REPRO-Bench / 11 /replication_package /Replication /ReplicationScript /ChangingHearts_GublerEtAl_Jul2021-1.R
| ## 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/fig_01.eps", p, width = 6.25, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_02.eps", p, width = 6, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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$seDiff) | |
| my_q <- qnorm(.975) | |
| ## Plotting the empathy gap for study 1 | |
| p <- ggplot(p_data_diff.df, aes(y = Diff, x = Condition, ymin = Diff - seDiff * my_q, ymax = Diff + seDiff * my_q)) + | |
| geom_errorbar(colour = "black", width = 0.1) + | |
| geom_line(aes(group=1)) + | |
| geom_point(size = 3, fill = "white") + | |
| labs(x = "Treatment", y = "Empathy Gap") | |
| ggsave("FiguresTables/fig_03.eps", p, width = 5.25, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## Standard deviation of empathy difference | |
| sd(stud01$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/fig_04.eps", p, width = 5.5, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_05.eps", pFinal, height = 6, width = 5.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_06.eps", height = 3, width = 5.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| 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/tab_01.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/tab_02.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/fig_B03.eps", p, width = 6.25, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_C04.eps", width = 8.5, height = 5.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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$sdev)^2) | |
| ) | |
| ## Plot 1 | |
| my_size <- 2.9 | |
| p1 <- ggplot(plot_fa, aes(x = Factor1, y = Factor2, label = Variables, | |
| family = "serif")) + | |
| geom_point() + | |
| geom_text(data = plot_fa[c(1, 9)], size = my_size, hjust = 1.1, | |
| vjust = -0.1) + | |
| geom_text(data = plot_fa[c(2, 5, 8)], size = my_size, hjust = -0.1, | |
| vjust = 1.1) + | |
| geom_text(data = plot_fa[c(4)], size = my_size, hjust = 1.1, vjust = 1.1) + | |
| geom_text(data = plot_fa[c(3, 6:7, 10)], size = my_size, hjust = -0.1, | |
| vjust = -0.1) + | |
| geom_vline(xintercept = 0, linetype = "dashed", size = 0.25) + | |
| geom_hline(yintercept = 0, linetype = "dashed", size = 0.25) + | |
| geom_circle(aes(x0 = 0.7, y0 = -0.24, r = 0.28), size = 0.25, | |
| inherit.aes = FALSE) + | |
| xlab("Factor 1") + | |
| ylab("Factor 2") + | |
| xlim(-0.33, 0.98) + | |
| ylim(-0.55, 0.75) | |
| plot(p1) | |
| ## Plot 2 | |
| p2 <- ggplot(plot_pc, aes(x = Labels, y = var_expl, group = 1)) + | |
| geom_line() + | |
| geom_point() + | |
| xlab("Components") + | |
| ylab("Variance Explained") + | |
| scale_x_discrete(labels = abbreviate) | |
| plot(p2) | |
| ## Final Plot | |
| pFinal <- plot_grid(p1, p2, align = "v", axis = "lr", ncol = 1) | |
| ggsave("FiguresTables/fig_D05.eps", pFinal, width = 6.25, height = 8, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## Study 2 factor analysis | |
| tmp <- na.omit(subset(stud02, | |
| select = c("pol1a", "pol1b", "pol2a", "pol2b", "pol2c", | |
| "pol2d", "pol2e", "pol3", "pol4a", "pol4b", | |
| "pol5a", "pol5b", "pol5c", "pol5d"))) | |
| tmp[, pol1b := abs(pol1b - 6)] | |
| tmp[, pol3 := abs(pol3 - 5)] | |
| princomp_fit <- princomp(tmp, cor = 2) | |
| explore_fit <- factanal(tmp, 2, rotation = "varimax") | |
| explore_load <- explore_fit$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/fig_D06.eps", pFinal, width = 6.25, height = 8, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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/fig_E07.eps", balplot_final, width = 5, height = 7, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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/tab_G08.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 = Male)", "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/tab_G09.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 = Male)", "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/tab_G10.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 = Male)", "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/tab_G11.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 = Male)", "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/tab_G12.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 = Male)", "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/tab_G13.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 = Male)", "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/tab_G14.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 = Male)", "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/tab_G15.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 = Male)", "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/tab_G16.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 = Male)", "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/tab_H17.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 = Male)", "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/fig_H08.eps", p, width = 3, height = 3, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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/tab_H18.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/tab_H19.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/tab_H20.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/fig_H09.eps", p, width = 6, height = 5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_H10.eps", p, width = 6, height = 5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_H11.eps", p, width = 3, height = 5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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/tab_H21.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/tab_H22.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/tab_H23.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/tab_H24.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/tab_H25.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/tab_H26.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/tab_H27.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/fig_H12.eps", height = 2.75, width = 6.25, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_H13.eps", height = 2.75, width = 3.75, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ######################################## | |
| #### 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$figure, | |
| ncol = 3, nrow = 1, rel_widths = c(1, 1, 1)) | |
| ggsave("FiguresTables/fig_H14.eps", pFinal, width = 6, height = 6.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## Study 2, Empathy | |
| set.seed(33333) | |
| s2_intchk_emp_1 <- interflex(estimator = "kernel", data = stud02, | |
| Y = "emp_index01", D = "condition", X = "icb_measure", | |
| treat.type = "discrete", base = "0", na.rm = TRUE, | |
| main = "Kernel", ylim = c(-0.34, 0.052), | |
| xlab = "", ylab = "Marginal Effect of Treatment on Empathy", | |
| cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) | |
| s2_intchk_emp_2 <- interflex(estimator = "binning", data = stud02, nbins = 2, | |
| cutoffs = 0.5, Y = "emp_index01", D = "condition", | |
| X = "icb_measure", treat.type = "discrete", | |
| base = "0", na.rm = TRUE, bin.labs = FALSE, | |
| main = "Two Bins (Paper)", ylim = c(-0.34, 0.052), | |
| xlab = "Outgroup Antipathy", ylab = "", | |
| cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) | |
| s2_intchk_emp_3 <- interflex(estimator = "binning", data = stud02, nbins = 3, | |
| Y = "emp_index01", D = "condition", X = "icb_measure", | |
| treat.type = "discrete", base = "0", na.rm = TRUE, | |
| main = "Three Bins", ylim = c(-0.34, 0.052), | |
| xlab = "", ylab = "", bin.labs = FALSE, | |
| cex.main = 0.5, cex.sub = 0.5, cex.lab = 0.5, cex.axis = 0.5) | |
| pFinal <- plot_grid(s2_intchk_emp_1$figure, s2_intchk_emp_2$figure, s2_intchk_emp_3$figure, | |
| nrow = 1, rel_widths = c(1, 1, 1)) | |
| ggsave("FiguresTables/fig_H15.eps", pFinal, width = 6, height = 2.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |
| ## 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/fig_H16.eps", pFinal, width = 6, height = 2.5, | |
| dpi = 300, device = cairo_ps, fallback_resolution = 300) | |