internet", "gender", "year_born", "partyid", +## "ideology", "grad_year", "religion", "rel_activity", "lds_mission", +## "other_lang", "mission_lang", "mission_area", "employ", "ethnicity", +## "marital", "income", "rate_survey", "Q57" +## ) +## predf[, (remove) := NULL] + +## ## Save data +## setwd("Data") +## save(predf, file = "pretest.RData") +## write.csv(predf, file = "pretest.csv", row.names = FALSE) +## setwd("..") + +############ +## Load data +############ +load("Data/pretest.RData") + +# Outgroup Antipathy Measure: +# Changing to numeric +predf$m_less <- as.numeric(predf$m_less) +predf$m_learn <- as.numeric(predf$m_learn) +# Reverse coding one of the variables +predf$m_learn <- abs(predf$m_learn - 8) +predf$m_suffer <- as.numeric(predf$m_suffer) +predf$gji_violence <- as.numeric(predf$gji_violence) +predf$gji_lazy <- as.numeric(predf$gji_lazy) +predf$gji_work <- as.numeric(predf$gji_work) +predf$gji_honest <- as.numeric(predf$gji_honest) +predf$gji_victim <- as.numeric(predf$gji_victim) +predf$gji_opport <- as.numeric(predf$gji_opport) +# Generating index: +preantipathy.df <- data.frame(predf$m_less,predf$m_learn,predf$m_suffer,predf$gji_violence,predf$gji_lazy,predf$gji_work,predf$gji_honest,predf$gji_victim,predf$gji_opport) +## cronbach(preantipathy.df) +predf$antipathy <- (predf$m_less + predf$m_learn + predf$m_suffer + predf$gji_violence + predf$gji_lazy + predf$gji_work + predf$gji_honest + predf$gji_victim + predf$gji_opport)/9 + +# AUTHORITARIANISM +auth.df <- data.frame(as.numeric(predf$v_indep), as.numeric(predf$v_obed), as.numeric(predf$v_curi), as.numeric(predf$v_well)) +predf$auth <- (as.numeric(predf$v_indep) + (3-as.numeric(predf$v_obed)) + as.numeric(predf$v_curi) + (3-as.numeric(predf$v_well)))/4 + +# SOCIAL DOMINANCE ORIENTATION +# Changing to numeric +predf$sdo_eQualize <- as.numeric(predf$sdo_eQualize) +predf$sdo_inferior <- as.numeric(predf$sdo_inferior) +predf$sdo_desirable <- as.numeric(predf$sdo_desirable) +predf$sdo_chance <- as.numeric(predf$sdo_chance) +predf$sdo_problems <- as.numeric(predf$sdo_problems) +predf$sdo_step <- as.numeric(predf$sdo_step) +predf$sdo_ideal <- as.numeric(predf$sdo_ideal) +predf$sdo_stay <- as.numeric(predf$sdo_stay) +# Reverse coding +predf$sdo_eQualize <- abs(predf$sdo_eQualize - 8) +predf$sdo_desirable <- abs(predf$sdo_desirable - 8) +predf$sdo_problems <- abs(predf$sdo_problems - 8) +predf$sdo_ideal <- abs(predf$sdo_ideal - 8) +# Generating the index variable +pre.sdo.df <- subset(predf, select=c("sdo_eQualize","sdo_inferior","sdo_desirable","sdo_chance","sdo_problems","sdo_step","sdo_ideal","sdo_stay")) +predf$sdo <- (predf$sdo_eQualize + predf$sdo_inferior + predf$sdo_desirable + predf$sdo_chance + predf$sdo_problems + predf$sdo_step + predf$sdo_ideal + predf$sdo_stay)/ncol(pre.sdo.df) + +# FEELING THERM AND TRADITIONAL ETHNO MEASURE (coded now on a 7-point scale) +predf$whitetherm <- as.numeric(predf$t_white)*.07 +predf$hisptherm <- as.numeric(predf$t_hispanics)*.07 +predf$ethno <- (4 + ((predf$whitetherm)*(3/7) - (predf$hisptherm)*(3/7))) + + + +################################################################################ +##### Results from the Body of the Paper +################################################################################ + +######################################## +#### Section: Research Design +######################################## + +## Alpha for study 1 antipathy +Exp1CB$all["icb_pre"] + +## Treatment condition numbers for study 1 +table(stud01$treatment) +## 1 = Humanization +## 2 = Information +## 3 = Combined +## 4 = Control + +## Alpha for infrahumanization (positive secondary emotions) from first study +Exp1CB$all["possec"] + +## Alpha for empathy from first study +Exp1CB$all["emp"] + +## Alpha for the policy harm index +Exp1CB$all["harm"] + +## Alpha for antipathy from second study +myvars <- paste0("icb", c(1:6, 8:10)) +myvars[7] <- "icb8_rev" +psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE)$total$raw_alpha + +## Study 2 above/below midpoint +table(stud02$hi_icb) + +## Alpha for dissonance from second study +myvars <- c("diss1", "diss2", "diss5", "diss6", "diss7") +psych::alpha(stud02[, ..myvars])$total$raw_alpha + +## Alpha for empathy from second study +psych::alpha(stud02[, paste0("emp", 1:6), with = FALSE])$total$raw_alpha + +## Alpha for policies from second study +myvars <- c("pol1b_rev", "pol3_rev", "pol4a", "pol4b", "pol5a", "pol5b", "pol5c", "pol5d") +psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE)$total$raw_alpha + + + +######################################## +#### Section: Changing Hearts, Study 1 +######################################## + +## Main study 1 regression model for humanization ~ treatments * antipathy +r_s1_hum_antdint <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) + +## To produce the figure, use the predict function to calculate subgroup means +## and confidence intervals +p_data <- data.frame( + treatment = c(1, 2, 3, 4, 1, 2, 3, 4), + treatment1 = c(1, 0, 0, 0, 1, 0, 0, 0), + treatment2 = c(0, 1, 0, 0, 0, 1, 0, 0), + treatment3 = c(0, 0, 1, 0, 0, 0, 1, 0), + icb_pre_d = c(0, 0, 0, 0, 1, 1, 1, 1) +) +p_data <- cbind(p_data, + predict(r_s1_hum_antdint, newdata = p_data, interval = "confidence")) +p_data <- as.data.table(p_data) +labs01 <- c("Low", "High") +labs02 <- c("Control", "Information", "Humanization", + "Combined") +p_data[, icb_pre_d := plyr::mapvalues(icb_pre_d, 0:1, labs01)] +p_data[, icb_pre_d := factor(icb_pre_d, labs01, labs01)] +p_data[, treatment := plyr::mapvalues(treatment, c(4, 2, 1, 3), labs02)] +p_data[, treatment := factor(treatment, labs02, labs02)] + +## Plot of study 1 humanization by level of antipathy and treatment group +p <- ggplot(p_data, aes(y = fit, x = treatment, ymin = lwr, ymax = upr, + shape = icb_pre_d, + group = icb_pre_d)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line() + + geom_point(size = 3, fill = "white") + + scale_shape_manual("Outgroup Antipathy", values = 21:22) + + labs(x = "Treatment", y = "Humanization Level", + linetype = "Outgroup Antipathy") +ggsave("FiguresTables/hum01_antipathy.png", p, width = 6.25, height = 3) + +## Numbers quoted in the paragraph from study 1 +p_data[icb_pre_d == "High" & treatment == "Control"] +p_data[icb_pre_d == "High" & treatment == "Humanization"] +p_data[icb_pre_d == "Low" & treatment == "Control"] +p_data[icb_pre_d == "Low" & treatment == "Humanization"] + +## Statistical test of difference between conditions +## Comparing high antipathy in control to high antipathy in humanization treatment +hypo_01 <- "0*treatment1 + 1*icb_pre_d + 0*treatment1:icb_pre_d = 1*treatment1 + 1*icb_pre_d + 1*treatment1:icb_pre_d" +linearHypothesis(r_s1_hum_antdint, hypo_01) +## Above is equivalent to joint test of the coefficients on treatment1 and +## treatment1:icb_pre_d +## Comparing low antipathy in control to low antipathy in humanization treatment +hypo_02 <- "0*treatment1 + 0*icb_pre_d + 0*treatment1:icb_pre_d = 1*treatment1 + 0*icb_pre_d + 0*treatment1:icb_pre_d" +linearHypothesis(r_s1_hum_antdint, hypo_02) +## Above is equivalent to a test of the coefficient on treatment 1 + +## Standard deviation of the outcome +sd(stud01$possec, na.rm = TRUE) +0.18 / 0.27 +0.09 / 0.27 + +## Comparison to combined treatment +p_data[icb_pre_d == "High" & treatment == "Combined"] +p_data[icb_pre_d == "Low" & treatment == "Combined"] + +## Similar results with statistical test using the combined treatment +hypo_01 <- "0*treatment3 + 1*icb_pre_d + 0*treatment3:icb_pre_d = 1*treatment3 + 1*icb_pre_d + 1*treatment3:icb_pre_d" +linearHypothesis(r_s1_hum_antdint, hypo_01) +## Comparing low antipathy in control to low antipathy in humanization treatment +hypo_02 <- "0*treatment3 + 0*icb_pre_d + 0*treatment3:icb_pre_d = 1*treatment3 + 0*icb_pre_d + 0*treatment3:icb_pre_d" +linearHypothesis(r_s1_hum_antdint, hypo_02) + +## Standard deviation of the outcome +0.15 / 0.27 +0.10 / 0.27 + +## Preparing data for the study 1 marginal effects plots +# Regression models +reg_med_me1 <- lm(emp ~ treatment1 + icb_pre + treatment2 + treatment3 + + treatment2 * icb_pre + treatment3 * icb_pre + + treatment1 * icb_pre, + stud01) +reg_med_me2 <- lm(emp ~ treatment2 + icb_pre + treatment1 + treatment3 + + treatment1 * icb_pre + treatment3 * icb_pre + + treatment2 * icb_pre, + stud01) +reg_med_me3 <- lm(emp ~ treatment3 + icb_pre + treatment2 + treatment1 + + treatment2 * icb_pre + treatment1 * icb_pre + + treatment3 * icb_pre, + stud01) +## ME numbers +dat1 <- TwowayME.f(reg_med_me1, stud01$treatment1, stud01$icb_pre, 95) +dat2 <- TwowayME.f(reg_med_me2, stud01$treatment2, stud01$icb_pre, 95) +dat3 <- TwowayME.f(reg_med_me3, stud01$treatment3, stud01$icb_pre, 95) +dat1 <- cbind(treatment = "Humanization", dat1) +dat2 <- cbind(treatment = "Information", dat2) +dat3 <- cbind(treatment = "Combined", dat3) +plot_data1 <- rbind(dat1, dat2, dat3) +## Rug plot numbers +dat1 <- cbind(treatment = "Humanization", + icb_pre = as.numeric(reg_med_me1$model$icb_pre), + conb = as.numeric(0)) +dat2 <- cbind(treatment = "Information", + icb_pre = as.numeric(reg_med_me2$model$icb_pre), + conb = as.numeric(0)) +dat3 <- cbind(treatment = "Combined", + icb_pre = as.numeric(reg_med_me3$model$icb_pre), + conb = as.numeric(0)) +plot_data2 <- data.frame(rbind(dat1, dat2, dat3)) +plot_data2$icb_pre <- as.numeric(as.character(plot_data2$icb_pre)) +plot_data2$conb <- as.numeric(as.character(plot_data2$conb)) + +## Plot of study 1 marginal effects +set.seed(33333) #for rug plot +p <- ggplot(plot_data1, aes(x = Znew)) + + geom_line(aes(y = conb)) + + geom_line(aes(y = upper), linetype = "dashed") + + geom_line(aes(y = lower), linetype = "dashed") + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_rug(data = plot_data2, aes(x = icb_pre, y = conb), sides = "b", + position = position_jitter(width = 0.05, height = 0.001), + alpha = 0.05) + + xlab("Outgroup Antipathy") + + ylab("Marginal Effects of Treatment\non Empathic Concern") + + facet_wrap(~ treatment) +ggsave("FiguresTables/MEplots.png", p, width = 6, height = 3) + +## Main study 1 regression model for empathy ~ treatments * antipathy +r_s1_emp_antdint <- lm(emp ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) +## This corresponds to the difference in means tests below + +## Preparing data for the empathy gap plot +labs01 <- c("Low", "High") +labs02 <- c("Control", "Information", "Humanization", + "Combined") +p_data <- stud01[, list(emp = mean(emp, na.rm = TRUE), + se = sd(emp, na.rm = TRUE) / + sqrt(sum(!is.na(emp)))), + by = list(icb_pre_d, treatment)] +p_data <- p_data[!is.na(icb_pre_d)] +p_data[, icb_pre_d := plyr::mapvalues(icb_pre_d, 0:1, labs01)] +p_data[, icb_pre_d := factor(icb_pre_d, labs01, labs01)] +p_data[, treatment := plyr::mapvalues(treatment, c(4, 2, 1, 3), labs02)] +p_data[, treatment := factor(treatment, labs02, labs02)] +my_q <- qnorm(.975) +p_data_diff <- cbind( + "Condition" = c("Control", "Information", "Humanization", "Combined"), + "Diff" = c(p_data[1,3]-p_data[4,3], + p_data[7,3]-p_data[8,3], + p_data[2,3]-p_data[6,3], + p_data[3,3]-p_data[5,3]), + "seDiff" = c(sqrt(sd(stud01$emp[stud01$treatment==4 & stud01$icb_pre_d==0],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==4 & stud01$icb_pre_d==0])) + sd(stud01$emp[stud01$treatment==4 & stud01$icb_pre_d==1],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==4 & stud01$icb_pre_d==1]))), + sqrt(sd(stud01$emp[stud01$treatment==2 & stud01$icb_pre_d==0],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==2 & stud01$icb_pre_d==0])) + sd(stud01$emp[stud01$treatment==2 & stud01$icb_pre_d==1],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==2 & stud01$icb_pre_d==1]))), + sqrt(sd(stud01$emp[stud01$treatment==1 & stud01$icb_pre_d==0],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==1 & stud01$icb_pre_d==0])) + sd(stud01$emp[stud01$treatment==1 & stud01$icb_pre_d==1],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==1 & stud01$icb_pre_d==1]))), + sqrt(sd(stud01$emp[stud01$treatment==3 & stud01$icb_pre_d==0],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==3 & stud01$icb_pre_d==0])) + sd(stud01$emp[stud01$treatment==3 & stud01$icb_pre_d==1],na.rm=T)^2/sum(!is.na(stud01$emp[stud01$treatment==3 & stud01$icb_pre_d==1]))) + ) +) +p_data_diff.df <- as.data.frame(p_data_diff) +p_data_diff.df <- transform(p_data_diff.df, Condition = unlist(Condition)) +p_data_diff.df$Condition <- as.factor(p_data_diff.df$Condition) +p_data_diff.df$Condition <- factor(p_data_diff.df$Condition, as.character(p_data_diff.df$Condition)) +p_data_diff.df$Diff <- as.numeric(p_data_diff.df$Diff) +p_data_diff.df$seDiff <- as.numeric(p_data_diff.df$seDiff) +my_q <- qnorm(.975) + +## Plotting the empathy gap for study 1 +p <- ggplot(p_data_diff.df, aes(y = Diff, x = Condition, ymin = Diff - seDiff * my_q, ymax = Diff + seDiff * my_q)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line(aes(group=1)) + + geom_point(size = 3, fill = "white") + + labs(x = "Treatment", y = "Empathy Gap") +ggsave("FiguresTables/empdiff01.png", p, width = 5.25, height = 3) + +## Standard deviation of empathy difference +sd(stud01$emp, na.rm = TRUE) +## 0.28 +0.55 / 0.28 +0.18 / 0.28 + +######################################## +#### Section: Changing Hearts, Study 2 +######################################## + +## Preparing data for pre-post photo humanization results +labs01 <- c("Low", "High") +labs02 <- c("Pre-Photo", "Post-Photo") +p_data <- stud02[, list(hum01 = mean(pre_hum_measure, na.rm = TRUE), + se01 = sd(pre_hum_measure, na.rm = TRUE) / + sqrt(sum(!is.na(pre_hum_measure))), + hum02 = mean(post_hum_measure, na.rm = TRUE), + se02 = sd(post_hum_measure, na.rm = TRUE) / + sqrt(sum(!is.na(post_hum_measure)))), + by = list(hi_icb)] +p_data <- p_data[!is.na(hi_icb)] +p_data <- melt(p_data, id.vars = "hi_icb") +p_data <- as.data.table(p_data) +p_data[, wave := gsub(".+0(\\d)", "\\1", variable)] +p_data[, variable := gsub("\\d+", "", variable)] +p_data <- as.data.table(dcast(p_data, hi_icb + wave ~ variable)) +p_data[, hi_icb := plyr::mapvalues(hi_icb, 0:1, labs01)] +p_data[, hi_icb := factor(hi_icb, labs01, labs01)] +p_data[, wave := plyr::mapvalues(wave, 1:2, labs02)] +p_data[, wave := factor(wave, labs02, labs02)] +my_q <- qnorm(.975) + +## Plotting the humanization pre/post photo +p <- ggplot(p_data, aes(y = hum, x = wave, ymin = hum - se * my_q, + ymax = hum + se * my_q, shape = hi_icb, group = hi_icb)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line() + + geom_point(size = 3, fill = "white") + + scale_shape_manual("Outgroup Antipathy", values = 21:22) + + labs(x = "Wave", y = "Humanization Level", linetype = "Outgroup Antipathy") +tab_s02_hum <- p_data +ggsave("FiguresTables/hum02.png", p, width = 5.5, height = 3) + +## Statistical test for p-value for responding to images +with(stud02[hi_icb == 0], + t.test(pre_hum_measure, post_hum_measure, paired = TRUE)) +with(stud02[hi_icb == 1], + t.test(pre_hum_measure, post_hum_measure, paired = TRUE)) + +## Preparing data for figure showing empathy by condition and antipathy +labs01 <- c("Low", "High") +labs02 <- c("Legal Immigrants", "Illegal Immigrants") +p_data <- stud02[, list(emp = mean(emp_index01, na.rm = TRUE), + se = sd(emp_index01, na.rm = TRUE) / + sqrt(sum(!is.na(emp_index01)))), + by = list(hi_icb, condition)] +p_data <- p_data[!is.na(hi_icb)] +p_data <- p_data[!is.na(condition)] +p_data[, hi_icb := plyr::mapvalues(hi_icb, 0:1, labs01)] +p_data[, hi_icb := factor(hi_icb, labs01, labs01)] +p_data[, condition := plyr::mapvalues(condition, 0:1, labs02)] +p_data[, condition := factor(condition, labs02, labs02)] +my_q <- qnorm(.975) + +## Plotting figure of empathy by condition and antipathy +p1 <- ggplot(p_data, aes(y = emp, x = condition, ymin = emp - se * my_q, + ymax = emp + se * my_q, shape = hi_icb, group = hi_icb)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line() + + geom_point(size = 3, fill = "white") + + scale_shape_manual("Outgroup\nAntipathy", values = 21:22) + + labs(x = "", y = "Self-reported Empathic Concern", + linetype = "Outgroup Antipathy") +tab_s02_emp <- p_data + +## Preparing data for study 2 empathy gap figure +p_data_diff <- cbind( + "Condition" = c("Legal Immigrants", "Illegal Immigrants"), + "Diff" = c(p_data[hi_icb == "Low" & condition == "Legal Immigrants", 3] - p_data[hi_icb == "High" & condition == "Legal Immigrants", 3], + p_data[hi_icb == "Low" & condition == "Illegal Immigrants", 3] - p_data[hi_icb == "High" & condition == "Illegal Immigrants", 3] + ), + "seDiff" = c(sqrt(sd(stud02$emp_index01[stud02$condition==0 & stud02$hi_icb==0],na.rm=T)^2/sum(!is.na(stud02$emp_index01[stud02$condition==0 & stud02$hi_icb==0])) + sd(stud02$emp_index01[stud02$condition==0 & stud02$hi_icb==1],na.rm=T)^2/sum(!is.na(stud02$emp_index01[stud02$condition==0 & stud02$hi_icb==1]))), + sqrt(sd(stud02$emp_index01[stud02$condition==1 & stud02$hi_icb==0],na.rm=T)^2/sum(!is.na(stud02$emp_index01[stud02$condition==1 & stud02$hi_icb==0])) + sd(stud02$emp_index01[stud02$condition==1 & stud02$hi_icb==1],na.rm=T)^2/sum(!is.na(stud02$emp_index01[stud02$condition==1 & stud02$hi_icb==1]))) + ) +) +p_data_diff.df <- as.data.frame(p_data_diff) +p_data_diff.df <- transform(p_data_diff.df, Condition = unlist(Condition)) +p_data_diff.df$Condition <- as.factor(p_data_diff.df$Condition) +p_data_diff.df$Condition <- factor(p_data_diff.df$Condition, as.character(p_data_diff.df$Condition)) +p_data_diff.df$Diff <- as.numeric(p_data_diff.df$Diff) +p_data_diff.df$seDiff <- as.numeric(p_data_diff.df$seDiff) +my_q <- qnorm(.975) + +## Plotting empathy gap figure for study 2 +p2 <- ggplot(p_data_diff.df, aes(y = Diff, x = Condition, ymin = Diff - seDiff * my_q, ymax = Diff + seDiff * my_q)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line(aes(group=1)) + + geom_point(size = 3, fill = "white") + + labs(x = "Treatment", y = "Empathy Gap" + ) +tab_s02_emp <- p_data_diff.df + +## Creating a figure with both the empathy by condition/antipathy and empathy +## gap figures are on the same plot +pFinal <- plot_grid(p1, p2, align = "v", axis = "lr", ncol = 1) +ggsave("FiguresTables/s02_empplusdiff.png", pFinal, height = 6, width = 5.5) + +## Statistical test for difference in differences test +r_s2_emp_antdint <- lm(emp_index01 ~ condition * hi_icb, stud02) +summary(r_s2_emp_antdint) + +## Coefficient of interest is on condition:hi_icb, which is equal to the +## difference between the point estimates in the empathy gap figure + +######################################## +#### Section: Dissonance as a Mechanism +######################################## + +## Preparing data for the plot of study 2 dissonance by antipathy/condition +labs01 <- c("Low", "High") +labs02 <- c("Legal Immigrants", "Illegal Immigrants") + +p_data <- stud02[, list(diss = mean(diss_measure, na.rm = TRUE), + se = sd(diss_measure, na.rm = TRUE) / + sqrt(sum(!is.na(diss_measure)))), + by = list(hi_icb, condition)] +p_data <- p_data[!is.na(hi_icb)] +p_data[, hi_icb := plyr::mapvalues(hi_icb, 0:1, labs01)] +p_data[, hi_icb := factor(hi_icb, labs01, labs01)] +p_data[, condition := plyr::mapvalues(condition, 0:1, labs02)] +p_data[, condition := factor(condition, labs02, labs02)] +my_q <- qnorm(.975) +p_data <- p_data[!is.na(condition)] + +## Plot of study 2 dissonance by antipathy/condition +ggplot(p_data, aes(y = diss, x = condition, ymin = diss - se * my_q, + ymax = diss + se * my_q, shape = hi_icb, group = hi_icb)) + + geom_errorbar(colour = "black", width = 0.1) + + geom_line() + + geom_point(size = 3, fill = "white") + + scale_shape_manual("Outgroup Antipathy", values = 21:22) + + labs(x = "Experimental Condition", y = "Self-reported Dissonance", + linetype = "Outgroup Antipathy") +ggsave("FiguresTables/s02_diss.png", height = 3, width = 5.5) +tab_s02_diss <- p_data + +## Regression model for statistical tests +r_s2_diss_antdint <- lm(diss_measure ~ condition * hi_icb, stud02) +summary(r_s2_diss_antdint) + +## For first statistical test mentioned, of high antipathy vs low antipathy in +## the legal treatment, see the coefficient on "hi_icb" + +## For the second statistical test mentioned, dissonance change between +## treatments for low antipathy, see the coefficient on "condition" + +## For the third statistical test, we are testing the hypothesis that the +## increase in dissonance between treatments is larger for those with high +## antipathy, which corresponds to the coefficient on the interaction term, +## i.e. "condition:hi_icb" + +######################################## +#### Section: Changing Minds about Policy +######################################## + +## Study 1 +r_s1_harm <- lm(harm ~ treatment1 + treatment2 + treatment3, stud01) +r_s1_harm_antcint <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre + + treatment1:icb_pre + treatment2:icb_pre + + treatment3:icb_pre, + stud01) +sink("FiguresTables/r_s1_harm_antcint.tex") +apsrtable(r_s1_harm, r_s1_harm_antcint, + digits = 2, + align = "c", + model.names = c("(1)", "(2)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Standard deviation of the outcome +sd(stud01$harm, na.rm = TRUE) +## 0.22 +0.01 / 0.22 +0.03 / 0.22 + +## Study 2 +r_s2_harm <- lm(policy_harm ~ condition, stud02) +r_s2_harm_antcint <- lm(policy_harm ~ condition * icb_measure, stud02) +sink("FiguresTables/r_s2_harm_antcint.tex") +apsrtable(r_s2_harm, r_s2_harm_antcint, + digits = 2, + align = "c", + model.names = c("(1)", "(2)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Standard deviation of the outcome +sd(stud02$policy_harm, na.rm = TRUE) +## 0.23 + +################################################################################ +##### Appendix +################################################################################ + +######################################## +#### Survey Administration and Sampling Details +######################################## + +## Voters numbers +invited <- 5513 +completed <- nrow(dat_2[voter_dummy == 1 & finished == 1, ]) +completed +responserate <- format_dec(completed / invited * 100) +responserate +analysis <- format_int(nrow(stud01[voter_dummy == 1, ])) +analysis + +## Activist numbers +invited <- 2517 + 25711 +invited +completed <- nrow(dat_2[activist_dummy == 1 & finished == 1, ]) +completed +responserate <- format_dec(completed / invited * 100) +responserate +analysis <- format_int(nrow(stud01[activist_dummy == 1, ])) +analysis + +## Elected officials numbers +invited <- 1714 +completed <- nrow(dat_2[elect_dummy == 1 & finished == 1, ]) +completed +responserate <- format_dec(completed / invited * 100) +responserate +analysis <- format_int(nrow(stud01[elect_dummy == 1, ])) +analysis + +## Total number +total <- format_int(nrow(stud01)) + +######################################## +#### Survey Measures - Measure of Outgroup Antipathy +######################################## + +## Getting plot data ready for the figures +p_data_1 <- stud01[, "icb_pre", with = FALSE] +p_data_1[, dataset := "Study 1"] +p_data_2 <- as.data.table(stud02[, "icb_measure"]) +p_data_2[, icb_measure := zero_to_one(as.numeric(icb_measure))] +p_data_2[, dataset := "Study 2"] +p_data <- rbindlist(list(p_data_1, p_data_2)) + +## Plot for outgroup antipathy for studies 1 and 2 +p <- ggplot(p_data, aes(x = icb_pre)) + + geom_density(fill = "light gray", adjust = 0.75) + + facet_wrap(~ dataset) + + labs(x = "Outgroup Antipathy", y = "Density") +ggsave("FiguresTables/density_icbs.png", p, width = 6.25, height = 3) + +## N-sizes quoted in the figure for studies 1 and 2 +table(is.na(p_data[dataset == "Study 1"]$icb_pre)) +table(is.na(p_data[dataset == "Study 2"]$icb_pre)) + +######################################## +#### Validation of Outgroup Antipathy Measure +######################################## + +## Uses the pilot data + +## Histograms +hist(predf$antipathy) +hist(predf$hisptherm) +hist(predf$ethno) +hist(predf$sdo) +hist(predf$auth) + +##To check for correlations: +cor(predf$antipathy,predf$hisptherm,use="complete.obs") +cor(predf$antipathy,predf$ethno,use="complete.obs") +cor(predf$antipathy,predf$sdo,use="complete.obs") +cor(predf$antipathy,predf$auth,use="complete.obs") + +## Correlation Matrix +var_names <- c("antipathy", "hisptherm", "ethno", "sdo", "auth") +var_labs <- c("Outgroup Antipathy", "Latino Feeling Therm.", "Ethnocentrism", + "SDO", "Authoritarianism") +predf <- as.data.frame(predf) +mymat <- cor(predf[, var_names], use = "complete.obs") +colnames(mymat) <- rownames(mymat) <- var_labs +stargazer(mymat, type = "latex", digits = 2, float = FALSE, + out = "FiguresTables/corrmat_pretest.tex") + +var_labs <- c("Outgroup\nAntipathy", "Latino Feeling\nThermometer", + "Ethnocentrism", "SDO", "Authoritarianism") +myplot_hex <- function (data, mapping, ...) { + p <- ggplot(data = data, mapping = mapping) + stat_binhex(..., bins = 15) + + scale_fill_gradientn(colours = c("light gray", "black")) + p +} +myplot_2dhist <- function (data, mapping, ...) { + p <- ggplot(data = data, mapping = mapping) + geom_bin2d(...) + + scale_fill_gradient(low = "light gray", high = "black") + p +} +ggpairs(predf[, var_names], columnLabels = var_labs, + lower = list(continuous = myplot_2dhist)) +ggsave("FiguresTables/corrmat_pretest.png", width = 8.5, height = 5.5) + +######################################## +#### Factor Analysis +######################################## + +## Study 1 factor analysis +tmp <- na.omit(subset(stud01, + select = c("law_english", "law_tuition", "law_welfare", + "law_hire", "immig_opinion_reverse", "arizona_law", + "st8_hb497", "st8_hb116", "st8_hb466", + "st8_hb469"))) +princomp_fit <- princomp(tmp, cor = 2) +explore_fit <- factanal(tmp, 2, rotation = "varimax") +explore_load <- explore_fit$loadings[, 1:2] +plot_fa <- data.table( + "Variables" = c("Law (English)", "Law (Tuition)", "Law (Welfare)", + "Law (Hire)", "Immigration Opinion", "Arizona Law", "State Bill Harm", "State Bill Help 1", + "State Bill Help 2", "State Bill Help 3"), + "Factor1" = as.data.table(explore_load)$Factor1, + "Factor2" = as.data.table(explore_load)$Factor2 +) +plot_pc <- data.table( + "Labels" = ordered(names(princomp_fit$sdev), + levels = names(princomp_fit$sdev)), + "var_expl" = (princomp_fit$sdev)^2 / sum((princomp_fit$sdev)^2) +) + +## Plot 1 +my_size <- 2.9 +p1 <- ggplot(plot_fa, aes(x = Factor1, y = Factor2, label = Variables, + family = "serif")) + + geom_point() + + geom_text(data = plot_fa[c(1, 9)], size = my_size, hjust = 1.1, + vjust = -0.1) + + geom_text(data = plot_fa[c(2, 5, 8)], size = my_size, hjust = -0.1, + vjust = 1.1) + + geom_text(data = plot_fa[c(4)], size = my_size, hjust = 1.1, vjust = 1.1) + + geom_text(data = plot_fa[c(3, 6:7, 10)], size = my_size, hjust = -0.1, + vjust = -0.1) + + geom_vline(xintercept = 0, linetype = "dashed", size = 0.25) + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_circle(aes(x0 = 0.7, y0 = -0.24, r = 0.28), size = 0.25, + inherit.aes = FALSE) + + xlab("Factor 1") + + ylab("Factor 2") + + xlim(-0.33, 0.98) + + ylim(-0.55, 0.75) +plot(p1) + +## Plot 2 +p2 <- ggplot(plot_pc, aes(x = Labels, y = var_expl, group = 1)) + + geom_line() + + geom_point() + + xlab("Components") + + ylab("Variance Explained") + + scale_x_discrete(labels = abbreviate) +plot(p2) + +## Final Plot +pFinal <- plot_grid(p1, p2, align = "v", axis = "lr", ncol = 1) +ggsave("FiguresTables/s1_factoranalysis.png", width = 6.25, height = 8) + +## Study 2 factor analysis +tmp <- na.omit(subset(stud02, + select = c("pol1a", "pol1b", "pol2a", "pol2b", "pol2c", + "pol2d", "pol2e", "pol3", "pol4a", "pol4b", + "pol5a", "pol5b", "pol5c", "pol5d"))) +tmp[, pol1b := abs(pol1b - 6)] +tmp[, pol3 := abs(pol3 - 5)] +princomp_fit <- princomp(tmp, cor = 2) +explore_fit <- factanal(tmp, 2, rotation = "varimax") +explore_load <- explore_fit$loadings[, 1:2] +plot_fa <- data.table( + "Variables" = c("Aid Legal", "Aid Illegal", "Don't Give Back", + "See Themselves American", "Not Bothered To Learn", + "Assimilate Well", "Should Try Harder", "Immigration Opinion", + "Taking Resources", "Should Deny Rights", + "Law (English)", "Law (Tuition)", "Law (Welfare)", + "Law (Hire)"), + "Factor1" = as.data.table(explore_load)$Factor1, + "Factor2" = as.data.table(explore_load)$Factor2 +) +plot_pc <- data.table( + "Labels" = ordered(names(princomp_fit$sdev), + levels = names(princomp_fit$sdev)), + "var_expl" = (princomp_fit$sdev)^2 / sum((princomp_fit$sdev)^2) +) + +## Plot 1 +my_size <- 2.9 +p1 <- ggplot(plot_fa, aes(x = Factor1, y = Factor2, label = Variables, + family = "serif")) + + geom_point() + + geom_text(data = plot_fa[c(1, 5, 7, 10, 14)], size = my_size, hjust = 1.1, + vjust = -0.1) + + geom_text(data = plot_fa[c(6, 9, 13)], size = my_size, hjust = -0.1, + vjust = 1.1) + + geom_text(data = plot_fa[c(2, 8)], size = my_size, hjust = 1.1, vjust = 1.1) + + geom_text(data = plot_fa[c(12, 3:4, 11)], size = my_size, hjust = -0.1, + vjust = -0.1) + + geom_vline(xintercept = 0, linetype = "dashed", size = 0.25) + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_circle(aes(x0 = 0.625, y0 = 0.3, r = 0.32), size = 0.25, + inherit.aes = FALSE) + + xlab("Factor 1") + + ylab("Factor 2") + + xlim(-0.25, 0.95) + + ylim(-0.55, 0.75) +plot(p1) + +## Plot 2 +p2 <- ggplot(plot_pc, aes(x = Labels, y = var_expl, group = 1)) + + geom_line() + + geom_point() + + xlab("Components") + + ylab("Variance Explained") + + scale_x_discrete(labels = abbreviate) +plot(p2) + +## Final Plot +pFinal <- plot_grid(p1, p2, align = "v", axis = "lr", ncol = 1) +ggsave("FiguresTables/s2_factoranalysis.png", width = 6.25, height = 8) + +######################################## +#### Balance +######################################## + +## A few additional simplified variables +stud01[, student := as.integer(employ == "Student")] +stud01[is.na(employ), student := NA] +stud01[, married := as.integer(marital == "Married")] +stud01[is.na(marital), married := NA] + +myvars1 <- c("treatment", "gendernum", "age", "student", "partyidnum", + "incomenum", "educationnum", "married") + +tmpdat <- stud01[treatment %in% c(1, 4), ..myvars1] +tmpdat[, treatment := as.logical(treatment == 1)] +baltest_1a <- xBalance(as.formula(paste("treatment ~ ", paste0(myvars1[-1], collapse = " + "))), + data = tmpdat, + report = c("chisquare.test", "std.diffs")) + +tmpdat <- stud01[treatment %in% c(2, 4), ..myvars1] +tmpdat[, treatment := as.logical(treatment == 2)] +baltest_1b <- xBalance(as.formula(paste("treatment ~ ", paste0(myvars1[-1], collapse = " + "))), + data = tmpdat, + report = c("chisquare.test", "std.diffs")) + +tmpdat <- stud01[treatment %in% c(3, 4), ..myvars1] +tmpdat[, treatment := as.logical(treatment == 3)] +baltest_1c <- xBalance(as.formula(paste("treatment ~ ", paste0(myvars1[-1], collapse = " + "))), + data = tmpdat, + report = c("chisquare.test", "std.diffs")) + +myvars2 <- c("condition", "gender", "age", "w1_4", "partyid") + +tmpdat <- stud02[, ..myvars2] +tmpdat[, condition := as.logical(condition == 1)] +tmpdat <- tmpdat[!is.na(condition)] +baltest_2a <- xBalance(as.formula(paste("condition ~ ", paste0(myvars2[-1], collapse = " + "))), + data = tmpdat, + report = c("chisquare.test", "std.diffs")) + +myvars1_labs <- c("Gender", "Age", "Student", "Party ID", "Income", "Education", + "Married") +myvars1_labs <- c(myvars1_labs, paste(myvars1_labs, "(NA)")) + +baldat_1 <- data.table( + var = rep(myvars1_labs, 3), + treat = rep(c("Humanization", "Information", "Combined"), + each = length(myvars1_labs)), + balstat = c(baltest_1a$results[1:14, 1, ], + baltest_1b$results[1:14, 1, ], + baltest_1c$results[1:14, 1, ]) +) +baldat_1[, treat := factor(treat, c("Humanization", "Information", "Combined"))] +baldat_1[, var := factor(var, rev(myvars1_labs))] + +## Overall statistics (reported in the table) +baltest_1a$overall +baltest_1b$overall +baltest_1c$overall + +myvars2_labs <- c("Gender", "Age", "Student", "Party ID", "Gender (NA)", + "Age (NA)", "Student (NA)", "Party ID (NA)") + +baldat_2 <- data.table( + var = myvars2_labs, + treat = rep("Illegal Condition", length(myvars2_labs)), + balstat = baltest_2a$results[1:8, 1, ] +) +baldat_2[, var := factor(var, rev(myvars2_labs))] + +## Overall statistics (reported in the table) +baltest_2a$overall + +balplot_1 <- ggplot(baldat_1, aes(x = balstat, y = var, color = treat)) + + geom_vline(xintercept = 0, linetype = 2, size = 0.5) + + geom_point() + + coord_cartesian(xlim = c(-0.125, 0.1)) + + labs(y = "Covariate", x = "Standardized Difference", colour = "Treatment", + title = "Study 1") + +balplot_2 <- ggplot(baldat_2, aes(x = balstat, y = var)) + + geom_vline(xintercept = 0, linetype = 2, size = 0.5) + + geom_point() + + coord_cartesian(xlim = c(-0.125, 0.1)) + + labs(y = "Covariate", x = "Standardized Difference", + title = "Study 2") + +balplot_final <- plot_grid(balplot_1, balplot_2, ncol = 1, + rel_heights = c(1.6, 1), align = "v", axis = "lr") +ggsave("FiguresTables/balance.png", balplot_final, width = 5, height = 7) + +######################################## +#### Descriptive Statistics +######################################## + +## Use this function to unstandardize some measures +rescale_func <- function(x, mymin, mymax) { + x * (mymax - mymin) + mymin +} + +## N-size +dim(stud01) +dim(stud02) + +## Gender +table(stud01$gender, useNA = "ifany") +18/3498 +table(stud02$gender, useNA = "ifany") +table(stud02$gender, useNA = "ifany") / 1982 +5/1982 + +## Age +table(stud01$age, useNA = "ifany") +mean(stud01$age, na.rm = TRUE) +59/3498 +table(stud02$age, useNA = "ifany") +mean(stud02$age, na.rm = TRUE) +7/1982 + +## Student +table(stud01$employ, useNA = "ifany") +table(stud01$employ, useNA = "ifany") / 3498 +table(stud02$w1_4, useNA = "ifany") +table(stud02$w1_4, useNA = "ifany") / 1982 + +## Party ID +table(rescale_func(stud01$partyidnum, 1, 7), useNA = "ifany") +mean(rescale_func(stud01$partyidnum, 1, 7), na.rm = TRUE) +173/3498 +table(rescale_func(stud02$partyid, 1, 7), useNA = "ifany") +mean(rescale_func(stud02$partyid, 1, 7), na.rm = TRUE) +14/1982 + +## Education +table(stud01$education, useNA = "ifany") +table(stud01$education, useNA = "ifany") / 3498 +## No data in study 2 + +## Income +table(stud01$income, useNA = "ifany") +table(stud01$income, useNA = "ifany") / 3498 +## No data in study 2 + +## Marital Status +table(stud01$marital, useNA = "ifany") +table(stud01$marital, useNA = "ifany") / 3498 +## No data on marital status in study 2 + +######################################## +#### Supporting Tables, Changing Hearts, Study 1 +######################################## + +## Study 1 humanization, supporting table for figure 2 +r_s1_hum_treat <- lm(possec ~ treatment1 + treatment2 + treatment3, + data = stud01) +r_s1_hum_antdint <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) +r_s1_hum_antdint_cont <- lm(possec ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + + treatment2:icb_pre_d + treatment3:icb_pre_d + + gendernum + age + partyidnum, + data = stud01) +sink("FiguresTables/r_s1_hum_antdint_cont.tex") +apsrtable(r_s1_hum_treat, r_s1_hum_antdint, r_s1_hum_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + + +## Study 1 empathy, supporting table for figure 3 +r_s1_emp_treat <- lm(emp ~ treatment1 + treatment2 + treatment3, + stud01) +r_s1_emp_antcint <- lm(emp ~ treatment1 + treatment2 + treatment3 + icb_pre + + treatment1:icb_pre + treatment2:icb_pre + + treatment3:icb_pre, + stud01) +r_s1_emp_antcint_cont <- lm(emp ~ treatment1 + treatment2 + treatment3 + + icb_pre + treatment1:icb_pre + + treatment2:icb_pre + treatment3:icb_pre + + gendernum + age + partyidnum, + stud01) +sink("FiguresTables/r_s1_emp_antcint_cont.tex") +apsrtable(r_s1_emp_treat, r_s1_emp_antcint, r_s1_emp_antcint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Main study 1 regression model for empathy ~ treatments * antipathy +r_s1_emp_antdint <- lm(emp ~ treatment1 + treatment2 + treatment3 + icb_pre_d + + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + data = stud01) +r_s1_emp_antdint_cont <- lm(emp ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + + treatment2:icb_pre_d + treatment3:icb_pre_d + + gendernum + age + partyidnum, + data = stud01) +sink("FiguresTables/r_s1_emp_antdint_cont.tex") +apsrtable(r_s1_emp_treat, r_s1_emp_antdint, r_s1_emp_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Supporting Tables, Changing Hearts, Study 2 +######################################## + +r_s2_emp_treat <- lm(emp_index01 ~ condition, stud02) +r_s2_emp_antdint <- lm(emp_index01 ~ condition * hi_icb, stud02) +r_s2_emp_antdint_cont <- lm(emp_index01 ~ condition * hi_icb + gender + age + + partyid, + data = stud02) +sink("FiguresTables/r_s2_emp_antdint_cont.tex") +apsrtable(r_s2_emp_treat, r_s2_emp_antdint, r_s2_emp_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Supporting Tables, Dissonance as a Mechanism +######################################## + +r_s2_diss_treat <- lm(diss_measure ~ condition, stud02) +r_s2_diss_antdint <- lm(diss_measure ~ condition * hi_icb, stud02) +r_s2_diss_antdint_cont <- lm(diss_measure ~ condition * hi_icb + gender + + age + partyid, + stud02) +sink("FiguresTables/r_s2_diss_antdint_cont.tex") +apsrtable(r_s2_diss_treat, r_s2_diss_antdint, r_s2_diss_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Supporting Tables, Changing Minds about Policy +######################################## + +## Study 1 +r_s1_harm <- lm(harm ~ treatment1 + treatment2 + treatment3, stud01) +r_s1_harm_antdint <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre_d + + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_harm_antdint_cont <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre_d + + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d + gendernum + age + + partyidnum, + stud01) +sink("FiguresTables/r_s1_harm_antdint_cont.tex") +apsrtable(r_s1_harm, r_s1_harm_antdint, r_s1_harm_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Study 2 +r_s2_harm <- lm(policy_harm ~ condition, stud02) +r_s2_harm_antdint <- lm(policy_harm ~ condition * hi_icb, stud02) +r_s2_harm_antdint_cont <- lm(policy_harm ~ condition * hi_icb + gender + age + + partyid, + stud02) +sink("FiguresTables/r_s2_harm_antdint_cont.tex") +apsrtable(r_s2_harm, r_s2_harm_antdint, r_s2_harm_antdint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Continuous + +## Study 1 +r_s1_harm <- lm(harm ~ treatment1 + treatment2 + treatment3, stud01) +r_s1_harm_antcint <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre + + treatment1:icb_pre + treatment2:icb_pre + + treatment3:icb_pre, + stud01) +r_s1_harm_antcint_cont <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre + + treatment1:icb_pre + treatment2:icb_pre + + treatment3:icb_pre + gendernum + age + partyidnum, + stud01) +sink("FiguresTables/r_s1_harm_antcint_cont.tex") +apsrtable(r_s1_harm, r_s1_harm_antcint, r_s1_harm_antcint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Study 2 +r_s2_harm <- lm(policy_harm ~ condition, stud02) +r_s2_harm_antcint <- lm(policy_harm ~ condition * icb_measure, stud02) +r_s2_harm_antcint_cont <- lm(policy_harm ~ condition * icb_measure + gender + + age + partyid, + stud02) +sink("FiguresTables/r_s2_harm_antcint_cont.tex") +apsrtable(r_s2_harm, r_s2_harm_antcint, r_s2_harm_antcint_cont, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Additional Results: Marginal Effects by Antipathy, Study 2 +######################################## + +## ME regressions +r_s2_me1 <- lm(emp_index01 ~ condition, stud02) +r_s2_me2 <- lm(emp_index01 ~ condition * icb_measure, stud02) +r_s2_me3 <- lm(emp_index01 ~ condition * icb_measure + gender + age + partyid, + stud02) +sink("FiguresTables/r_s2_mereg.tex") +apsrtable(r_s2_me1, r_s2_me2, r_s2_me3, + digits = 2, + align = "c", + model.names = c("(1)", "(2)", "(3)"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy", + "Gender (1 = Female)", "Age", "Party ID (0--1)"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## ME fig +pdat1 <- TwowayME.f(r_s2_me2, stud02$condition, stud02$icb_measure, 95) +pdat2 <- data.frame(icb_measure = stud02$icb_measure, + conb = 0) +p <- ggplot(pdat1, aes(x = Znew)) + + geom_line(aes(y = conb)) + + geom_line(aes(y = upper), linetype = "dashed") + + geom_line(aes(y = lower), linetype = "dashed") + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_rug(data = pdat2, aes(x = icb_measure, y = conb), sides = "b", + position = position_jitter(width = 0.05, height = 0.001), + alpha = 0.05) + + labs(x = "Outgroup Antipathy", y = "Marginal Effects of Treatment\non Empathy") +ggsave("FiguresTables/s2_me.png", p, width = 3, height = 3) + +######################################## +#### Additional Results, by Sub-Population +######################################## + +## Humanization +r_s1_hum_antdint <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) +r_s1_hum_subpop1 <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + voter_dummy == 1) +r_s1_hum_subpop2 <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + delegate_dummy == 1 | caucus_dummy == 1) +r_s1_hum_subpop3 <- lm(possec ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + elect_dummy == 1) +sink("FiguresTables/r_hum_subpop.tex") +apsrtable(r_s1_hum_antdint, r_s1_hum_subpop1, r_s1_hum_subpop2, r_s1_hum_subpop3, + digits = 2, + align = "c", + model.names = c("Everyone", "Voters", "Activists", "Elected Officials"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Antipathy $\\times$ Humanization", + "Antipathy $\\times$ Information", "Antipathy $\\times$ Combined"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note, + "Variables are on a 0--1 scale")) +sink() + +## Empathy +r_s1_emp_antdint <- lm(emp ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) +r_s1_emp_subpop1 <- lm(emp ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + voter_dummy == 1) +r_s1_emp_subpop2 <- lm(emp ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + delegate_dummy == 1 | caucus_dummy == 1) +r_s1_emp_subpop3 <- lm(emp ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + elect_dummy == 1) +sink("FiguresTables/r_emp_subpop.tex") +apsrtable(r_s1_emp_antdint, r_s1_emp_subpop1, r_s1_emp_subpop2, r_s1_emp_subpop3, + digits = 2, + align = "c", + model.names = c("Everyone", "Voters", "Activists", "Elected Officials"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Antipathy $\\times$ Humanization", + "Antipathy $\\times$ Information", "Antipathy $\\times$ Combined"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note, + "Variables are on a 0--1 scale")) +sink() + +## Empathy +r_s1_harm_antdint <- lm(harm ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01) +r_s1_harm_subpop1 <- lm(harm ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + voter_dummy == 1) +r_s1_harm_subpop2 <- lm(harm ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + delegate_dummy == 1 | caucus_dummy == 1) +r_s1_harm_subpop3 <- lm(harm ~ treatment1 + treatment2 + treatment3 + + treatment1 * icb_pre_d + treatment2 * icb_pre_d + + treatment3 * icb_pre_d, + data = stud01, + elect_dummy == 1) +sink("FiguresTables/r_harm_subpop.tex") +apsrtable(r_s1_harm_antdint, r_s1_harm_subpop1, r_s1_harm_subpop2, r_s1_harm_subpop3, + digits = 2, + align = "c", + model.names = c("Everyone", "Voters", "Activists", "Elected Officials"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Antipathy $\\times$ Humanization", + "Antipathy $\\times$ Information", "Antipathy $\\times$ Combined"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note, + "Variables are on a 0--1 scale")) +sink() + +######################################## +#### Additional Results, Interaction with Political Ideology or Party ID +######################################## + +## Study 1 Interaction, ideology (both empathy and policy outcomes) +reg_med_me1 <- lm(emp ~ treatment1 + ideologynum + treatment2 + treatment3 + + treatment2 * ideologynum + treatment3 * ideologynum + + treatment1 * ideologynum, + stud01) +reg_med_me2 <- lm(emp ~ treatment2 + ideologynum + treatment1 + treatment3 + + treatment1 * ideologynum + treatment3 * ideologynum + + treatment2 * ideologynum, + stud01) +reg_med_me3 <- lm(emp ~ treatment3 + ideologynum + treatment2 + treatment1 + + treatment2 * ideologynum + treatment1 * ideologynum + + treatment3 * ideologynum, + stud01) +reg_med_me1harm <- lm(harm ~ treatment1 + ideologynum + treatment2 + treatment3 + + treatment2 * ideologynum + treatment3 * ideologynum + + treatment1 * ideologynum, + stud01) +reg_med_me2harm <- lm(harm ~ treatment2 + ideologynum + treatment1 + treatment3 + + treatment1 * ideologynum + treatment3 * ideologynum + + treatment2 * ideologynum, + stud01) +reg_med_me3harm <- lm(harm ~ treatment3 + ideologynum + treatment2 + treatment1 + + treatment2 * ideologynum + treatment1 * ideologynum + + treatment3 * ideologynum, + stud01) +dat1 <- TwowayME.f(reg_med_me1, stud01$treatment1, stud01$ideologynum, 95) +dat2 <- TwowayME.f(reg_med_me2, stud01$treatment2, stud01$ideologynum, 95) +dat3 <- TwowayME.f(reg_med_me3, stud01$treatment3, stud01$ideologynum, 95) +dat1harm <- TwowayME.f(reg_med_me1harm, stud01$treatment1, stud01$ideologynum, 95) +dat2harm <- TwowayME.f(reg_med_me2harm, stud01$treatment1, stud01$ideologynum, 95) +dat3harm <- TwowayME.f(reg_med_me3harm, stud01$treatment1, stud01$ideologynum, 95) +dat1 <- cbind(outcome = "Empathic Concern", treatment = "Humanization", dat1) +dat2 <- cbind(outcome = "Empathic Concern", treatment = "Information", dat2) +dat3 <- cbind(outcome = "Empathic Concern", treatment = "Combined", dat3) +dat1harm <- cbind(outcome = "Policy Harm", treatment = "Humanization", dat1harm) +dat2harm <- cbind(outcome = "Policy Harm", treatment = "Information", dat2harm) +dat3harm <- cbind(outcome = "Policy Harm", treatment = "Combined", dat3harm) +plot_data1 <- rbind(dat1, dat2, dat3, dat1harm, dat2harm, dat3harm) +dat1 <- cbind(outcome = "Empathic Concern", treatment = "Humanization", + ideologynum = as.numeric(reg_med_me1$model$ideologynum), + conb = as.numeric(0)) +dat2 <- cbind(outcome = "Empathic Concern", treatment = "Information", + ideologynum = as.numeric(reg_med_me2$model$ideologynum), + conb = as.numeric(0)) +dat3 <- cbind(outcome = "Empathic Concern", treatment = "Combined", + ideologynum = as.numeric(reg_med_me3$model$ideologynum), + conb = as.numeric(0)) +dat1harm <- cbind(outcome = "Policy Harm", treatment = "Humanization", + ideologynum = as.numeric(reg_med_me1harm$model$ideologynum), + conb = as.numeric(0)) +dat2harm <- cbind(outcome = "Policy Harm", treatment = "Information", + ideologynum = as.numeric(reg_med_me2harm$model$ideologynum), + conb = as.numeric(0)) +dat3harm <- cbind(outcome = "Policy Harm", treatment = "Combined", + ideologynum = as.numeric(reg_med_me3harm$model$ideologynum), + conb = as.numeric(0)) +plot_data2 <- data.frame(rbind(dat1, dat2, dat3, dat1harm, dat2harm, dat3harm)) +plot_data2$ideologynum <- as.numeric(as.character(plot_data2$ideologynum)) +plot_data2$conb <- as.numeric(as.character(plot_data2$conb)) +p <- ggplot(plot_data1, aes(x = Znew)) + + geom_line(aes(y = conb)) + + geom_line(aes(y = upper), linetype = "dashed") + + geom_line(aes(y = lower), linetype = "dashed") + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_rug(data = plot_data2, aes(x = ideologynum, y = conb), sides = "b", + position = position_jitter(width = 0.05, height = 0.001), + alpha = 0.05) + + xlab("Political Ideology") + + ylab("Marginal Effects of Treatment\non Empathic Concern and Policy Harm") + + facet_grid(outcome ~ treatment) +ggsave("FiguresTables/s1_me_ideology.png", p, width = 6, height = 5) + +## Study 1 Interaction, party id, with empathy and policy as outcomes +reg_med_me1 <- lm(emp ~ treatment1 + partyidnum + treatment2 + treatment3 + + treatment2 * partyidnum + treatment3 * partyidnum + + treatment1 * partyidnum, + stud01) +reg_med_me2 <- lm(emp ~ treatment2 + partyidnum + treatment1 + treatment3 + + treatment1 * partyidnum + treatment3 * partyidnum + + treatment2 * partyidnum, + stud01) +reg_med_me3 <- lm(emp ~ treatment3 + partyidnum + treatment2 + treatment1 + + treatment2 * partyidnum + treatment1 * partyidnum + + treatment3 * partyidnum, + stud01) +reg_med_me1harm <- lm(harm ~ treatment1 + partyidnum + treatment2 + treatment3 + + treatment2 * partyidnum + treatment3 * partyidnum + + treatment1 * partyidnum, + stud01) +reg_med_me2harm <- lm(harm ~ treatment2 + partyidnum + treatment1 + treatment3 + + treatment1 * partyidnum + treatment3 * partyidnum + + treatment2 * partyidnum, + stud01) +reg_med_me3harm <- lm(harm ~ treatment3 + partyidnum + treatment2 + treatment1 + + treatment2 * partyidnum + treatment1 * partyidnum + + treatment3 * partyidnum, + stud01) +dat1 <- TwowayME.f(reg_med_me1, stud01$treatment1, stud01$partyidnum, 95) +dat2 <- TwowayME.f(reg_med_me2, stud01$treatment2, stud01$partyidnum, 95) +dat3 <- TwowayME.f(reg_med_me3, stud01$treatment3, stud01$partyidnum, 95) +dat1harm <- TwowayME.f(reg_med_me1harm, stud01$treatment1, stud01$partyidnum, 95) +dat2harm <- TwowayME.f(reg_med_me2harm, stud01$treatment1, stud01$partyidnum, 95) +dat3harm <- TwowayME.f(reg_med_me3harm, stud01$treatment1, stud01$partyidnum, 95) +dat1 <- cbind(outcome = "Empathic Concern", treatment = "Humanization", dat1) +dat2 <- cbind(outcome = "Empathic Concern", treatment = "Information", dat2) +dat3 <- cbind(outcome = "Empathic Concern", treatment = "Combined", dat3) +dat1harm <- cbind(outcome = "Policy Harm", treatment = "Humanization", dat1harm) +dat2harm <- cbind(outcome = "Policy Harm", treatment = "Information", dat2harm) +dat3harm <- cbind(outcome = "Policy Harm", treatment = "Combined", dat3harm) +plot_data1 <- rbind(dat1, dat2, dat3, dat1harm, dat2harm, dat3harm) +dat1 <- cbind(outcome = "Empathic Concern", treatment = "Humanization", + partyidnum = as.numeric(reg_med_me1$model$partyidnum), + conb = as.numeric(0)) +dat2 <- cbind(outcome = "Empathic Concern", treatment = "Information", + partyidnum = as.numeric(reg_med_me2$model$partyidnum), + conb = as.numeric(0)) +dat3 <- cbind(outcome = "Empathic Concern", treatment = "Combined", + partyidnum = as.numeric(reg_med_me3$model$partyidnum), + conb = as.numeric(0)) +dat1harm <- cbind(outcome = "Policy Harm", treatment = "Humanization", + partyidnum = as.numeric(reg_med_me1harm$model$partyidnum), + conb = as.numeric(0)) +dat2harm <- cbind(outcome = "Policy Harm", treatment = "Information", + partyidnum = as.numeric(reg_med_me2harm$model$partyidnum), + conb = as.numeric(0)) +dat3harm <- cbind(outcome = "Policy Harm", treatment = "Combined", + partyidnum = as.numeric(reg_med_me3harm$model$partyidnum), + conb = as.numeric(0)) +plot_data2 <- data.frame(rbind(dat1, dat2, dat3, dat1harm, dat2harm, dat3harm)) +plot_data2$partyidnum <- as.numeric(as.character(plot_data2$partyidnum)) +plot_data2$conb <- as.numeric(as.character(plot_data2$conb)) +p <- ggplot(plot_data1, aes(x = Znew)) + + geom_line(aes(y = conb)) + + geom_line(aes(y = upper), linetype = "dashed") + + geom_line(aes(y = lower), linetype = "dashed") + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_rug(data = plot_data2, aes(x = partyidnum, y = conb), sides = "b", + position = position_jitter(width = 0.05, height = 0.001), + alpha = 0.05) + + xlab("Party ID") + + ylab("Marginal Effects of Treatment\non Empathic Concern and Policy Harm") + + facet_grid(outcome ~ treatment) +ggsave("FiguresTables/s1_me_partyid.png", p, width = 6, height = 5) + +## Study 2, with empathy and policy outcomes +r_s2_meparty <- lm(emp_index01 ~ condition * partyid, stud02) +r_s2_mepartyharm <- lm(policy_harm ~ condition * partyid, stud02) +pdat1a <- TwowayME.f(r_s2_meparty, stud02$condition, stud02$partyid, 95) +pdat1b <- TwowayME.f(r_s2_mepartyharm, stud02$condition, stud02$partyid, 95) +pdat1 <- rbind(cbind(outcome = "Empathic Concern", pdat1a), + cbind(outcome = "Policy Harm", pdat1b)) +pdat2a <- data.frame(outcome = "Empathic Concern", + partyid = stud02$partyid, + conb = 0) +pdat2b <- data.frame(outcome = "Policy Harm", + partyid = stud02$partyid, + conb = 0) +pdat2 <- data.frame(rbind(pdat2a, pdat2b)) +p <- ggplot(pdat1, aes(x = Znew)) + + geom_line(aes(y = conb)) + + geom_line(aes(y = upper), linetype = "dashed") + + geom_line(aes(y = lower), linetype = "dashed") + + geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + + geom_rug(data = pdat2, aes(x = partyid, y = conb), sides = "b", + position = position_jitter(width = 0.05, height = 0.001), + alpha = 0.05) + + labs(x = "Party ID", y = "Marginal Effects of Treatment\non Empathic Concern and Policy Harm") + + facet_wrap(~ outcome, ncol = 1) +ggsave("FiguresTables/s2_meparty.png", p, width = 3, height = 5) + +######################################## +#### Additional Results, Study 2 3-item antipathy measure +######################################## + +## New antipathy measure +myvars <- paste0("icb", 8:10) +stud02[, icb_alt := psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE)$scores] +stud02[, hi_icb_alt := as.integer(!(icb_alt < 4))] +stud02[is.na(icb_alt), hi_icb_alt := NA] + +myvars <- c("condition", "hi_icb_alt", "emp_index01", "diss_measure", + "policy_harm") +tmpdat <- stud02[, ..myvars] +setnames(tmpdat, "hi_icb_alt", "hi_icb") + +## Study 2 humanization t-test +with(stud02[hi_icb_alt == 0], + t.test(pre_hum_measure, post_hum_measure, paired = TRUE)) +with(stud02[hi_icb_alt == 1], + t.test(pre_hum_measure, post_hum_measure, paired = TRUE)) + +## Study 2 tables +r_s2icbalt_1 <- lm(emp_index01 ~ condition * hi_icb, tmpdat) +r_s2_emp_antdint <- lm(emp_index01 ~ condition * hi_icb, stud02) +sink("FiguresTables/r_s2icbalt_1.tex") +apsrtable(r_s2icbalt_1, r_s2_emp_antdint, + digits = 2, + align = "c", + model.names = c("3-Item", "9-Item"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() +r_s2icbalt_2 <- lm(diss_measure ~ condition * hi_icb, tmpdat) +r_s2_diss_antdint <- lm(diss_measure ~ condition * hi_icb, stud02) +sink("FiguresTables/r_s2icbalt_2.tex") +apsrtable(r_s2icbalt_2, r_s2_diss_antdint, + digits = 2, + align = "c", + model.names = c("3-Item", "9-Item"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() +r_s2icbalt_3 <- lm(policy_harm ~ condition * hi_icb, tmpdat) +r_s2_harm_antdint <- lm(policy_harm ~ condition * hi_icb, stud02) +sink("FiguresTables/r_s2icbalt_3.tex") +apsrtable(r_s2icbalt_3, r_s2_harm_antdint, + digits = 2, + align = "c", + model.names = c("3-Item", "9-Item"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Additional Results, Separate Laws +######################################## + +## Study 1, examining the policy outcomes separately +r_s1_law1_antint <- lm(law_english ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_law2_antint <- lm(law_tuition ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_law3_antint <- lm(law_welfare ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_law4_antint <- lm(law_hire ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_immop_antint <- lm(immig_opinion_reverse ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_ariz_antint <- lm(arizona_law ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_arizlike_antint <- lm(st8_hb497 ~ treatment1 + treatment2 + treatment3 + + icb_pre_d + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +sink("FiguresTables/r_s1_polsep.tex") +apsrtable(r_s1_law1_antint, r_s1_law2_antint, r_s1_law3_antint, + r_s1_law4_antint, r_s1_immop_antint, r_s1_ariz_antint, + r_s1_arizlike_antint, + digits = 2, + align = "c", + model.names = c("Law (English)", "Law (Tuition)", "Law (Welfare)", + "Law (Hire)", "Imm. Opinion", "AZ Law", + "State Bill Harm"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", + "Antipathy $\\times$ Humanization", + "Antipathy $\\times$ Information", + "Antipathy $\\times$ Combined"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Study 2, examining the policy outcomes separately +myvars <- c("condition", "hi_icb", "pol1b", "pol3", "pol4a", "pol4b", "pol5a", + "pol5b", "pol5c", "pol5d") +tmpdat <- stud02[, ..myvars] +tmpdat[, pol1b := abs((pol1b - 1) / (5 - 1) - 1)] +tmpdat[, pol3 := abs((pol3 - 1) / (4 - 1) - 1)] +tmpdat[, pol4a := (pol4a - 1) / (8 - 1)] +tmpdat[, pol4b := (pol4b - 1) / (8 - 1)] +tmpdat[, pol5a := (pol5a - 1) / (8 - 1)] +tmpdat[, pol5b := (pol5b - 1) / (8 - 1)] +tmpdat[, pol5c := (pol5c - 1) / (8 - 1)] +tmpdat[, pol5d := (pol5d - 1) / (8 - 1)] +r_s2_aidillegal <- lm(pol1b ~ condition * hi_icb, tmpdat) +r_s2_immop <- lm(pol3 ~ condition * hi_icb, tmpdat) +r_s2_takeresource <- lm(pol4a ~ condition * hi_icb, tmpdat) +r_s2_denyrights <- lm(pol4b ~ condition * hi_icb, tmpdat) +r_s2_law1 <- lm(pol5a ~ condition * hi_icb, tmpdat) +r_s2_law2 <- lm(pol5b ~ condition * hi_icb, tmpdat) +r_s2_law3 <- lm(pol5c ~ condition * hi_icb, tmpdat) +r_s2_law4 <- lm(pol5d ~ condition * hi_icb, tmpdat) +sink("FiguresTables/r_s2_polsep.tex") +apsrtable(r_s2_law1, r_s2_law2, r_s2_law3, r_s2_law4, r_s2_immop, + r_s2_aidillegal, r_s2_takeresource, r_s2_denyrights, + digits = 2, + align = "c", + model.names = c("Law (English)", "Law (Tuition)", "Law (Welfare)", + "Law (Hire)", "Imm. Opinion", "Aid Illegal", + "Take Resources", "Deny Rights"), + coef.names = c("Intercept", "Illegal", "Antipathy", + "Illegal $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Additional Results, Common Policy Outcomes +######################################## + +stud01[, harm_alt := rowMeans(.SD, na.rm = TRUE), + .SDcols = c("law_english", "law_tuition", "law_welfare", "law_hire", + "immig_opinion_reverse")] + +myvars <- c("pol3_rev", "pol5a", "pol5b", "pol5c", "pol5d") +tmpcb <- psych::alpha(as.data.frame(stud02[, ..myvars]), check.keys = TRUE) +stud02[, harm_alt := tmpcb$scores] +stud02[, harm_alt := (harm_alt - 1) / (6.4 - 1)] + +r_s1_harm_antdint <- lm(harm ~ treatment1 + treatment2 + treatment3 + icb_pre_d + + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +r_s1_harm_alt <- lm(harm_alt ~ treatment1 + treatment2 + treatment3 + icb_pre_d + + treatment1:icb_pre_d + treatment2:icb_pre_d + + treatment3:icb_pre_d, + stud01) +sink("FiguresTables/r_s1_harm_alt.tex") +apsrtable(r_s1_harm_alt, r_s1_harm_antdint, + digits = 2, + align = "c", + model.names = c("Common Policy Items", "Full Policy Scale"), + coef.names = c("Intercept", "Humanization", "Information", + "Combined", "Outgroup Antipathy", "Humanization $\\times$ Antipathy", + "Information $\\times$ Antipathy", "Combined $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +## Study 2 +r_s2_harm_antdint <- lm(policy_harm ~ condition * hi_icb, stud02) +r_s2_harm_alt <- lm(harm_alt ~ condition * hi_icb, stud02) +sink("FiguresTables/r_s2_harm_alt.tex") +apsrtable(r_s2_harm_alt, r_s2_harm_antdint, + digits = 2, + align = "c", + model.names = c("Common Policy Items", "Full Policy Scale"), + coef.names = c("Intercept", "Illegal Condition", "Outgroup Antipathy", + "Illegal Condition $\\times$ Antipathy"), + order = "lr", + coef.rows = 2, + Sweave = TRUE, + stars = "default", + notes = list(se.note, stars.note)) +sink() + +######################################## +#### Additional Results, Empathy and Policy +######################################## + +## Hexagon plot of relationship between empathy and harmful policies in study 1 +p_data <- stud01[, c("treatment", "emp", "harm", "icb_pre_d"), with = FALSE] +p_data[, icb_pre_d := factor(icb_pre_d)] +p_data[, icb_pre_d := plyr::mapvalues(icb_pre_d, 0:1, c("Low", "High"))] +mylabs <- c("Control", "Information", "Humanization", + "Combined") +p_data[, treatment := plyr::mapvalues(treatment, c(4, 1:3), mylabs)] +p_data[, treatment := factor(treatment, mylabs)] +p_data <- na.omit(p_data) +ggplot(p_data, aes(x = emp, y = harm)) + + stat_binhex(bins = 15) + + scale_fill_gradientn(colours = c("light gray", "black")) + + geom_smooth(aes(linetype = icb_pre_d), method = "lm", colour = "black", + se = FALSE, size = 0.75) + + labs(x = "Empathic Concern", y = "Support for Harmful Policies", + linetype = "Outgroup\nAntipathy", fill = "Count") + + facet_wrap(~ treatment, nrow = 1) +ggsave("FiguresTables/s1_hexplot_empANDharm_BYantipathy.png", height = 2.75, + width = 6.25) + +## Hexagon plot for study 2 +p_data <- stud02[, c("condition", "emp_index01", "policy_harm", "hi_icb"), + with = FALSE] +p_data[, hi_icb := factor(hi_icb)] +p_data[, hi_icb := plyr::mapvalues(hi_icb, 0:1, c("Low", "High"))] +mylabs <- c("Legal Immigrants", "Illegal Immigrants") +p_data[, condition := plyr::mapvalues(condition, 0:1, mylabs)] +p_data[, condition := factor(condition, mylabs)] +p_data <- na.omit(p_data) +ggplot(p_data, aes(x = emp_index01, y = policy_harm)) + + stat_binhex(bins = 15) + + scale_fill_gradientn(colours = c("light gray", "black")) + + geom_smooth(aes(linetype = hi_icb), method = "lm", colour = "black", + se = FALSE, size = 0.75) + + labs(x = "Empathic Concern", y = "Support for Harmful Policies", + linetype = "Outgroup\nAntipathy", fill = "Count") + + facet_wrap(~ condition, nrow = 1) +ggsave("FiguresTables/s2_hexplot_empANDharm_BYantipathy.png", height = 2.75, + width = 3.75) + +######################################## +#### Additional Results, flexible interaction effects +######################################## + +## Study 1, Empathy, Humanization +set.seed(33333) +s1_intchk_emp_1 <- interflex(estimator = "kernel", data = stud01, + Y = "emp", D = "treatment", X = "icb_pre", + treat.type = "discrete", base = "4", na.rm = TRUE, + main = "Kernel", ylim = c(-0.25, 0.6), ncols = 1, + xlab = "", ylab = "Marginal Effect of Treatment on Empathy", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +s1_intchk_emp_2 <- interflex(estimator = "binning", data = stud01, + Y = "emp", D = "treatment", X = "icb_pre", + treat.type = "discrete", base = "4", na.rm = TRUE, + nbins = 2, main = "Two Bins (Paper)", ylab = "", + ylim = c(-0.25, 0.6), ncols = 1, + xlab = "Outgroup Antipathy (Moderator)", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5, + bin.labs = FALSE) +s1_intchk_emp_3 <- interflex(estimator = "binning", data = stud01, + Y = "emp", D = "treatment", X = "icb_pre", + treat.type = "discrete", base = "4", na.rm = TRUE, + nbins = 3, main = "Three Bins", ylab = "", xlab = "", + ylim = c(-0.25, 0.6), ncols = 1, + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5, + bin.labs = FALSE) +pFinal <- plot_grid(s1_intchk_emp_1$figure, s1_intchk_emp_2$figure, s1_intchk_emp_3$figure, + ncol = 3, nrow = 1, rel_widths = c(1, 1, 1)) +ggsave("FiguresTables/s1_intchk_emp_new.png", pFinal, width = 6, height = 6.5) + +## Study 2, Empathy +set.seed(33333) +s2_intchk_emp_1 <- interflex(estimator = "kernel", data = stud02, + Y = "emp_index01", D = "condition", X = "icb_measure", + treat.type = "discrete", base = "0", na.rm = TRUE, + main = "Kernel", ylim = c(-0.34, 0.052), + xlab = "", ylab = "Marginal Effect of Treatment on Empathy", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +s2_intchk_emp_2 <- interflex(estimator = "binning", data = stud02, nbins = 2, + cutoffs = 0.5, Y = "emp_index01", D = "condition", + X = "icb_measure", treat.type = "discrete", + base = "0", na.rm = TRUE, bin.labs = FALSE, + main = "Two Bins (Paper)", ylim = c(-0.34, 0.052), + xlab = "Outgroup Antipathy", ylab = "", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +s2_intchk_emp_3 <- interflex(estimator = "binning", data = stud02, nbins = 3, + Y = "emp_index01", D = "condition", X = "icb_measure", + treat.type = "discrete", base = "0", na.rm = TRUE, + main = "Three Bins", ylim = c(-0.34, 0.052), + xlab = "", ylab = "", bin.labs = FALSE, + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +pFinal <- plot_grid(s2_intchk_emp_1$figure, s2_intchk_emp_2$figure, s2_intchk_emp_3$figure, + nrow = 1, rel_widths = c(1, 1, 1)) +ggsave("FiguresTables/s2_intchk_emp_new.png", width = 6, height = 2.5) + +## Study 2, Dissonance +set.seed(33333) +s2_intchk_diss_1 <- interflex(estimator = "kernel", data = stud02, + Y = "diss_measure", D = "condition", X = "icb_measure", + treat.type = "discrete", base = "0", na.rm = TRUE, + main = "Kernel", ylim = c(-0.125, 0.2), + xlab = "", ylab = "Marginal Effect of Treatment on Dissonance", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +s2_intchk_diss_2 <- interflex(estimator = "binning", data = stud02, nbins = 2, + cutoffs = 0.5, Y = "diss_measure", D = "condition", + X = "icb_measure", treat.type = "discrete", + base = "0", na.rm = TRUE, bin.labs = FALSE, + main = "Two Bins (Paper)", ylim = c(-0.125, 0.2), + xlab = "Outgroup Antipathy", ylab = "", + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +s2_intchk_diss_3 <- interflex(estimator = "binning", data = stud02, nbins = 3, + Y = "diss_measure", D = "condition", X = "icb_measure", + treat.type = "discrete", base = "0", na.rm = TRUE, + main = "Three Bins", ylim = c(-0.125, 0.2), + xlab = "", ylab = "", bin.labs = FALSE, + cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) +pFinal <- plot_grid(s2_intchk_diss_1$figure, s2_intchk_diss_2$figure, s2_intchk_diss_3$figure, + nrow = 1, rel_widths = c(1, 1, 1)) +ggsave("FiguresTables/s2_intchk_diss_new.png", width = 6, height = 2.5)