--- title: "Replication Code for Lucid Survey Data Analysis" author: "Brian Guay & Christopher Johnston" date: "6/18/2020" output: html_document: number_sections: yes toc: yes pdf_document: toc: yes subtitle: Ideological Asymmetries and the Determinants of Politically-Motivated Reasoning (American Journal of Political Science, 2020) --- ```{r knitr setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` # Introduction This markdown file contains the code necessary to run the analysis using data collected from the 2018 Lucid survey (Studies 1 and 2). When compiling in markdown, verify that this markdown file is saved in the same folder as the other replication. R Markdown will set the working directory to this folder by default. When running the code line-by-line, set the working directory to the same location as these materials are located. # Setup ```{r setup, warning = F, echo = T, message = F, include = T} # Clear working environment rm(list = ls()) # Log output sink(file = "lucid_code_log.txt") # Create folder to store figures dir.create("lucid_figures") # Install required packages install.packages(c("grDevices", "ggplot2", "dplyr", "estimatr", "psych", "knitr")) # Load packages library(grDevices) # for plotting library(ggplot2) # for plotting library(dplyr) # for data cleaning library(estimatr) # for robust standard errors library(psych) # for calculating measures of internal consistency library(knitr) # for compiling markdown # Load post functions (see README file for description) source("post.R") source("postSim.R") # Function for running lm function with common set of controls quick_lm <- function(outcome = my_outcome, keepers = my_keepers, new = NULL, data = my_data){ keepers <- paste(keepers, collapse = " + ") new <- paste(new, collapse = " + ") rhs <- paste(keepers, new, sep = " + ") model <- paste0(outcome, " ~ ", rhs) fit <- lm(as.formula(model), data = data) } # Function for running glm function with common set of controls quick_glm <- function(outcome = my_outcome, keepers = my_keepers, new = NULL, data = my_data){ keepers <- paste(keepers, collapse = " + ") # character string of IVs separated by "+" new <- paste(new, collapse = " + ") # new variables rhs <- paste(keepers, new, sep = " + ") #paste keepers and new model <- paste0(outcome, " ~ ", rhs) # fit <- glm(as.formula(model), data = data, family = binomial(link = "logit")) } # Function to make colors in base R plots transparent makeTransparent<-function(someColor, alpha=100) { newColor<-col2rgb(someColor) apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2], blue=curcoldata[3],alpha=alpha, maxColorValue=255)}) } # Standard error std_error <- function(var){ sd(var, na.rm = T) / sqrt(length(var[!is.na(var)])) } # Function to put variables on a 0-1 scale rescale_01 <- function(x, max){ (x-1)/(max-1) } # Set seed set.seed(4453) # Set number of simulations for post-estimation to 10000 nsims <- 10000 # Import data data_original <- read.csv("lucid_data.csv", na.strings=c("","NA")) ``` # Data Cleanining ## Attention Checks As discussed in the manuscript, respondents who failed at least one of the two attention checks were prevented from completing the remainder of the survey. Additionally, respondents who proceeded to use a mobile device for the survey (despite being instructed that they would be unable to complete the survey) were prevented from completing the survey. 1.4% of respondents were removed for using a mobile device, 5.9\% were removed for failing the Trump attention check (correctly identifying Trump as the president of the United States), and 6.9\% were removed for failing the numeracy attention check (correctly adding 2 + 2). ```{r, warning = F, echo = F, message = F, include = T} # Breakdown of respondents who were kicked off survey table(data_original$kickoff) prop.table(table(data_original$kickoff, useNA = "always"))*100 # 1.37% kicked off for using a mobile device # 5.87% kicked off for failing Trump attention check # 6.86% kicked off for failing numeracy attention check # Exclude respondents who were kicked off survey data <- data_original[is.na(data_original$kickoff),] nrow(data) # 1,816 respondents in analytical sample ``` ## Party ID and Ideology ```{r} # Political Party ------------------------------------------------------------------------ data$pid7 <- ifelse(data$pid_gen == 1, data$demstr, ifelse(data$pid_gen == 2, data$repstr, ifelse(data$pid_gen > 2, data$pidlean, NA))) # Indicator for Republican (vs. Democrat) ------------------------------------------------ # where Republican = 1, Democrat = 0, and Independent = NA data$rep_dem <- ifelse(data$pid7 < 4, 0, ifelse(data$pid7 > 4, 1, NA)) table(data$pid7, data$rep_dem, useNA = "always") # Ideology ------------------------------------------------------------------------------- data$ideo7 <- data$ideo data$ideo7[data$ideo7 == -99] <- NA table(data$ideo, data$ideo7, useNA = "always") # Indicator for Conservative (vs. Liberal) ----------------------------------------------- data$con_lib <- ifelse(data$ideo7 < 4, 0, ifelse(data$ideo7 > 4, 1, NA)) table(data$ideo7, data$con_lib, useNA = "always") ``` ## Experiment 1 (Evidence Interpretation Task) Respondents were randomly assigned to received evidence about one of five salient political issues (\textit{issue}: a concealed handgun ban, a minimum wage increase, affirmative action, sanctuary cities, or abortion. Respondents were also randomized to receive evidence that supported the liberal or conservative position on the assigned issue, which was achieved by changing the words “increase” and “decrease” in the column names in the evidence table (\textit{evidence direction}, see Appendix for full question wording). ### Create Variables Indicating Issue and Evidence Direction Condition Assignment ```{r} # Create variable for evidence direction condition --------------------------------------- data$exp1_condition <- ifelse(data$DO.BR.FL_133 == "Direction Experiments--Condition A", "A", ifelse(data$DO.BR.FL_133 == "Direction Experiments-- Condition B", "B", "foo")) table(data$exp1_condition, data$DO.BR.FL_133, useNA = "always") # Create variable for issue condition assignment ----------------------------------------- table(data$pol_issue, useNA = "always") # distribution of issue asssignment variable # Create character versoion of issue assignment variable data$pol_issue.ch <- as.character(data$pol_issue) table(data$pol_issue, data$pol_issue.ch, useNA = "always") # drop original pol_issue variable data <- select(data, -c(pol_issue)) # create final issue assignment variable with abbreviated level names data$exp1_issue <- ifelse(data$pol_issue.ch == "abortion", "abort", ifelse(data$pol_issue.ch == "affirmative action", "aa", ifelse(data$pol_issue.ch == "sanctuary cities", "imm", ifelse(data$pol_issue.ch == "raising the minimum wage", "wage", ifelse(data$pol_issue.ch == "carrying concealed handguns", "gun", "foo"))))) table(data$exp1_issue, data$pol_issue.ch, useNA = "always") # remove pol_issue.ch data <- select(data,-c(pol_issue.ch)) # Create indicator for whether evidence points toward liberal position ------------------- data$exp1_liberal_evidence <- ifelse(data$exp1_condition == "A" & data$exp1_issue %in% c("gun", "wage", "imm"), 1, ifelse(data$exp1_condition == "B" & data$exp1_issue %in% c("aa", "abort"), 1, 0)) table(data$exp1_liberal_evidence, data$exp1_condition, data$exp1_issue) ``` ### Combine Outcome Measure Colummns Into Single Variable Which survey questions respondents answer is dependent on the experimental condition each is assigned to. For instance, a respondent assigned to the gun control condition received questions about gun control and not, for instance, immigration. Therefore, responses to experimental outcomes are recorded in different columns in the data set. To combine to different outcome measures responses into a single column, we sum across all outcome measures (regardless of whether the respondent was assigned to answer each measure). Because the rowSums() function ignores missing values (i.e., NAs), and outcome measures which respondents were not assigned to answer contain NAs, the column containing the sum contains the appropriate outcome response for each respondent. There are 10 columns in which the experimental outcome can be recorded, as there are 10 experimental conditions: 5 (issue condition = abortion, affirmative action, gun control, minimum wage, or immigration) X 2 (evidence direction condition = A or B). In the code below we use the following shorthand to refer to each issue: abort = abortion, aa = affirmative action, gun = gun control, wage = minimum wage, imm = immigration. ```{r} # Combine Experiment 1 Outcomes Into Single Variable (ex) # Create vector of outcome variable names from survey # (A = direction condition A, B = direction condition B) exp1_A <- c("exp1_abort_A", "exp1_aa_A", "exp1_gun_A", "exp1_wage_A", "exp1_imm_A") exp1_B <- c("exp1_abort_B", "exp1_aa_B", "exp1_gun_B", "exp1_wage_B", "exp1_imm_B") # all outcome variables are integer vectors apply(data[,c(exp1_A, exp1_B)], 2, class) # 4 unique values of outcome variables apply(data[,c(exp1_A, exp1_B)], 2, unique) # Sum across columns for respondents in evidence direction condition A data$response_1A <- rowSums(data[,exp1_A], na.rm=T) data$response_1A[data$response_1A == -99] <- NA # recode skipped responses as NA data$response_1A[data$response_1A == 0] <- NA # recode 0 as NA, since NA + NA = 0 head(data[1:200,c(exp1_A, "response_1A")]) # verify method # Sum across columns for respondents in evidence direction condition B data$response_1B <- rowSums(data[,exp1_B], na.rm=T) data$response_1B[data$response_1B == -99] <- NA # recode skipped, but seen responses as NA data$response_1B[data$response_1B == 0] <- NA # recode 0 as NA, since NA + NA = 0 head(data[1:200,c(exp1_B, "response_1B")]) # verify method # Create one response variable for experiment 1, combining direction conditions A and B. data$exp1_response <- ifelse(data$exp1_condition == "A", data$response_1A, ifelse(data$exp1_condition == "B", data$response_1B, 9999)) # Verify method table(data$exp1_response, data$response_1A, useNA = "always") table(data$exp1_response, data$response_1B, useNA = "always") ``` ### Create Indicator for Correct Response for Experiment 1 1 indicates correct interpretation of the evidence and 0 indicates incorrect interpretation. In direction condition A, the 2nd response option is correct. In direction condition B, the 1st response option is correct. ```{r} data$exp1_correct <- ifelse(data$exp1_condition == "A" & data$exp1_response == 2, 1, ifelse(data$exp1_condition == "A" & data$exp1_response == 1, 0, ifelse(data$exp1_condition == "B" & data$exp1_response == 2, 0, ifelse(data$exp1_condition == "B" & data$exp1_response == 1, 1, 9999)))) table(data$exp1_correct, useNA = "always") ``` ## Experiment 2 For Experiment 2, respondents were randomly assigned to receive evidence about one of the four remaining issues that they were not assigned to in the Experiment 1 (i.e., if a respondent in Experiment 1 received information about gun control, they were randomly assigned to receive information about either a minimum wage increase, affirmative action, sanctuary cities, or abortion in Experiment 2). Respondents were again randomized to receive evidence that supported either the liberal or conservative position on the assigned issue. Below we follow the same approach as followed in Experiment 1, above. ### Create Variables Indicating Issue and Evidence Direction Condition Assignment ```{r} # Create variable for experimental condition data$exp2_condition <- ifelse(data$DO.BR.FL_137 == "Quality Experiments-- Condition A", "A", ifelse(data$DO.BR.FL_137 == "Quality Experiments-- Condition B", "B", NA)) table(data$DO.BR.FL_137, data$exp2_condition) # Create variable indicating the issue each respondent received data$pol_issue_2.ch <- as.character(data$pol_issue_2) table(data$pol_issue_2, data$pol_issue_2.ch) # Drop original pol_issue variable data <- select(data, -c(pol_issue_2)) data$exp2_issue <- ifelse(data$pol_issue_2.ch == "abortion", "abort", ifelse(data$pol_issue_2.ch == "affirmative action", "aa", ifelse(data$pol_issue_2.ch == "sanctuary cities", "imm", ifelse(data$pol_issue_2.ch == "raising the minimum wage", "wage", ifelse(data$pol_issue_2.ch == "carrying concealed handguns", "gun", NA))))) table(data$pol_issue_2.ch, data$exp2_issue) # Drop original pol_issue.ch variable data <- select(data, -c(pol_issue_2.ch)) # Create indicator for whether evidence points toward liberal position data$exp2_liberal_evidence <- ifelse(data$exp2_condition == "A" & data$exp2_issue %in% c("aa", "abort"), 1, ifelse(data$exp2_condition == "B" & data$exp2_issue %in% c("gun", "wage", "imm"), 1, 0)) ``` ### Aggregate Outcome Responses Across Conditions Following the same procedure used in Experiment 1, we create a common response variable for Experiment 2 below. Because there are two outcomes in Experiment 2, the 'sample size' and 'causal claim' outcomes, we create two common response variables. ```{r} # Exp 2 (Condition A, Sample Size) exp2_A_sample <- c("exp2_abort_A_1", "exp2_aa_A_1", "exp2_gun_A_1", "exp2_wage_A_1", "exp2_imm_A_1") # Exp 2 (Condition B, Sample Size) exp2_B_sample <- c("exp2_abort_B_1", "exp2_aa_B_1", "exp2_gun_B_1", "exp2_wage_B_1", "exp2_imm_B_1") # Exp 2 (Condition A, Causal) exp2_A_causal <- c("exp2_abort_A_2", "exp2_aa_A_2", "exp2_gun_A_2", "exp2_wage_A_2", "exp2_imm_A_2") # Exp 2 (Condition B, Causal) exp2_B_causal <- c("exp2_abort_B_2", "exp2_aa_B_2", "exp2_gun_B_2", "exp2_wage_B_2", "exp2_imm_B_2") # Collapse response variable for sample size question data$exp2_sample <- rowSums(data[,c(exp2_A_sample, exp2_B_sample)], na.rm=T) data$exp2_sample[data$exp2_sample == 0] <- NA data$exp2_sample[data$exp2_sample == -99] <- NA head(data[1:200,c(exp2_A_sample, exp2_B_sample, "exp2_sample")]) # verify method # Reverse code sample size outcome, such that high values indicate size is sufficient # (note: causal outcome is already coded such that higher values indicate that a causal # claim can be made) table(data$exp2_sample) data$exp2_goodSample <- 8 - data$exp2_sample table(data$exp2_sample, data$exp2_goodSample) # Create response variable for causal inference outcome data$exp2_goodCausal <- rowSums(data[,c(exp2_A_causal, exp2_B_causal)], na.rm=T) data$exp2_goodCausal[data$exp2_goodCausal == 0] <- NA #recode unseen skipped responses as NA data$exp2_goodCausal[data$exp2_goodCausal == -99] <- NA #recode seen skipped responses as NA head(data[1:200,c(exp2_A_causal, exp2_B_causal, "exp2_goodCausal")]) # verify method ``` ## Create Congeniality Variables For Experiments 1 and 2 Now that we have collapsed outcome variables into a single outcome variable for Experiment 1 and two outcome variables for Experiment 2 (sample size and causal claim outcomes), we next determine whether each respondent received congenial or uncongenial information, based upon both the direction of the information they were randomly assigned to receive and their prior beliefs. As explained in the manuscript and appendix, we operationalize prior beliefs separately in three ways: the respondent's party identification, self-reported ideology, and position on the issue they received information about. For each operationalization, we create two congeniality variables: a continuous version (e.g., 1 = very uncongenial, 7 = very congenial) and a binary version (e.g., 0 = uncongenial, 1 = congenial). For the binary measure, moderates and non-leaning Independents (for the party ID and ideology operationalizations of congeniality, respectively), are coded as NA. For the continuous measures, they are coded as 4, at the center of the continuous measures, which range from 1-7. Issue positions are measured on a 6-point scale, so the issue position operationalization of congeniality does not face a similar issue. ### Experiment 1 ```{r} # Party ID ------------------------------------------- # Binary data$exp1_congenial_pid_binary <- ifelse(data$pid7 < 4 & data$exp1_liberal_evidence == 1, 1, ifelse(data$pid7 > 4 & data$exp1_liberal_evidence == 0, 1, ifelse(data$pid7 == 4, NA, 0))) head(data[,c("exp1_congenial_pid_binary", "exp1_liberal_evidence", "pid7")]) # verify # Continuous data$exp1_congenial_pid_cont <- ifelse(data$exp1_liberal_evidence == 1, 8 - data$pid7, ifelse(data$exp1_liberal_evidence == 0, data$pid7, NA)) data$exp1_congenial_pid_cont.s <- as.numeric(scale(data$exp1_congenial_pid_cont)) head(data[,c("exp1_congenial_pid_cont", "exp1_liberal_evidence", "pid7")]) # verify # Ideology --------------------------------------------------- # Binary data$exp1_congenial_ideo_binary <- ifelse(data$ideo7 < 4 & data$exp1_liberal_evidence == 1, 1, ifelse(data$ideo7 > 4 & data$exp1_liberal_evidence == 0, 1, ifelse(data$ideo7 == 4, NA, 0))) data[1:10,c("exp1_congenial_ideo_binary", "exp1_liberal_evidence", "ideo7")] # verify # Continuous data$exp1_congenial_ideo_cont <- ifelse(data$exp1_liberal_evidence == 1, 8 - data$ideo7, ifelse(data$exp1_liberal_evidence == 0, data$ideo7, NA)) data$exp1_congenial_ideo_cont.s <- as.numeric(scale(data$exp1_congenial_ideo_cont)) data[1:10,c("exp1_congenial_ideo_cont", "exp1_liberal_evidence", "ideo7")] # verify # Issue Position ------------------------------------------------------------------------- # Continuous # distributions of issue position variables (note that skipped responses = -99) apply(data[,c("issue_gun", "issue_aa", "issue_abort", "issue_imm", "issue_wage")], 2, function(x) table(x, useNA = "always")) # recode NAs data$issue_gun[data$issue_gun == -99] <- NA data$issue_aa[data$issue_aa == -99] <- NA data$issue_abort[data$issue_abort == -99] <- NA data$issue_imm[data$issue_imm == -99] <- NA data$issue_wage[data$issue_wage == -99] <- NA # verify apply(data[,c("issue_gun", "issue_aa", "issue_abort", "issue_imm", "issue_wage")], 2, function(x) table(x, useNA = "always")) # recode such that higher values indicate liberal positions data$gun_lib <- 7 -data$issue_gun data$wage_lib <- data$issue_wage # already coded in correct direction data$aa_lib <- data$issue_aa # already coded in correct direction data$imm_lib <- 7 - data$issue_imm data$abort_lib <- 7 - data$issue_abort issue_vars <- c("gun_lib", "wage_lib", "aa_lib", "imm_lib", "wage_lib") psych::alpha(data[,issue_vars]) # verify all load in same direction # Initialize continuous issue congeniality measure data$exp1_congenial_issue_cont <- NA # note that the logic of the creation of this continuous congeniality measure is # similar to that of the continuous pid and ideology congeniality measures, though # in this case the issue positions are coded such that liberal values are larger # (for pid and ideology, larger values indicated more conservative views). This difference # is taken into account below. data$exp1_congenial_issue_cont <- ifelse(data$exp1_issue == "gun" & data$exp1_liberal_evidence == 1, data$gun_lib, ifelse(data$exp1_issue == "gun" & data$exp1_liberal_evidence == 0, 7 - data$gun_lib, ifelse(data$exp1_issue == "wage" & data$exp1_liberal_evidence == 1, data$wage_lib, ifelse(data$exp1_issue == "wage" & data$exp1_liberal_evidence == 0, 7 - data$wage_lib, ifelse(data$exp1_issue == "aa" & data$exp1_liberal_evidence == 1, data$aa_lib, ifelse(data$exp1_issue == "aa" & data$exp1_liberal_evidence == 0, 7 - data$aa_lib, ifelse(data$exp1_issue == "imm" & data$exp1_liberal_evidence == 1, data$imm_lib, ifelse(data$exp1_issue == "imm" & data$exp1_liberal_evidence == 0, 7 - data$imm_lib, ifelse(data$exp1_issue == "abort" & data$exp1_liberal_evidence == 1, data$abort_lib, ifelse(data$exp1_issue == "abort" & data$exp1_liberal_evidence == 0, 7 - data$abort_lib, NA)))))))))) data$exp1_congenial_issue_cont.s <- as.numeric(scale(data$exp1_congenial_issue_cont)) # Binary data$exp1_congenial_issue_binary <- ifelse(data$exp1_congenial_issue_cont >= 4, 1, ifelse(data$exp1_congenial_issue_cont < 4, 0, NA)) table(data$exp1_congenial_issue_binary, data$exp1_congenial_issue_cont) # verify # Check correlations between continuous pid, ideology, and issue measures of congeniality cor(data$exp1_congenial_pid_cont.s, data$exp1_congenial_ideo_cont.s, use = "complete.obs") cor(data$exp1_congenial_pid_cont.s, data$exp1_congenial_issue_cont.s, use = "complete.obs") cor(data$exp1_congenial_ideo_cont.s, data$exp1_congenial_issue_cont.s, use = "complete.obs") ``` ### Experiment 2 ```{r} # Party ID ------------------------------------------------------------------------------- # Binary data$exp2_congenial_pid_binary <- ifelse(data$pid7 < 4 & data$exp2_liberal_evidence == 1, 1, ifelse(data$pid7 > 4 & data$exp2_liberal_evidence == 0, 1, ifelse(data$pid7 == 4, NA, 0))) # Continous data$exp2_congenial_pid_cont <- ifelse(data$exp2_liberal_evidence == 1, 8 - data$pid7, ifelse(data$exp2_liberal_evidence == 0, data$pid7, NA)) data$exp2_congenial_pid_cont.s <- as.numeric(scale(data$exp2_congenial_pid_cont)) # Ideology ------------------------------------------------------------------------------- # Binary data$exp2_congenial_ideo_binary <- ifelse(data$ideo7 < 4 & data$exp2_liberal_evidence == 1, 1, ifelse(data$ideo7 > 4 & data$exp2_liberal_evidence == 0, 1, ifelse(data$ideo7 == 4, NA, 0))) # Continuous ideo congeniality measure --------------------------------------------------- data$exp2_congenial_ideo_cont <- ifelse(data$exp2_liberal_evidence == 1, 8 - data$ideo7, ifelse(data$exp2_liberal_evidence == 0, data$ideo7, NA)) data$exp2_congenial_ideo_cont.s <- as.numeric(scale(data$exp2_congenial_ideo_cont)) # Issue Position ------------------------------------------------------------------------- # Continuous # Initialize continuous congeniality measure data$exp2_congenial_issue_cont <- NA data$exp2_congenial_issue_cont <- ifelse(data$exp2_issue == "gun" & data$exp2_liberal_evidence == 1, data$gun_lib, ifelse(data$exp2_issue == "gun" & data$exp2_liberal_evidence == 0, 7 - data$gun_lib, ifelse(data$exp2_issue == "wage" & data$exp2_liberal_evidence == 1, data$wage_lib, ifelse(data$exp2_issue == "wage" & data$exp2_liberal_evidence == 0, 7 - data$wage_lib, ifelse(data$exp2_issue == "aa" & data$exp2_liberal_evidence == 1, data$aa_lib, ifelse(data$exp2_issue == "aa" & data$exp2_liberal_evidence == 0, 7 - data$aa_lib, ifelse(data$exp2_issue == "imm" & data$exp2_liberal_evidence == 1, data$imm_lib, ifelse(data$exp2_issue == "imm" & data$exp2_liberal_evidence == 0, 7 - data$imm_lib, ifelse(data$exp2_issue == "abort" & data$exp2_liberal_evidence == 1, data$abort_lib, ifelse(data$exp2_issue == "abort" & data$exp2_liberal_evidence == 0, 7 - data$abort_lib, NA)))))))))) data$exp2_congenial_issue_cont.s <- as.numeric(scale(data$exp2_congenial_issue_cont)) # Binary data$exp2_congenial_issue_binary <- ifelse(data$exp2_congenial_issue_cont >= 4, 1, ifelse(data$exp2_congenial_issue_cont < 4, 0, NA)) ``` ## Standardize Outcomes for Experiment 2 ```{r} # Unstandardized version of DVs data$exp2_goodSample_uns <- data$exp2_goodSample data$exp2_goodCausal_uns <- data$exp2_goodCausal # Drop original variables data <- data %>% dplyr::select(-exp2_goodSample, -exp2_goodCausal) colnames(data)[grep(colnames(data), pattern = "good")] # verify # Standardize DVs data$exp2_goodSample <- as.numeric(scale(data$exp2_goodSample_uns)) data$exp2_goodCausal <- as.numeric(scale(data$exp2_goodCausal_uns)) table(data$exp2_goodSample, data$exp2_goodSample_uns) table(data$exp2_goodCausal, data$exp2_goodCausal_uns) ``` ## Put Continuous Congeniality Measures on 0-1 Scale ```{r} data$exp1_congenial_issue_cont.01 <- (data$exp1_congenial_issue_cont -1)/5 data$exp1_congenial_ideo_cont.01 <- (data$exp1_congenial_ideo_cont -1)/6 data$exp1_congenial_pid_cont.01 <- (data$exp1_congenial_pid_cont -1)/6 data$exp2_congenial_issue_cont.01 <- (data$exp2_congenial_issue_cont -1)/5 data$exp2_congenial_ideo_cont.01 <- (data$exp2_congenial_ideo_cont -1)/6 data$exp2_congenial_pid_cont.01 <- (data$exp2_congenial_pid_cont -1)/6 with(data,table(exp1_congenial_pid_cont.01, exp1_congenial_pid_cont)) with(data,table(exp1_congenial_ideo_cont.01, exp1_congenial_ideo_cont)) with(data,table(exp1_congenial_issue_cont.01, exp1_congenial_issue_cont)) with(data,table(exp2_congenial_pid_cont.01, exp2_congenial_pid_cont)) with(data,table(exp2_congenial_ideo_cont.01, exp2_congenial_ideo_cont)) with(data,table(exp2_congenial_issue_cont.01, exp2_congenial_issue_cont)) ``` ## Need for Closure ```{r} # Original coding: low values = open, high values = closed nfc_vars <- paste("nfc", 1:10, sep = "_") apply(data[,nfc_vars], 2, function(x) table(x, useNA = "always")) data[,nfc_vars][data[,nfc_vars] == -99] <- NA data$nfc_mean <- rowMeans(data[,nfc_vars], na.rm=T) data$nfc_mean.s <- as.numeric(scale(data$nfc_mean)) psych::alpha(data[,nfc_vars]) ``` ## 5-item Mini IPIP ```{r} pip_vars_original <- paste("pip", 1:5, sep = "_") apply(data[,pip_vars_original], 2, function(x) table(x, useNA = "always")) # Recode NAs data[,pip_vars_original][data[,pip_vars_original] == -99] <- NA # Verify apply(data[,pip_vars_original], 2, function(x) table(x, useNA = "always")) # Reverse code two of the PIP variables # pip_1: 5 = have vivid imagination data$pip_1.r <- 6 - data$pip_1 table(data$pip_1, data$pip_1.r) #check # pip_5: 5 = love to think up new ways of doing things data$pip_5.r <- 6 - data$pip_5 table(data$pip_5, data$pip_5.r) #check # Variables that do not need to be recoded: # pip_2: 5 = not interested in abstract ideas # pip_3: 5 = have difficulty understanding abstract ideas # pip_4: 5 = do not have good imagination pip_vars <- c("pip_1.r", "pip_2", "pip_3", "pip_4", "pip_5.r") psych::alpha(data[,pip_vars]) # verify that items load in same direction # Create mean mini-IPIP score data$pip_mean <- rowMeans(data[,pip_vars], na.rm=T) ``` ## Schwartz Values ```{r} # Direction of original coding: higher values correspond to... # sv1 = open # sv2 = closed # sv3 = closed # sv4 = open # sv5 = closed sv_vars_original <- c("sv_1", "sv_2", "sv_3", "sv_4", "sv_5") apply(data[,sv_vars_original], 2, table) data[,sv_vars_original][data[,sv_vars_original] == -99] <- NA apply(data[,sv_vars_original], 2, table) # verify # Variables that need to be recoded are 1 and 4: data$sv_1.r <- 6 - data$sv_1 table(data$sv_1, data$sv_1.r) data$sv_4.r <- 6 - data$sv_4 table(data$sv_4, data$sv_4.r) sv_vars <- c("sv_1.r", "sv_2", "sv_3", "sv_4.r", "sv_5") psych::alpha(data[,sv_vars]) # Create mean variable data$sv_mean <- rowMeans(data[,sv_vars], na.rm=T) ``` ## Create Openness Index (NFC, IPIP, & Schwartz) We create a mean openness trait index by averaging all items from each scale. ```{r} # Check consistency among 3 mean variables psych::alpha(data[,c("nfc_mean", "pip_mean", "sv_mean")]) # put mean indices on 0-1 scale data$nfc_mean.01 <- rescale_01(data$nfc_mean, max = 6) data$pip_mean.01 <- rescale_01(data$pip_mean, max = 5) data$sv_mean.01 <- rescale_01(data$sv_mean, max = 5) # create mean trait index data$trait_index <- rowMeans(data[,c("nfc_mean.01", "pip_mean.01", "sv_mean.01")], na.rm=T) data$trait_index.s <- as.numeric(scale(data$trait_index)) ``` ## Political Identity ```{r} # recode NAs data[,paste("pol_id_", 1:4, sep = "")][data[,paste("pol_id_", 1:4, sep = "")] == -99] <- NA # high values correspond to strong political identity for items 2 and 4. So recode items # 1 and 3. data$pol_id_1.r <- 6 - data$pol_id_1 data$pol_id_2.r <- data$pol_id_2 data$pol_id_3.r <- 6 - data$pol_id_3 data$pol_id_4.r <- data$pol_id_4 pol_id_vars <- c("pol_id_1.r", "pol_id_2.r", "pol_id_3.r", "pol_id_4.r") psych::alpha(data[,pol_id_vars]) #good # create mean score data$pol_id <- rowMeans(data[,pol_id_vars], na.rm=T) data$pol_id.s <- as.numeric(scale(data$pol_id)) ``` ## Partisan Identity ```{r new} # 4 = being party member is extremely important to me data$huddy_import <- ifelse(data$pid7 < 4, data$dem_import, ifelse(data$pid7 > 4, data$rep_import, NA)) # 4 = party describes me extremely well data$huddy_describe <- ifelse(data$pid7 < 4, data$dem_describe, ifelse(data$pid7 > 4, data$rep_describe, NA)) # 5 = use "we" all the time to talk about party data$huddy_we <- ifelse(data$pid7 < 4, data$dem_we, ifelse(data$pid7 > 4, data$rep_we, NA)) # 4 = think about myself as party member a great deal data$huddy_think <- ifelse(data$pid7 < 4, data$dem_think, ifelse(data$pid7 > 4, data$rep_think, NA)) huddy_vars <- c("huddy_import", "huddy_describe", "huddy_we", "huddy_think") # distribution of responses apply(data[,huddy_vars], 2, function(x) table(x, useNA = "always")) # recode NAs data[,huddy_vars][data[,huddy_vars] < 0] <- NA # create standardized mean scale data$huddy_import.01 <- rescale_01(data$huddy_import, max = 4) data$huddy_describe.01 <- rescale_01(data$huddy_describe, max = 4) data$huddy_we.01 <- rescale_01(data$huddy_we, max = 5) data$huddy_think.01 <- rescale_01(data$huddy_think, max = 4) # verify apply(data[,paste(huddy_vars, ".01", sep = "")], 2, range, na.rm=T) psych::alpha(data[,c("huddy_import.01", "huddy_describe.01", "huddy_we.01", "huddy_think.01")]) data$huddy_id <- rowMeans(data[,c("huddy_import.01", "huddy_describe.01", "huddy_we.01", "huddy_think.01")], na.rm=T) data$huddy_id.s <- as.numeric(scale(data$huddy_id)) ``` ## Numeracy ```{r} rasch_vars <- c("rasch_1", "rasch_2", "rasch_3", "rasch_4", "rasch_5", "rasch_6", "rasch_7") #apply(data[,rasch_vars], 2, function(x) table(x, useNA = "always")) apply(data[,rasch_vars], 2, class) # recode character vectors as numeric vectors data$rasch_1.r <- as.numeric(as.character(data$rasch_1)) data$rasch_2.r <- as.numeric(as.character(data$rasch_2)) data$rasch_3.r <- as.numeric(as.character(data$rasch_3)) data$rasch_4.r <- as.numeric(as.character(data$rasch_4)) data$rasch_5.r <- as.numeric(as.character(data$rasch_5)) data$rasch_6.r <- as.numeric(as.character(data$rasch_6)) data$rasch_7.r <- as.numeric(as.character(data$rasch_7)) rasch_vars.r <- paste(rasch_vars, ".r", sep = "") # recode NAs data[,rasch_vars.r][data[,rasch_vars.r] == -99] <- NA # create indicators for correct responses data$rasch_1_corr <- ifelse(data$rasch_1.r == 10, 1, 0) data$rasch_2_corr <- ifelse(data$rasch_2.r == .1, 1, 0) data$rasch_6_corr <- ifelse(data$rasch_6.r == 20, 1, 0) data$rasch_5_corr <- ifelse(data$rasch_5.r == 100, 1, 0) data$rasch_3_corr <- ifelse(data$rasch_3.r == 4, 1, 0) data$rasch_4_corr <- ifelse(data$rasch_4.r == 29, 1, 0) data$rasch_7_corr <- ifelse(data$rasch_7.r == 500, 1, 0) rasch_corr_vars <- c("rasch_1_corr", "rasch_2_corr", "rasch_3_corr", "rasch_4_corr", "rasch_5_corr", "rasch_6_corr", "rasch_7_corr") apply(data[,rasch_corr_vars], 2, function(x) round(prop.table(table(x)),2)) psych::alpha(data[,rasch_corr_vars]) # create mean numeracy score data$numeracy <- rowMeans(data[,rasch_corr_vars], na.rm=T) data$numeracy.s <- as.numeric(scale(data$numeracy)) ``` ## Political Knowledge ```{r} apply(data[,c("pk_speaker", "pk_senterm", "pk_cj", "pk_merkel", "pk_putin")], 2, function(x) table(x, useNA = "always")) data$pk_speaker_corr <- ifelse(data$pk_speaker == 4, 1, ifelse(data$pk_speaker == -99, NA, 0)) data$pk_senterm_corr <- ifelse(data$pk_senterm == 3, 1, ifelse(data$pk_senterm == -99, NA, 0)) data$pk_cj_corr <- ifelse(data$pk_cj == 3, 1, ifelse(data$pk_cj == -99, NA, 0)) data$pk_merkel_corr <- ifelse(data$pk_merkel == 2, 1, ifelse(data$pk_merkel == -99, NA, 0)) data$pk_putin_corr <- ifelse(data$pk_putin == 2, 1, ifelse(data$pk_putin == -99, NA, 0)) pk_vars <- c("pk_speaker_corr", "pk_senterm_corr", "pk_cj_corr", "pk_merkel_corr", "pk_putin_corr") apply(data[,pk_vars], 2, function(x) prop.table(table(x))) data$pk_mean <- rowMeans(data[,pk_vars]) data$pk_mean.s <- as.numeric(scale(data$pk_mean)) cor(data$pk_mean, data$pk_mean.s, use = "complete.obs") psych::alpha(data[,pk_vars]) ``` ## Demographics ```{r} # Gender table(data$gender, useNA = "always") data$female <- ifelse(data$gender == 2, 1, 0) data$female[data$female == -99] <- NA table(data$female, useNA = "always") table(data$gender, data$female) # Race # note: respondents were instructed to check all that apply: 1 = White, 2 = Black, # 3 = Hispanic, 4 = Asian, 5 = Native American, 6 = Middle Eastern, # 7 = Mixed race, 8 = Other # recode NAs data$race_1[data$race_1 == -99] <- NA data$race_2[data$race_2 == -99] <- NA data$race_3[data$race_3 == -99] <- NA data$race_4[data$race_4 == -99] <- NA data$race_5[data$race_5 == -99] <- NA data$race_6[data$race_6 == -99] <- NA data$race_7[data$race_7 == -99] <- NA data$race_white <- ifelse(data$race_1 == 1, 1, 0) data$race_black <- ifelse(data$race_2 == 1, 1, 0) data$race_other <- ifelse(data$race_4 == 1 | data$race_5 == 1 | data$race_6 == 1 | data$race_7 == 1 | data$race_8 == 1, 1, 0) data$hispanic <- ifelse(data$race_3 == 1, 1, 0) data$nonhisp_black <- ifelse(data$race_2 == 1 & data$race_3 != 1, 1, 0) # Education # 1 = < HS, 2 = HS, 3 = some college, 4 = 2-yr college, 5 = 4-yr college, 6 = postgrad data$education <- data$educatt data$education[data$education == -99] <- NA data$edu_lesshs <- ifelse(data$education == 1, 1, 0) data$edu_hs <- ifelse(data$education == 2, 1, 0) data$edu_somecollege <- ifelse(data$education == 3 | data$education == 4, 1, 0) data$edu_college <- ifelse(data$education == 5, 1, 0) data$edu_grad <- ifelse(data$education == 6, 1, 0) # Age data$age <- 2018-data$birthyr data$age.s <- as.numeric(scale(data$age)) # Income # 1-13 scale, where 1 = < 10k, 13 = > 150k data$income <- data$faminc data$income[data$income == -99] <- NA data$income.s <- as.numeric(scale(data$income)) ``` # Analysis: Ideological Asymmetries in PMR ## Model Nominclature The following naming syntax is used when running models and calculating quantities of interest below. - mx = model x (where m1 = asymmetry model and m2 = moderation model) - ex = experiment x - where: - e1 = experiment 1 - e2ss = experiment 2, sample size outcome - e2cc = experiment 2, causal claim outcome - iss/ideo/pid/ = operationalization of R's L/R dimension - where, iss = issue; ideo = ideology; pid = party id. - lib/con = data is subsetted to those individuals who received either - liberal (lib) or conservative (con) -leaning evidence - Example: m1_e1_iss_lib - m1 = model 1 (asymmetry model) - e1 = experiment 1 - iss = L/R dimension defined by R's issue position - lib = R received liberal-leaning evidence in experiment 1 The first set of models ("Asymmetry Models") explore the extent to which there exists an asymmetry in how liberals and conservatives engage in politically motivated reasoning. In total, there are 18 models (3 (interpretation DV, sample size DV, causal claim DV) X 3 (L/R dimension = issue stance, ideology, party ID) X 2 (liberal, conservative evidence)): we run separate models for each of the 3 experimental outcomes: Experiment 1 (interpretation outcome), Experiment 2 (sample size outcome), and Experiment 2 (causal claim outcome), each operationalization of the respondent's L/R dimension (issue stance, self-reported ideology, and self-reported party ID), and respondents' who were randomly assigned to receive liberal- or conservative-leaning evidence. \\ Order of Asymmetry Models: - Models for Experiment 1 - L/R = issue position, liberal evidence - L/R = issue position, conservative evidence - L/R = ideology, liberal evidence - L/R = ideology, conservative evidence - L/R = party ID, liberal evidence - L/R = party ID, conservative evidence - Models for Experiment 2 (Sample Size outcome) - L/R = issue position, liberal evidence - L/R = issue position, conservative evidence - L/R = ideology, liberal evidence - L/R = ideology, conservative evidence - L/R = party ID, liberal evidence - L/R = party ID, conservative evidence - Models for Experiment 2 (Causal Claim outcome) - L/R = issue position, liberal evidence - L/R = issue position, conservative evidence - L/R = ideology, liberal evidence - L/R = ideology, conservative evidence - L/R = party ID, liberal evidence - L/R = party ID, conservative evidence ## Create common set of controls for all models ```{r warning = F, echo = F, message = F, include = T} # asymmetry models, experiment 1 m1_e1_controls <- "exp1_issue" # assymetry models, experiment 2 m1_e2_controls <- "exp2_issue" # moderation models, experiment 1 m2_e1_controls <- "age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + exp1_issue" # moderation models, experiment 2 m2_e2_controls <- "age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + exp2_issue" ``` ## Create Variable for Whether R Has Liberal or Conservative View on the Issue They Received Evidence About Higher values indicate more conservative-leaning evidence. ```{r} # Experiment 1 --------------------------------------------------------------------------- # continuous data$exp1_issue_lr <- ifelse(data$exp1_issue == "gun", 7 - data$gun_lib, ifelse(data$exp1_issue == "aa", 7 - data$aa_lib, ifelse(data$exp1_issue == "wage", 7 - data$wage_lib, ifelse(data$exp1_issue == "abort", 7 - data$abort_lib, ifelse(data$exp1_issue == "imm", 7 - data$imm_lib, NA))))) table(data$exp1_issue_lr, useNA = "always") # binary data$exp1_issue_lr_binary <- ifelse(data$exp1_issue_lr <= 3, 0, ifelse(data$exp1_issue_lr >= 4, 1, NA)) table(data$exp1_issue_lr, data$exp1_issue_lr_binary, useNA = "always") # Experiment 2 --------------------------------------------------------------------------- # continuous data$exp2_issue_lr <- ifelse(data$exp2_issue == "gun", 7 - data$gun_lib, ifelse(data$exp2_issue == "aa", 7 - data$aa_lib, ifelse(data$exp2_issue == "wage", 7 - data$wage_lib, ifelse(data$exp2_issue == "abort", 7 - data$abort_lib, ifelse(data$exp2_issue == "imm", 7 - data$imm_lib, NA))))) # binary data$exp2_issue_lr_binary <- ifelse(data$exp2_issue_lr <= 3, 0, ifelse(data$exp2_issue_lr >= 4, 1, NA)) table(data$exp2_issue_lr, data$exp2_issue_lr_binary, useNA = "always") ``` ## Binary Models (For Main Analysis) ```{r warning = F, echo = F, message = F, include = T} # Experiment 1 --------------------------------------------------------------------------- #L/R = issue m1_e1_iss_lib <- quick_glm(outcome = "exp1_correct", new = "exp1_issue_lr_binary", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_iss_con <- quick_glm(outcome = "exp1_correct", new = "exp1_issue_lr_binary", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) #L/R = ideology m1_e1_ideo_lib <- quick_glm(outcome = "exp1_correct", new = "con_lib", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_ideo_con <- quick_glm(outcome = "exp1_correct", new = "con_lib", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) #L/R = party id m1_e1_pid_lib <- quick_glm(outcome = "exp1_correct", new = "rep_dem", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_pid_con <- quick_glm(outcome = "exp1_correct", new = "rep_dem", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) # Experiment 2 (Sample Size outcome) ----------------------------------------------------- #L/R = issue m1_e2ss_iss_lib <- quick_lm(outcome = "exp2_goodSample", new = "exp2_issue_lr_binary", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_iss_con <- quick_lm(outcome = "exp2_goodSample", new = "exp2_issue_lr_binary", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = ideology m1_e2ss_ideo_lib <- quick_lm(outcome = "exp2_goodSample", new = "con_lib", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_ideo_con <- quick_lm(outcome = "exp2_goodSample", new = "con_lib", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = party id m1_e2ss_pid_lib <- quick_lm(outcome = "exp2_goodSample", new = "rep_dem", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_pid_con <- quick_lm(outcome = "exp2_goodSample", new = "rep_dem", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- #L/R = issue m1_e2cc_iss_lib <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_issue_lr_binary", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_iss_con <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_issue_lr_binary", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = ideology m1_e2cc_ideo_lib <- quick_lm(outcome = "exp2_goodCausal", new = "con_lib", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_ideo_con <- quick_lm(outcome = "exp2_goodCausal", new = "con_lib", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = party id m1_e2cc_pid_lib <- quick_lm(outcome = "exp2_goodCausal", new = "rep_dem", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_pid_con <- quick_lm(outcome = "exp2_goodCausal", new = "rep_dem", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) ``` ## Binary Post-Estimation ```{r warning = F, echo = F, message = F, include = T} # Experiment 1 --------------------------------------------------------------------------- p_m1_e1_iss_lib <- post(model = m1_e1_iss_lib, x1name = "exp1_issue_lr_binary", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e1_iss_con <- post(model = m1_e1_iss_con, x1name = "exp1_issue_lr_binary",x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e1_ideo_lib <- post(model = m1_e1_ideo_lib, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e1_ideo_con <- post(model = m1_e1_ideo_con, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e1_pid_lib <- post(model = m1_e1_pid_lib, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e1_pid_con <- post(model = m1_e1_pid_con, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) # Experiment 2 (Sample Size outcome) ----------------------------------------------------- p_m1_e2ss_iss_lib <- post(model = m1_e2ss_iss_lib, x1name = "exp2_issue_lr_binary", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2ss_iss_con <- post(model = m1_e2ss_iss_con, x1name = "exp2_issue_lr_binary", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2ss_ideo_lib <- post(model = m1_e2ss_ideo_lib, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2ss_ideo_con <- post(model = m1_e2ss_ideo_con, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2ss_pid_lib <- post(model = m1_e2ss_pid_lib, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2ss_pid_con <- post(model = m1_e2ss_pid_con, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- p_m1_e2cc_iss_lib <- post(model = m1_e2cc_iss_lib, x1name = "exp2_issue_lr_binary", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2cc_iss_con <- post(model = m1_e2cc_iss_con, x1name = "exp2_issue_lr_binary", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2cc_ideo_lib <- post(model = m1_e2cc_ideo_lib, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2cc_ideo_con <- post(model = m1_e2cc_ideo_con, x1name = "con_lib", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2cc_pid_lib <- post(model = m1_e2cc_pid_lib, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) p_m1_e2cc_pid_con <- post(model = m1_e2cc_pid_con, x1name = "rep_dem", x1vals = c(0, 1), n.sims = nsims, digits = 5) # Calculate Mean Differences and Confidence Intervals ------------------------------------ # Experiment 1 d_m1_e1_iss <- (p_m1_e1_iss_con@sims[,ncol(p_m1_e1_iss_con@sims)] - p_m1_e1_iss_lib@sims[,ncol(p_m1_e1_iss_lib@sims)]) - (p_m1_e1_iss_lib@sims[,1] - p_m1_e1_iss_con@sims[,1]) d_m1_e1_ideo <- (p_m1_e1_ideo_con@sims[,ncol(p_m1_e1_ideo_con@sims)] - p_m1_e1_ideo_lib@sims[,ncol(p_m1_e1_ideo_lib@sims)]) - (p_m1_e1_ideo_lib@sims[,1] - p_m1_e1_ideo_con@sims[,1]) d_m1_e1_pid <- (p_m1_e1_pid_con@sims[,ncol(p_m1_e1_pid_con@sims)] - p_m1_e1_pid_lib@sims[,ncol(p_m1_e1_pid_lib@sims)]) - (p_m1_e1_pid_lib@sims[,1] - p_m1_e1_pid_con@sims[,1]) # Experiment 2 (Sample Size) d_m1_e2ss_iss <- (p_m1_e2ss_iss_con@sims[,ncol(p_m1_e2ss_iss_con@sims)] - p_m1_e2ss_iss_lib@sims[,ncol(p_m1_e2ss_iss_lib@sims)]) - (p_m1_e2ss_iss_lib@sims[,1] - p_m1_e2ss_iss_con@sims[,1]) d_m1_e2ss_ideo <- (p_m1_e2ss_ideo_con@sims[,ncol(p_m1_e2ss_ideo_con@sims)] - p_m1_e2ss_ideo_lib@sims[,ncol(p_m1_e2ss_ideo_lib@sims)]) - (p_m1_e2ss_ideo_lib@sims[,1] - p_m1_e2ss_ideo_con@sims[,1]) d_m1_e2ss_pid <- (p_m1_e2ss_pid_con@sims[,ncol(p_m1_e2ss_pid_con@sims)] - p_m1_e2ss_pid_lib@sims[,ncol(p_m1_e2ss_pid_lib@sims)]) - (p_m1_e2ss_pid_lib@sims[,1] - p_m1_e2ss_pid_con@sims[,1]) # Experiment 2 (Causal Claim) d_m1_e2cc_iss <- (p_m1_e2cc_iss_con@sims[,ncol(p_m1_e2cc_iss_con@sims)] - p_m1_e2cc_iss_lib@sims[,ncol(p_m1_e2cc_iss_lib@sims)]) - (p_m1_e2cc_iss_lib@sims[,1] - p_m1_e2cc_iss_con@sims[,1]) d_m1_e2cc_ideo <- (p_m1_e2cc_ideo_con@sims[,ncol(p_m1_e2cc_ideo_con@sims)] - p_m1_e2cc_ideo_lib@sims[,ncol(p_m1_e2cc_ideo_lib@sims)]) - (p_m1_e2cc_ideo_lib@sims[,1] - p_m1_e2cc_ideo_con@sims[,1]) d_m1_e2cc_pid <- (p_m1_e2cc_pid_con@sims[,ncol(p_m1_e2cc_pid_con@sims)] - p_m1_e2cc_pid_lib@sims[,ncol(p_m1_e2cc_pid_lib@sims)]) - (p_m1_e2cc_pid_lib@sims[,1] - p_m1_e2cc_pid_con@sims[,1]) # Create table of differences and means -------------------------------------------------- # as dataframe d_names <- as.data.frame(cbind(d_m1_e1_iss, d_m1_e1_ideo, d_m1_e1_pid, d_m1_e2ss_iss, d_m1_e2ss_ideo, d_m1_e2ss_pid, d_m1_e2cc_iss, d_m1_e2cc_ideo, d_m1_e2cc_pid)) # as numeric vector d_names_r <- c(d_m1_e1_iss, d_m1_e1_ideo, d_m1_e1_pid, d_m1_e2ss_iss, d_m1_e2ss_ideo, d_m1_e2ss_pid, d_m1_e2cc_iss, d_m1_e2cc_ideo, d_m1_e2cc_pid) # calculate means and 95% confidence intervals dtbl <- data.frame(name = colnames(d_names), mean = apply(d_names, 2, mean), ci.lo = apply(d_names, 2, quantile, probs = .025), ci.hi = apply(d_names, 2, quantile, probs = .975)) ``` ## Binary Figures We create separate figures for each experimental outcome (Experiment 1; Experiment 2 Sample Size outcome; and Experiment 2 Causal Claim outcome). For each of these experimental outcomes, we create separate figures for each operationalization of a respondent's L/R dimension (issue position, ideology, and party ID). This results in 9 figures. For the sake of simplicity, the 3 figures for each experimental outcome are horizontally arranged in a single pdf file. ```{r warning = F, echo = F, message = F, include = T} # ---------------------------------------------------------------------------------------- # Experiment 1 --------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_b_asym_1.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,2,1.1,1)) # Issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(.20, .70), xlab = "Issue Position", ylab = "Pr(Correct)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e1_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e1_iss_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e1_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e1_iss_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_iss_lib@est[1:2,2], y1 = p_m1_e1_iss_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_iss_con@est[1:2,2], y1 = p_m1_e1_iss_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) legend("topleft", legend = c("Left", "Right"), # lty = , lwd = 4, col = c("deepskyblue3", "firebrick2"), pch = c(16,15), bty = "n", cex = 2, title = "Evidence:") # Ideology-------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(.20, .70), xlab = "Ideology", ylab = "Pr(Correct)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e1_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e1_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e1_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e1_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_ideo_lib@est[1:2,2], y1 = p_m1_e1_ideo_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_ideo_con@est[1:2,2], y1 = p_m1_e1_ideo_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) legend(.5, 1, legend = c("Left", "Right"), lty = 1, lwd = 4, col = c("deepskyblue3", "firebrick2"), pch = 16, bty = "n", cex = 2, title = "Evidence:") mtext("Pr(Correct Interpretation of Evidence)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(.20, .70), xlab = "Party ID", ylab = "Pr(Correct)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e1_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e1_pid_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e1_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e1_pid_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_pid_lib@est[1:2,2], y1 = p_m1_e1_pid_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e1_pid_con@est[1:2,2], y1 = p_m1_e1_pid_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) dev.off() # ---------------------------------------------------------------------------------------- # Experiment 2 (Sample Size outcome) ----------------------------------------------------- # ---------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_b_asym_2.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,1,1.1,1)) # Issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Issue Position", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2ss_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2ss_iss_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2ss_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2ss_iss_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_iss_lib@est[1:2,2], y1 = p_m1_e2ss_iss_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_iss_con@est[1:2,2], y1 = p_m1_e2ss_iss_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) # Ideology ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Ideology", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2ss_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2ss_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2ss_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2ss_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_ideo_lib@est[1:2,2], y1 = p_m1_e2ss_ideo_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_ideo_con@est[1:2,2], y1 = p_m1_e2ss_ideo_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) mtext("Sample Size is Sufficient (in SDs)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Party ID", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2ss_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2ss_pid_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2ss_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2ss_pid_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_pid_lib@est[1:2,2], y1 = p_m1_e2ss_pid_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2ss_pid_con@est[1:2,2], y1 = p_m1_e2ss_pid_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) dev.off() # ---------------------------------------------------------------------------------------- # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- # ---------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_b_asym_3.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,1,1.1,1)) # issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Issue Position", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2cc_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2cc_iss_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2cc_iss_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2cc_iss_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_iss_lib@est[1:2,2], y1 = p_m1_e2cc_iss_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_iss_con@est[1:2,2], y1 = p_m1_e2cc_iss_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) # Ideology ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Ideology", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2cc_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2cc_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2cc_ideo_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2cc_ideo_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_ideo_lib@est[1:2,2], y1 = p_m1_e2cc_ideo_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_ideo_con@est[1:2,2], y1 = p_m1_e2cc_ideo_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) mtext("Can Make Causal Claim (in SDs)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(-.2, 1.2), ylim = c(-.5, .5), xlab = "Party ID", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7, xaxt = "n") axis(1, at = c(0, 1), labels = c("", ""), cex.axis = 1.5) points(0:1, p_m1_e2cc_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", cex = 4) points(0:1, p_m1_e2cc_pid_con@est[1:2,1], pch = 15, col = "firebrick2", cex = 4) points(0:1, p_m1_e2cc_pid_lib@est[1:2,1], pch = 16, col = "deepskyblue3", type = "l", lwd = 4, lty = 2) points(0:1, p_m1_e2cc_pid_con@est[1:2,1], pch = 15, col = "firebrick2", type = "l", lwd = 4, lty = 2) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_pid_lib@est[1:2,2], y1 = p_m1_e2cc_pid_lib@est[1:2,3], col = makeTransparent( "deepskyblue3"), lwd = 7) segments(x0 = c(0, 1), x1 = c(0, 1), y0 = p_m1_e2cc_pid_con@est[1:2,2], y1 = p_m1_e2cc_pid_con@est[1:2,3], col = makeTransparent("firebrick2"), lwd = 7) dev.off() # ---------------------------------------------------------------------------------------- # Difference in Differences Plot --------------------------------------------------------- # ---------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_b_asym_4.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,6,1,0), mar = c(4.1,1,1.1,1), xpd = NA) x_vals <- c(1,3,5) # Experiment 1 --------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Evidence Interpretation", ylab = "", cex.lab = 2, cex.axis = 1.7, xaxt = "n") points(x_vals, dtbl$mean[1:3], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl$ci.lo[1:3], y1 = dtbl$ci.hi[1:3], lwd = 7, col = makeTransparent("black", 150)) mtext("(Conservative - Liberal)", side = 2, outer = T, padj = -1.8, cex = 1.7) # Experiment 2 (SS) ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Sample Size", ylab = "", cex.lab = 2, cex.axis = 1.7, yaxt = "n", xaxt = "n") points(x_vals, dtbl$mean[4:6], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl$ci.lo[4:6], y1 = dtbl$ci.hi[4:6], lwd = 7, col = makeTransparent("black", 150)) # Experiment 2 (CC) ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Causality", ylab = "", cex.lab = 2, cex.axis = 1.7, yaxt = "n", xaxt = "n") points(x_vals, dtbl$mean[7:9], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl$ci.lo[7:9], y1 = dtbl$ci.hi[7:9], lwd = 7, col = makeTransparent("black", 150) ) # cross-plot axis axis(1, at = -17:6, lwd.tick=0, labels=FALSE) segments(x0 = -17, x1 = -17, y0 = -1.08, y1 = -1.12) segments(x0 = 6, x1 = 6, y0 = -1.08, y1 = -1.12) # horizontal line at y = 0 segments(x0 = -17.5, x1 = 6, y0 = 0, y1 = 0, lty = 2) legend("topright", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), bty = "n", cex = 3) dev.off() ``` ## Continuous Models (For Appendix) ```{r warning = F, echo = F, message = F, include = T} # Experiment 1 --------------------------------------------------------------------------- #L/R = issue m1_e1_iss_lib_cnt <- quick_glm(outcome = "exp1_correct", new = "exp1_issue_lr", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_iss_con_cnt <- quick_glm(outcome = "exp1_correct", new = "exp1_issue_lr", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) #L/R = ideology m1_e1_ideo_lib_cnt <- quick_glm(outcome = "exp1_correct", new = "ideo7", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_ideo_con_cnt <- quick_glm(outcome = "exp1_correct", new = "ideo7", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) #L/R = party id m1_e1_pid_lib_cnt <- quick_glm(outcome = "exp1_correct", new = "pid7", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 1,]) m1_e1_pid_con_cnt <- quick_glm(outcome = "exp1_correct", new = "pid7", keepers = m1_e1_controls, data = data[data$exp1_liberal_evidence == 0,]) # Experiment 2 (Sample Size outcome) ----------------------------------------------------- #L/R = issue m1_e2ss_iss_lib_cnt <- quick_lm(outcome = "exp2_goodSample", new = "exp2_issue_lr", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_iss_con_cnt <- quick_lm(outcome = "exp2_goodSample", new = "exp2_issue_lr", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = ideology m1_e2ss_ideo_lib_cnt <- quick_lm(outcome = "exp2_goodSample", new = "ideo7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_ideo_con_cnt <- quick_lm(outcome = "exp2_goodSample", new = "ideo7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = party id m1_e2ss_pid_lib_cnt <- quick_lm(outcome = "exp2_goodSample", new = "pid7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2ss_pid_con_cnt <- quick_lm(outcome = "exp2_goodSample", new = "pid7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- #L/R = issue m1_e2cc_iss_lib_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_issue_lr", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_iss_con_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_issue_lr", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = ideology m1_e2cc_ideo_lib_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "ideo7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_ideo_con_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "ideo7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) #L/R = party id m1_e2cc_pid_lib_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "pid7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 1,]) m1_e2cc_pid_con_cnt <- quick_lm(outcome = "exp2_goodCausal", new = "pid7", keepers = m1_e2_controls, data = data[data$exp2_liberal_evidence == 0,]) ``` ## Continuous Post-Estimation ```{r warning = F, echo = F, message = F, include = T} # Experiment 1 --------------------------------------------------------------------------- p_m1_e1_iss_lib_cnt <- post(model = m1_e1_iss_lib_cnt, x1name = "exp1_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e1_iss_con_cnt <- post(model = m1_e1_iss_con_cnt, x1name = "exp1_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e1_ideo_lib_cnt <- post(model = m1_e1_ideo_lib_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e1_ideo_con_cnt <- post(model = m1_e1_ideo_con_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e1_pid_lib_cnt <- post(model = m1_e1_pid_lib_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e1_pid_con_cnt <- post(model = m1_e1_pid_con_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) # Experiment 2 (Sample Size outcome) ----------------------------------------------------- p_m1_e2ss_iss_lib_cnt <- post(model = m1_e2ss_iss_lib_cnt, x1name = "exp2_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e2ss_iss_con_cnt <- post(model = m1_e2ss_iss_con_cnt, x1name = "exp2_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e2ss_ideo_lib_cnt <- post(model = m1_e2ss_ideo_lib_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2ss_ideo_con_cnt <- post(model = m1_e2ss_ideo_con_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2ss_pid_lib_cnt <- post(model = m1_e2ss_pid_lib_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2ss_pid_con_cnt <- post(model = m1_e2ss_pid_con_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- p_m1_e2cc_iss_lib_cnt <- post(model = m1_e2cc_iss_lib_cnt, x1name = "exp2_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e2cc_iss_con_cnt <- post(model = m1_e2cc_iss_con_cnt, x1name = "exp2_issue_lr", x1vals = 1:6, n.sims = nsims, digits = 5) p_m1_e2cc_ideo_lib_cnt <- post(model = m1_e2cc_ideo_lib_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2cc_ideo_con_cnt <- post(model = m1_e2cc_ideo_con_cnt, x1name = "ideo7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2cc_pid_lib_cnt <- post(model = m1_e2cc_pid_lib_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) p_m1_e2cc_pid_con_cnt <- post(model = m1_e2cc_pid_con_cnt, x1name = "pid7", x1vals = 1:7, n.sims = nsims, digits = 5) # Calculate Mean Differences and Confidence Intervals ------------------------------------ # Experiment 1 d_m1_e1_iss_cnt <- (p_m1_e1_iss_con_cnt@sims[,ncol(p_m1_e1_iss_con_cnt@sims)] - p_m1_e1_iss_lib_cnt@sims[,ncol(p_m1_e1_iss_lib_cnt@sims)]) - (p_m1_e1_iss_lib_cnt@sims[,1] - p_m1_e1_iss_con_cnt@sims[,1]) d_m1_e1_ideo_cnt <- (p_m1_e1_ideo_con_cnt@sims[,ncol(p_m1_e1_ideo_con_cnt@sims)] - p_m1_e1_ideo_lib_cnt@sims[,ncol(p_m1_e1_ideo_lib_cnt@sims)]) - (p_m1_e1_ideo_lib_cnt@sims[,1] - p_m1_e1_ideo_con_cnt@sims[,1]) d_m1_e1_pid_cnt <- (p_m1_e1_pid_con_cnt@sims[,ncol(p_m1_e1_pid_con_cnt@sims)] - p_m1_e1_pid_lib_cnt@sims[,ncol(p_m1_e1_pid_lib_cnt@sims)]) - (p_m1_e1_pid_lib_cnt@sims[,1] - p_m1_e1_pid_con_cnt@sims[,1]) # Experiment 2 (Sample Size) d_m1_e2ss_iss_cnt <- (p_m1_e2ss_iss_con_cnt@sims[,ncol(p_m1_e2ss_iss_con_cnt@sims)] - p_m1_e2ss_iss_lib_cnt@sims[,ncol(p_m1_e2ss_iss_lib_cnt@sims)]) - (p_m1_e2ss_iss_lib_cnt@sims[,1] - p_m1_e2ss_iss_con_cnt@sims[,1]) d_m1_e2ss_ideo_cnt <- (p_m1_e2ss_ideo_con_cnt@sims[,ncol(p_m1_e2ss_ideo_con_cnt@sims)] - p_m1_e2ss_ideo_lib_cnt@sims[,ncol(p_m1_e2ss_ideo_lib_cnt@sims)]) - (p_m1_e2ss_ideo_lib_cnt@sims[,1] - p_m1_e2ss_ideo_con_cnt@sims[,1]) d_m1_e2ss_pid_cnt <- (p_m1_e2ss_pid_con_cnt@sims[,ncol(p_m1_e2ss_pid_con_cnt@sims)] - p_m1_e2ss_pid_lib_cnt@sims[,ncol(p_m1_e2ss_pid_lib_cnt@sims)]) - (p_m1_e2ss_pid_lib_cnt@sims[,1] - p_m1_e2ss_pid_con_cnt@sims[,1]) # Experiment 2 (Causal Claim) d_m1_e2cc_iss_cnt <- (p_m1_e2cc_iss_con_cnt@sims[,ncol(p_m1_e2cc_iss_con_cnt@sims)] - p_m1_e2cc_iss_lib_cnt@sims[,ncol(p_m1_e2cc_iss_lib_cnt@sims)]) - (p_m1_e2cc_iss_lib_cnt@sims[,1] - p_m1_e2cc_iss_con_cnt@sims[,1]) d_m1_e2cc_ideo_cnt <- (p_m1_e2cc_ideo_con_cnt@sims[,ncol(p_m1_e2cc_ideo_con_cnt@sims)] - p_m1_e2cc_ideo_lib_cnt@sims[,ncol(p_m1_e2cc_ideo_lib_cnt@sims)]) - (p_m1_e2cc_ideo_lib_cnt@sims[,1] - p_m1_e2cc_ideo_con_cnt@sims[,1]) d_m1_e2cc_pid_cnt <- (p_m1_e2cc_pid_con_cnt@sims[,ncol(p_m1_e2cc_pid_con_cnt@sims)] - p_m1_e2cc_pid_lib_cnt@sims[,ncol(p_m1_e2cc_pid_lib_cnt@sims)]) - (p_m1_e2cc_pid_lib_cnt@sims[,1] - p_m1_e2cc_pid_con_cnt@sims[,1]) # Create table of differences and means # as dataframe d_names_cnt <- as.data.frame(cbind(d_m1_e1_iss_cnt, d_m1_e1_ideo_cnt, d_m1_e1_pid_cnt, d_m1_e2ss_iss_cnt, d_m1_e2ss_ideo_cnt, d_m1_e2ss_pid_cnt, d_m1_e2cc_iss_cnt, d_m1_e2cc_ideo_cnt, d_m1_e2cc_pid_cnt)) # as numeric vector d_names_r_cnt <- c(d_m1_e1_iss_cnt, d_m1_e1_ideo_cnt, d_m1_e1_pid_cnt, d_m1_e2ss_iss_cnt, d_m1_e2ss_ideo_cnt, d_m1_e2ss_pid_cnt, d_m1_e2cc_iss_cnt, d_m1_e2cc_ideo_cnt, d_m1_e2cc_pid_cnt) dtbl_cnt <- data.frame(name = colnames(d_names_cnt), mean = apply(d_names_cnt, 2, mean), ci.lo = apply(d_names_cnt, 2, quantile, probs = .025), ci.hi = apply(d_names_cnt, 2, quantile, probs = .975)) ``` ## Continuous Figures We create separate figures for each experimental outcome (Experiment 1; Experiment 2, Sample Size outcome; and Experiment 2, Causal Claim outcome). For each of these experimental outcomes, we create separate figures for each operationalization of a respondent's L/R dimension (issue position, ideology, and party ID). This results in 9 figures. For simplicity, the 3 figures for each experimental outcome are horizontally arranged in a single pdf file. Each of the 3 figures in each PDF uses a different operationalization of a respondent's L/R orientation. ```{r warning = F, echo = F, message = F, include = T} #----------------------------------------------------------------------------------------- # Experiment 1 --------------------------------------------------------------------------- #----------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_c_asym_1.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,2,1.1,1)) # Issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(1,6), ylim = c(0, 1), xlab = "Issue Position", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:6, p_m1_e1_iss_lib_cnt@est[1:6,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:6, p_m1_e1_iss_con_cnt@est[1:6,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e1_iss_lib_cnt@est[1:6,2], rev(p_m1_e1_iss_lib_cnt@est[1:6,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e1_iss_con_cnt@est[1:6,2], rev(p_m1_e1_iss_con_cnt@est[1:6,3])), col= adjustcolor("firebrick2", .1), border=NA) legend(1.5, 1, legend = c("Left", "Right"), lty = c(2,1), lwd = 4, col = c("deepskyblue3", "firebrick2"), bty = "n", cex = 2, title = "Evidence:") # Ideology ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(0, 1), xlab = "Ideology", ylab = "Pr(Correct)", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e1_ideo_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e1_ideo_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e1_ideo_lib_cnt@est[1:7,2], rev(p_m1_e1_ideo_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e1_ideo_con_cnt@est[1:7,2], rev(p_m1_e1_ideo_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) mtext("Pr(Correct Interpretation of Evidence)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(0, 1), xlab = "Party ID", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e1_pid_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e1_pid_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e1_pid_lib_cnt@est[1:7,2], rev(p_m1_e1_pid_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e1_pid_con_cnt@est[1:7,2], rev(p_m1_e1_pid_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) dev.off() #----------------------------------------------------------------------------------------- # Experiment 2 (Sample Size outcome) ----------------------------------------------------- #----------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_c_asym_2.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,1,1.1,1)) # Issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(1,6), ylim = c(-1, 1), xlab = "Issue Position", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:6, p_m1_e2ss_iss_lib_cnt@est[1:6,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:6, p_m1_e2ss_iss_con_cnt@est[1:6,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e2ss_iss_lib_cnt@est[1:6,2], rev(p_m1_e2ss_iss_lib_cnt@est[1:6,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e2ss_iss_con_cnt@est[1:6,2], rev(p_m1_e2ss_iss_con_cnt@est[1:6,3])), col= adjustcolor("firebrick2", .1), border=NA) # Ideology ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(-1, 1), xlab = "Ideology", ylab = "Sample Size is Sufficient (in SDs)", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e2ss_ideo_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e2ss_ideo_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2ss_ideo_lib_cnt@est[1:7,2], rev(p_m1_e2ss_ideo_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2ss_ideo_con_cnt@est[1:7,2], rev(p_m1_e2ss_ideo_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) mtext("Sample Size is Sufficient (in SDs)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(-1, 1), xlab = "Party ID", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e2ss_pid_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e2ss_pid_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2ss_pid_lib_cnt@est[1:7,2], rev(p_m1_e2ss_pid_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2ss_pid_con_cnt@est[1:7,2], rev(p_m1_e2ss_pid_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) dev.off() #----------------------------------------------------------------------------------------- # Experiment 2 (Causal Claim outcome) ---------------------------------------------------- #----------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_c_asym_3.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,5,1,0), mar = c(4.1,1,1.1,1)) # Issue Positions ------------------------------------------------------------------------ plot(1,1, col = "white", bty = "n", xlim = c(1,6), ylim = c(-1, 1), xlab = "Issue Position", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:6, p_m1_e2cc_iss_lib_cnt@est[1:6,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:6, p_m1_e2cc_iss_con_cnt@est[1:6,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e2cc_iss_lib_cnt@est[1:6,2], rev(p_m1_e2cc_iss_lib_cnt@est[1:6,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,6,1), rev(seq(1,6,1))), c(p_m1_e2cc_iss_con_cnt@est[1:6,2], rev(p_m1_e2cc_iss_con_cnt@est[1:6,3])), col= adjustcolor("firebrick2", .1), border=NA) # Ideology ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(-1, 1), xlab = "Ideology", ylab = "Can Make Causal Claim (in SDs)", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e2cc_ideo_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e2cc_ideo_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2cc_ideo_lib_cnt@est[1:7,2], rev(p_m1_e2cc_ideo_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2cc_ideo_con_cnt@est[1:7,2], rev(p_m1_e2cc_ideo_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) mtext("Can Make Causal Claim (in SDs)", side = 2, outer = T, padj = -2, cex = 1.7) # Party ID ------------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(1,7), ylim = c(-1, 1), xlab = "Party ID", ylab = "", cex.lab = 2, cex.axis = 1.7) points(1:7, p_m1_e2cc_pid_lib_cnt@est[1:7,1], pch = 16, col = "deepskyblue3", lty = 2, type = "l", lwd = 4) points(1:7, p_m1_e2cc_pid_con_cnt@est[1:7,1], pch = 15, col = "firebrick2", type = "l", lwd = 4) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2cc_pid_lib_cnt@est[1:7,2], rev(p_m1_e2cc_pid_lib_cnt@est[1:7,3])), col= adjustcolor("deepskyblue3", .1), border=NA) polygon(c(seq(1,7,1), rev(seq(1,7,1))), c(p_m1_e2cc_pid_con_cnt@est[1:7,2], rev(p_m1_e2cc_pid_con_cnt@est[1:7,3])), col= adjustcolor("firebrick2", .1), border=NA) dev.off() #----------------------------------------------------------------------------------------- # Difference in Differences Plot --------------------------------------------------------- #----------------------------------------------------------------------------------------- pdf("lucid_figures/lucid_u_c_asym_4.pdf", height = 6, width = 8) par(mfrow = c(1,3), oma = c(.1,6,1,0), mar = c(4.1,1,1.1,1), xpd = NA) x_vals <- c(1,3,5) # Experiment 1 --------------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Evidence Interpretation", ylab = "", cex.lab = 2, cex.axis = 1.7, xaxt = "n") points(x_vals, dtbl_cnt$mean[1:3], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl_cnt$ci.lo[1:3], y1 = dtbl_cnt$ci.hi[1:3], lwd = 5, col = makeTransparent("black", 150)) mtext("(Conservative - Liberal)", side = 2, outer = T, padj = -1.8, cex = 1.7) # Experiment 2 (SS) ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Sample Size", ylab = "", cex.lab = 2, cex.axis = 1.7, yaxt = "n", xaxt = "n") points(x_vals, dtbl_cnt$mean[4:6], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl_cnt$ci.lo[4:6], y1 = dtbl_cnt$ci.hi[4:6], lwd = 5, col = makeTransparent("black", 150)) # Experiment 2 (CC) ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(0,7), ylim = c(-1,1), xlab = "Causality", ylab = "", cex.lab = 2, cex.axis = 1.7, yaxt = "n", xaxt = "n") points(x_vals, dtbl_cnt$mean[7:9], pch = c(15, 16, 17), cex = 4) segments(x0 = x_vals, x1 = x_vals, y0 = dtbl_cnt$ci.lo[7:9], y1 = dtbl_cnt$ci.hi[7:9], lwd = 5, col = makeTransparent("black", 150) ) # cross-plot axis axis(1, at = -17:6, lwd.tick=0, labels=FALSE) segments(x0 = -17, x1 = -17, y0 = -1.08, y1 = -1.12) segments(x0 = 6, x1 = 6, y0 = -1.08, y1 = -1.12) # horizontal line at y = 0 segments(x0 = -17.5, x1 = 6, y0 = 0, y1 = 0, lty = 2) legend("topright", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), bty = "n", cex = 3) dev.off() ``` # Analysis: The Moderating Effect of Epistemic Needs ## Need for Closure These models use NFC as a measure of openness. In the next section we run identical models using the Trait Index instead. ```{r} # Experiment 1---------------------------------------------------------------------------- # Issue Position m2a_e1_iss <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_issue_binary*pol_id.s + exp1_congenial_issue_binary*nfc_mean.s + exp1_congenial_issue_binary*numeracy.s + exp1_congenial_issue_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Ideology m2a_e1_ideo <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_ideo_binary*pol_id.s + exp1_congenial_ideo_binary*nfc_mean.s + exp1_congenial_ideo_binary*numeracy.s + exp1_congenial_ideo_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Party ID m2a_e1_pid <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_pid_binary*huddy_id.s + exp1_congenial_pid_binary*nfc_mean.s + exp1_congenial_pid_binary*numeracy.s + exp1_congenial_pid_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Experiment 2 (Sample Size)-------------------------------------------------------------- # Issue Positions m2a_e2ss_iss <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_issue_binary*pol_id.s + exp2_congenial_issue_binary*nfc_mean.s + exp2_congenial_issue_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Ideology m2a_e2ss_ideo <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_ideo_binary*pol_id.s + exp2_congenial_ideo_binary*nfc_mean.s + exp2_congenial_ideo_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Party ID m2a_e2ss_pid <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_pid_binary*huddy_id.s + exp2_congenial_pid_binary*nfc_mean.s + exp2_congenial_pid_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Experiment 2 (Causal Claim)------------------------------------------------------------- # Issue Positions m2a_e2cc_iss <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_issue_binary*pol_id.s + exp2_congenial_issue_binary*nfc_mean.s + exp2_congenial_issue_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Ideology m2a_e2cc_ideo <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_ideo_binary*pol_id.s + exp2_congenial_ideo_binary*nfc_mean.s + exp2_congenial_ideo_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Party ID m2a_e2cc_pid <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_pid_binary*huddy_id.s + exp2_congenial_pid_binary*nfc_mean.s + exp2_congenial_pid_binary*pk_mean.s", keepers = m2_e2_controls, data = data) ``` ## Trait Index In these models we subsitute the Openness Trait Index for NFC. ```{r} # Experiment 1---------------------------------------------------------------------------- # Issue Position m2b_e1_iss <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_issue_binary*pol_id.s + exp1_congenial_issue_binary*trait_index.s + exp1_congenial_issue_binary*numeracy.s + exp1_congenial_issue_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Ideology m2b_e1_ideo <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_ideo_binary*pol_id.s + exp1_congenial_ideo_binary*trait_index.s + exp1_congenial_ideo_binary*numeracy.s + exp1_congenial_ideo_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Party ID m2b_e1_pid <- quick_glm(outcome = "exp1_correct", new = "exp1_congenial_pid_binary*huddy_id.s + exp1_congenial_pid_binary*trait_index.s + exp1_congenial_pid_binary*numeracy.s + exp1_congenial_pid_binary*pk_mean.s", keepers = m2_e1_controls, data = data) # Experiment 2 (Sample Size) ------------------------------------------------------------- # Issue Positions m2b_e2ss_iss <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_issue_binary*pol_id.s + exp2_congenial_issue_binary*trait_index.s + exp2_congenial_issue_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Ideology m2b_e2ss_ideo <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_ideo_binary*pol_id.s + exp2_congenial_ideo_binary*trait_index.s + exp2_congenial_ideo_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Party ID m2b_e2ss_pid <- quick_lm(outcome = "exp2_goodSample", new = "exp2_congenial_pid_binary*huddy_id.s + exp2_congenial_pid_binary*trait_index.s + exp2_congenial_pid_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Experiment 2 (Causal Claim)------------------------------------------------------------- # Issue Positions m2b_e2cc_iss <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_issue_binary*pol_id.s + exp2_congenial_issue_binary*trait_index.s + exp2_congenial_issue_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Ideology m2b_e2cc_ideo <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_ideo_binary*pol_id.s + exp2_congenial_ideo_binary*trait_index.s + exp2_congenial_ideo_binary*pk_mean.s", keepers = m2_e2_controls, data = data) # Party ID m2b_e2cc_pid <- quick_lm(outcome = "exp2_goodCausal", new = "exp2_congenial_pid_binary*huddy_id.s + exp2_congenial_pid_binary*trait_index.s + exp2_congenial_pid_binary*pk_mean.s", keepers = m2_e2_controls, data = data) ``` ## Figures ```{r} rownames <- c("m2a_e1_iss", "m2a_e1_ideo", "m2a_e1_pid", "m2a_e2ss_iss", "m2a_e2ss_ideo", "m2a_e2ss_pid", "m2a_e2cc_iss", "m2a_e2cc_ideo", "m2a_e2cc_pid") nfc_bin <- rbind( as.data.frame(summary(m2a_e1_iss)$coef)[21,1:2], as.data.frame(summary(m2a_e1_ideo)$coef)[21,1:2], as.data.frame(summary(m2a_e1_pid)$coef)[21,1:2], as.data.frame(summary(m2a_e2ss_iss)$coef)[20,1:2], as.data.frame(summary(m2a_e2ss_ideo)$coef)[20,1:2], as.data.frame(summary(m2a_e2ss_pid)$coef)[20,1:2], as.data.frame(summary(m2a_e2cc_iss)$coef)[20,1:2], as.data.frame(summary(m2a_e2cc_ideo)$coef)[20,1:2], as.data.frame(summary(m2a_e2cc_pid)$coef)[20,1:2] ) ti_bin <- rbind( as.data.frame(summary(m2b_e1_iss)$coef)[21,1:2], as.data.frame(summary(m2b_e1_ideo)$coef)[21,1:2], as.data.frame(summary(m2b_e1_pid)$coef)[21,1:2], as.data.frame(summary(m2b_e2ss_iss)$coef)[20,1:2], as.data.frame(summary(m2b_e2ss_ideo)$coef)[20,1:2], as.data.frame(summary(m2b_e2ss_pid)$coef)[20,1:2], as.data.frame(summary(m2b_e2cc_iss)$coef)[20,1:2], as.data.frame(summary(m2b_e2cc_ideo)$coef)[20,1:2], as.data.frame(summary(m2b_e2cc_pid)$coef)[20,1:2] ) id_bin <- rbind( as.data.frame(summary(m2a_e1_iss)$coef)[20,1:2], as.data.frame(summary(m2a_e1_ideo)$coef)[20,1:2], as.data.frame(summary(m2a_e1_pid)$coef)[20,1:2], as.data.frame(summary(m2a_e2ss_iss)$coef)[19,1:2], as.data.frame(summary(m2a_e2ss_ideo)$coef)[19,1:2], as.data.frame(summary(m2a_e2ss_pid)$coef)[19,1:2], as.data.frame(summary(m2a_e2cc_iss)$coef)[19,1:2], as.data.frame(summary(m2a_e2cc_ideo)$coef)[19,1:2], as.data.frame(summary(m2a_e2cc_pid)$coef)[19,1:2] ) pk_bin <- rbind( as.data.frame(summary(m2a_e1_iss)$coef)[23,1:2], as.data.frame(summary(m2a_e1_ideo)$coef)[23,1:2], as.data.frame(summary(m2a_e1_pid)$coef)[23,1:2], as.data.frame(summary(m2a_e2ss_iss)$coef)[21,1:2], as.data.frame(summary(m2a_e2ss_ideo)$coef)[21,1:2], as.data.frame(summary(m2a_e2ss_pid)$coef)[21,1:2], as.data.frame(summary(m2a_e2cc_iss)$coef)[21,1:2], as.data.frame(summary(m2a_e2cc_ideo)$coef)[21,1:2], as.data.frame(summary(m2a_e2cc_pid)$coef)[21,1:2] ) num_bin <- rbind( as.data.frame(summary(m2a_e1_iss)$coef)[22,1:2], as.data.frame(summary(m2a_e1_ideo)$coef)[22,1:2], as.data.frame(summary(m2a_e1_pid)$coef)[22,1:2] ) # NFC Plot-------------------------------------------------------------------------------- pdf("lucid_figures/nfc_openness_b_2way.pdf", height = 4, width = 8) par(mfrow = c(2,3), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = nfc_bin[1:3, 1] + 1.96*nfc_bin[1:3,2], y1 = nfc_bin[1:3, 1] - 1.96*nfc_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, nfc_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Need for Closure", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Sample Size") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = nfc_bin[4:6, 1] + 1.96*nfc_bin[4:6,2], y1 = nfc_bin[4:6, 1] - 1.96*nfc_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, nfc_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Causality") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = nfc_bin[7:9, 1] + 1.96*nfc_bin[7:9,2], y1 = nfc_bin[7:9, 1] - 1.96*nfc_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, nfc_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) # Trait Index Plot----------------------------------------------------------------------- # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = ti_bin[1:3, 1] + 1.96*ti_bin[1:3,2], y1 = ti_bin[1:3, 1] - 1.96*ti_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, ti_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Openness Index", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = ti_bin[4:6, 1] + 1.96*ti_bin[4:6,2], y1 = ti_bin[4:6, 1] - 1.96*ti_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, ti_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) legend("bottom", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = ti_bin[7:9, 1] + 1.96*ti_bin[7:9,2], y1 = ti_bin[7:9, 1] - 1.96*ti_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, ti_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) dev.off() # Identity Plot--------------------------------------------------------------------------- pdf("lucid_figures/identity_b_2way.pdf", height = 4, width = 8) par(mfrow = c(1,3), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = id_bin[1:3, 1] + 1.96*id_bin[1:3,2], y1 = id_bin[1:3, 1] - 1.96*id_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, id_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Identity", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Sample Size") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = id_bin[4:6, 1] + 1.96*id_bin[4:6,2], y1 = id_bin[4:6, 1] - 1.96*id_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, id_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) legend("bottom", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Causality") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = id_bin[7:9, 1] + 1.96*id_bin[7:9,2], y1 = id_bin[7:9, 1] - 1.96*id_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, id_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) dev.off() # Knowledge Plot-------------------------------------------------------------------------- pdf("lucid_figures/pk_b_2way.pdf", height = 4, width = 8) par(mfrow = c(1,3), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[1:3, 1] + 1.96*pk_bin[1:3,2], y1 = pk_bin[1:3, 1] - 1.96*pk_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, pk_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Political Knowledge", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Sample Size") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[4:6, 1] + 1.96*pk_bin[4:6,2], y1 = pk_bin[4:6, 1] - 1.96*pk_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, pk_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) legend("bottom", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Causality") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[7:9, 1] + 1.96*pk_bin[7:9,2], y1 = pk_bin[7:9, 1] - 1.96*pk_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, pk_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) dev.off() # Numeracy Plot----------------------------------------------------------------------- pdf("lucid_figures/numeracy_b_2way.pdf", height = 4, width = 8) par(mfrow = c(1,1), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = num_bin[1:3, 1] + 1.96*num_bin[1:3,2], y1 = num_bin[1:3, 1] - 1.96*num_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, num_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Numeracy", side = 2, padj = -5) # common y axis label legend("bottom", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) dev.off() # Combined PK and Numeracy Plot---------------------------------------------------------- pdf("lucid_figures/pk_numeracy_b_2way.pdf", height = 4, width = 8) par(mfrow = c(1,3), pch = 16, mar = c(0,2,1,0), oma = c(2,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.4,.7), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[1:3, 1] + 1.96*pk_bin[1:3,2], y1 = pk_bin[1:3, 1] - 1.96*pk_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, pk_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) # add numeracy points and segments num_adj <- .25 alpha_level <- 150 segments(x0 = 1:3 + num_adj, x1 = 1:3 + num_adj, y0 = num_bin[1:3, 1] + 1.96*num_bin[1:3,2], y1 = num_bin[1:3, 1] - 1.96*num_bin[1:3,2], lwd = 3, col = makeTransparent("gray40", alpha_level)) points(1:3 + num_adj, num_bin[1:3 + num_adj,1], cex = 2, pch = c(15, 16, 17), col = makeTransparent("black", alpha_level)) axis(2, at = seq(-.4, .7, .20), labels = seq(-.4, .7, .20)) # common y axis mtext(text = "Expertise", side = 2, padj = -5) # common y axis label legend(.8, -.25, legend = c("Political Knowledge", "Numeracy"), pch = c(15), col = c("black", makeTransparent("black", alpha_level)), cex = 1.5, ncol = 1, xpd = NA, pt.cex = 2, bty = "n") # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.4,.7), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Sample Size") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[4:6, 1] + 1.96*pk_bin[4:6,2], y1 = pk_bin[4:6, 1] - 1.96*pk_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, pk_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) #legend("bottom", legend = c("Political Knowledge", "Numeracy"), # pch = c(16), col = c("black", makeTransparent("black", # alpha_level)), # cex = 1.5, ncol = 2, xpd = NA, pt.cex = 2, lwd = 2) legend(.5, -.3, legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.4,.7), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Causality") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = pk_bin[7:9, 1] + 1.96*pk_bin[7:9,2], y1 = pk_bin[7:9, 1] - 1.96*pk_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, pk_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) dev.off() ``` # Ideological Differences in NFC and Openess Trait Index ```{r warning = F, echo = F, message = F, include = T} data$ideo3 <- ifelse(data$ideo7 < 4, "Liberal", ifelse(data$ideo7 == 4, "Moderate", ifelse(data$ideo7 > 3, "Conservative", NA))) par(mfrow = c(2, 1)) # Ideological Asymmetries in NFC plot(density(data$nfc_mean.s, na.rm=T), main = "Ideological Differences in NFC", xlim = c(-4, 4)) points(density(data$nfc_mean.s[data$ideo3 == "Liberal"], na.rm=T), col = "blue") points(density(data$nfc_mean.s[data$ideo3 == "Conservative"], na.rm=T), col = "red") abline(v = mean(data$nfc_mean.s[data$ideo3 == "Liberal"], na.rm=T), col = "blue") abline(v = mean(data$nfc_mean.s[data$ideo3 == "Conservative"], na.rm=T), col = "red") # Ideological Asymmetries in Openness plot(density(data$trait_index.s, na.rm=T), main = "Ideological Differences in Trait Index", xlim = c(-4, 4)) points(density(data$trait_index.s[data$ideo3 == "Liberal"], na.rm=T), col = "blue") points(density(data$trait_index.s[data$ideo3 == "Conservative"], na.rm=T), col = "red") abline(v = mean(data$trait_index.s[data$ideo3 == "Liberal"], na.rm=T), col = "blue") abline(v = mean(data$trait_index.s[data$ideo3 == "Conservative"], na.rm=T), col = "red") nfc_diff <- data %>% dplyr::group_by(ideo7) %>% dplyr::summarize(mean = mean(nfc_mean.s, na.rm=T), se = std_error(nfc_mean.s)) %>% na.omit() %>% ggplot(aes(x = ideo7, y = mean)) + coord_cartesian(ylim = c(-1, 1)) + geom_point() + geom_segment(aes(x = ideo7, xend = ideo7, y = mean - 1.96*se, yend = mean + 1.96*se)) + labs(x = "Ideology (Liberal to Conservative)", y= "Mean Need for Closure (in SDs)") + theme_bw() ggsave("lucid_figures/nfc_diff.pdf", nfc_diff, width = 6, height = 4) ti_diff <- data %>% dplyr::group_by(ideo7) %>% dplyr::summarize(mean = mean(trait_index.s, na.rm=T), se = std_error(trait_index.s)) %>% na.omit() %>% ggplot(aes(x = ideo7, y = mean)) + coord_cartesian(ylim = c(-1, 1)) + geom_point() + geom_segment(aes(x = ideo7, xend = ideo7, y = mean - 1.96*se, yend = mean + 1.96*se)) + labs(x = "Ideology (Liberal to Conservative)", y = "Openness Trait Index (in SDs)") + theme_bw() ggsave("lucid_figures/ti_diff.pdf", ti_diff, width = 6, height = 4) cor(data$nfc_mean, data$ideo7, use = "complete.obs") cor(data$trait_index, data$ideo7, use = "complete.obs") cor(data$nfc_mean, data$pid7, use = "complete.obs") cor(data$trait_index, data$pid7, use = "complete.obs") ``` # R&R Pooled 3-way Interactions ```{r} # Pool Data ----------------------------------------------------------------------------- # Rescale Outcomes data$exp2_goodSample_01 <- rescale_01(data$exp2_goodSample_uns, max = 7) #table(data$exp2_goodSample, data$exp2_goodSample_01) data$exp2_goodCausal_01 <- rescale_01(data$exp2_goodCausal_uns, max = 7) #table(data$exp2_goodCausal, data$exp2_goodCausal_01) # 1. Create shorter dataframe containing only variables needed for models # create respondent id var data$id <- 1:nrow(data) keep_vars <- c("id", "exp1_correct", "exp2_goodSample_01", "exp2_goodCausal_01", "exp1_congenial_issue_binary", "exp1_congenial_ideo_binary", "exp1_congenial_pid_binary", "exp1_congenial_issue_cont.s", "exp1_congenial_ideo_cont.s", "exp1_congenial_pid_cont.s", "pol_id.s", "huddy_id.s", "nfc_mean.s", "numeracy.s", "pk_mean.s", "age.s", "female", "edu_hs", "edu_somecollege", "edu_college", "edu_grad", "hispanic", "nonhisp_black", "income.s", "exp1_issue", "exp2_issue") # 2. create new dataframe new <- rbind(data, data, data) new$out_num <- c(rep( "e1", nrow(data)), rep ("e2ss", nrow(data)), rep ("e2cc", nrow(data))) # 3. create new outcome variable new$outcome <- NA new$outcome[new$out_num == "e1"] <- new$exp1_correct[new$out_num == "e1"] new$outcome[new$out_num == "e2ss"] <- new$exp2_goodSample_01[new$out_num == "e2ss"] new$outcome[new$out_num == "e2cc"] <- new$exp2_goodCausal_01[new$out_num == "e2cc"] # 4. create new issue varible new$issue <- NA new$issue[new$out_num == "e1"] <- new$exp1_issue[new$out_num == "e1"] new$issue[new$out_num == "e2ss"] <- new$exp2_issue[new$out_num == "e2ss"] new$issue[new$out_num == "e2cc"] <- new$exp2_issue[new$out_num == "e2cc"] #5. create new congeniality measure new$congenial_issue_binary <- ifelse(new$out_num == "e1", new$exp1_congenial_issue_binary, new$exp2_congenial_issue_binary) new$congenial_ideo_binary <- ifelse(new$out_num == "e1", new$exp1_congenial_ideo_binary, new$exp2_congenial_ideo_binary) new$congenial_pid_binary <- ifelse(new$out_num == "e1", new$exp1_congenial_pid_binary, new$exp2_congenial_pid_binary) new$congenial_issue_cont.s <- ifelse(new$out_num == "e1", new$exp1_congenial_issue_cont.s, new$exp2_congenial_issue_cont.s) new$congenial_ideo_cont.s <- ifelse(new$out_num == "e1", new$exp1_congenial_ideo_cont.s, new$exp2_congenial_ideo_cont.s) new$congenial_pid_cont.s <- ifelse(new$out_num == "e1", new$exp1_congenial_pid_cont.s, new$exp2_congenial_pid_cont.s) # 1A: Pool 3 Outcomes, Binary------------------------------------------------------------- # Issue Position pooled_a_iss_robust <- lm_robust(outcome ~ congenial_issue_binary*nfc_mean.s + congenial_issue_binary*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # Ideology pooled_a_ideo_robust <- lm_robust(outcome ~ congenial_ideo_binary*nfc_mean.s + congenial_ideo_binary*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # Party ID pooled_a_pid_robust <- lm_robust(outcome ~ congenial_pid_binary*nfc_mean.s + congenial_pid_binary*huddy_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # 1B: Pool 3 Outcomes, Continuous--------------------------------------------------------- # Issue Position pooled_b_iss_robust <- lm_robust(outcome ~ congenial_issue_cont.s*nfc_mean.s + congenial_issue_cont.s*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # Ideology pooled_b_ideo_robust <- lm_robust(outcome ~ congenial_ideo_cont.s*nfc_mean.s + congenial_ideo_cont.s*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # Party ID pooled_b_pid_robust <- lm_robust(outcome ~ congenial_pid_cont.s*nfc_mean.s + congenial_pid_cont.s*huddy_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new, clusters = id) # 1C: Pool 2 Outcomes, Binary------------------------------------------------------------ # Issue Position pooled_c_iss_robust <- lm_robust(outcome ~ congenial_issue_binary*nfc_mean.s + congenial_issue_binary*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) # Ideology pooled_c_ideo_robust <- lm_robust(outcome ~ congenial_ideo_binary*nfc_mean.s + congenial_ideo_binary*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) # Party ID pooled_c_pid_robust <- lm_robust(outcome ~ congenial_pid_binary*nfc_mean.s + congenial_pid_binary*huddy_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) # 1D: Pool 2 Outcomes, Continuous------------------------------------------------------- # Issue Position pooled_d_iss_robust <- lm_robust(outcome ~ congenial_issue_cont.s*nfc_mean.s + congenial_issue_cont.s*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) # Ideology pooled_d_ideo_robust <- lm_robust(outcome ~ congenial_ideo_cont.s*nfc_mean.s + congenial_ideo_cont.s*pol_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) # Party ID pooled_d_pid_robust <- lm_robust(outcome ~ congenial_pid_cont.s*nfc_mean.s + congenial_pid_cont.s*huddy_id.s*pk_mean.s + age.s + female + edu_hs + edu_somecollege + edu_college + edu_grad + hispanic + nonhisp_black + income.s + issue, data = new[new$out_num != "e1",], clusters = id) summary(pooled_d_iss_robust) summary(pooled_d_ideo_robust) summary(pooled_d_pid_robust) # To Plot: tidy(pooled_a_iss_robust)[23, c("term", "estimate", "std.error")] tidy(pooled_a_ideo_robust)[23, c("term", "estimate", "std.error")] tidy(pooled_a_pid_robust)[23, c("term", "estimate", "std.error")] tidy(pooled_b_iss_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_b_ideo_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_b_pid_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_c_iss_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_c_ideo_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_c_pid_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_d_iss_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_d_ideo_robust)[23,c("term", "estimate", "std.error")] tidy(pooled_d_pid_robust)[23,c("term", "estimate", "std.error")] r1_pooled_bin <- rbind( tidy(pooled_a_iss_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_a_ideo_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_a_pid_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_b_iss_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_b_ideo_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_b_pid_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_c_iss_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_c_ideo_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_c_pid_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_d_iss_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_d_ideo_robust)[23,c("term", "estimate", "std.error")], tidy(pooled_d_pid_robust)[23,c("term", "estimate", "std.error")]) r1_pooled_bin$model <- c(rep("a",3), rep("b",3), rep("c",3), rep("d",3)) r1_pooled_bin$measure <- rep(c("iss", "ideo", "pid"), 4) # a = 3 pool, binary # b = 3 pool, continuous # c = 3 pool, binary # d = 3 pool, continous pdf("lucid_figures/rr_3way_pooled.pdf", height = 4, width = 8) par(mfrow = c(2,1), pch = 16, mar = c(1,2,1,0), oma = c(.5,5,1,0)) # 3 Outcomes Pooled ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.1,.1), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "3 Pooled Outcomes (Experiments 1 & 2)") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) # binary congeniality segments(x0 = 1:3, x1 = 1:3, y0 = r1_pooled_bin[r1_pooled_bin$model == "a", 2] + 1.96*r1_pooled_bin[1:3,3], y1 = r1_pooled_bin[r1_pooled_bin$model == "a", 2] - 1.96*r1_pooled_bin[1:3,3], lwd = 3, col = "gray40") points(1:3, r1_pooled_bin[r1_pooled_bin$model == "a", 2], cex = 1, pch = c(15, 16, 17)) # continuous congeniality segments(x0 = 1.2:3.2, x1 = 1.2:3.2, y0 = r1_pooled_bin[r1_pooled_bin$model == "b", 2] + 1.96*r1_pooled_bin[1:3,3], y1 = r1_pooled_bin[r1_pooled_bin$model == "b", 2] - 1.96*r1_pooled_bin[1:3,3], lwd = 3, col = "gray40") points(1.2:3.2, r1_pooled_bin[r1_pooled_bin$model == "b", 2], cex = 1, pch = c(0, 1, 2)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Cong. X PK X ID", side = 2, padj = -5) # common y axis label # 2 Outcomes Pooled ---------------------------------------------------------------------- plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.1,.1), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "2 Pooled Outcomes (Experiment 2 Only)") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) # binary congeniality segments(x0 = 1:3, x1 = 1:3, y0 = r1_pooled_bin[r1_pooled_bin$model == "c", 2] + 1.96*r1_pooled_bin[1:3,3], y1 = r1_pooled_bin[r1_pooled_bin$model == "c", 2] - 1.96*r1_pooled_bin[1:3,3], lwd = 3, col = "gray40") points(1:3, r1_pooled_bin[r1_pooled_bin$model == "c", 2], cex = 1, pch = c(15, 16, 17)) # continuous congeniality segments(x0 = 1.2:3.2, x1 = 1.2:3.2, y0 = r1_pooled_bin[r1_pooled_bin$model == "d", 2] + 1.96*r1_pooled_bin[1:3,3], y1 = r1_pooled_bin[r1_pooled_bin$model == "d", 2] - 1.96*r1_pooled_bin[1:3,3], lwd = 3, col = "gray40") points(1.2:3.2, r1_pooled_bin[r1_pooled_bin$model == "d", 2], cex = 1, pch = c(0, 1, 2)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Cong. X PK X ID", side = 2, padj = -5) # common y axis label legend(x = .6, y = -.09, legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =.7, ncol = 3, xpd = NA) legend(x = 2.5, y = -.08, legend = c("Binary Congeniality Measure", "Continuous Congeniality Measure"), pch = c(16, 1), cex =.7, ncol = 1, xpd = NA) dev.off() ``` # R&R NFC and Trait Index Moderation Analysis with No Controls ## Need for Closure ```{r} # Experiment 1---------------------------------------------------------------------------- # Issue Position r1_nfc_e1_iss <- glm(exp1_correct ~ exp1_congenial_issue_binary*nfc_mean.s, data = data) summary(r1_nfc_e1_iss) # Ideology r1_nfc_e1_ideo <- glm(exp1_correct ~ exp1_congenial_ideo_binary*nfc_mean.s, data = data) summary(r1_nfc_e1_ideo) # Party ID r1_nfc_e1_pid <- glm(exp1_correct ~ exp1_congenial_pid_binary*nfc_mean.s, data = data) summary(r1_nfc_e1_pid) # Experiment 2 (Sample Size)-------------------------------------------------------------- # Issue Position r1_nfc_e2ss_iss <- glm(exp2_goodSample ~ exp2_congenial_issue_binary*nfc_mean.s, data = data) summary(r1_nfc_e2ss_iss) # Ideology r1_nfc_e2ss_ideo <- glm(exp2_goodSample ~ exp2_congenial_ideo_binary*nfc_mean.s, data = data) summary(r1_nfc_e2ss_ideo) # Party ID r1_nfc_e2ss_pid <- glm(exp2_goodSample ~ exp2_congenial_pid_binary*nfc_mean.s, data = data) summary(r1_nfc_e2ss_pid) # Experiment 2 (Causal Claim)------------------------------------------------------------- # issue Position r1_nfc_e2cc_iss <- glm(exp2_goodCausal ~ exp2_congenial_issue_binary*nfc_mean.s, data = data) summary(r1_nfc_e2cc_iss) # Ideology r1_nfc_e2cc_ideo <- glm(exp2_goodCausal ~ exp2_congenial_ideo_binary*nfc_mean.s, data = data) summary(r1_nfc_e2cc_ideo) # Party ID r1_nfc_e2cc_pid <- glm(exp2_goodCausal ~ exp2_congenial_pid_binary*nfc_mean.s, data = data) summary(r1_nfc_e2cc_pid) ``` ## Trait Index ```{r} # Experiment 1---------------------------------------------------------------------------- # Issue Position r1_ti_e1_iss <- glm(exp1_correct ~ exp1_congenial_issue_binary*trait_index.s, data = data) summary(r1_ti_e1_iss) # Ideology r1_ti_e1_ideo <- glm(exp1_correct ~ exp1_congenial_ideo_binary*trait_index.s, data = data) summary(r1_ti_e1_ideo) # Party ID r1_ti_e1_pid <- glm(exp1_correct ~ exp1_congenial_pid_binary*trait_index.s, data = data) summary(r1_ti_e1_pid) # Experiment 2 (Sample Size)-------------------------------------------------------------- # Issue Position r1_ti_e2ss_iss <- glm(exp2_goodSample ~ exp2_congenial_issue_binary*trait_index.s, data = data) summary(r1_ti_e2ss_iss) # Ideology r1_ti_e2ss_ideo <- glm(exp2_goodSample ~ exp2_congenial_ideo_binary*trait_index.s, data = data) summary(r1_ti_e2ss_ideo) # Party ID r1_ti_e2ss_pid <- glm(exp2_goodSample ~ exp2_congenial_pid_binary*trait_index.s, data = data) summary(r1_ti_e2ss_pid) # Experiment 2 (Causal Claim)------------------------------------------------------------- # issue Position r1_ti_e2cc_iss <- glm(exp2_goodCausal ~ exp2_congenial_issue_binary*trait_index.s, data = data) summary(r1_ti_e2cc_iss) # Ideology r1_ti_e2cc_ideo <- glm(exp2_goodCausal ~ exp2_congenial_ideo_binary*trait_index.s, data = data) summary(r1_ti_e2cc_ideo) # Party ID r1_ti_e2cc_pid <- glm(exp2_goodCausal ~ exp2_congenial_pid_binary*trait_index.s, data = data) summary(r1_ti_e2cc_pid) ``` ## Figures ```{r} rownames <- c("r1_nfc_e1_iss", "r1_nfc_e1_ideo", "r1_nfc_e1_pid", "r1_nfc_e2ss_iss", "r1_nfc_e2ss_ideo", "r1_nfc_e2ss_pid", "r1_nfc_e2cc_iss", "r1_nfc_e2cc_ideo", "r1_nfc_e2cc_pid") r1_nfc_bin <- rbind( as.data.frame(summary(r1_nfc_e1_iss)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e1_ideo)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e1_pid)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2ss_iss)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2ss_ideo)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2ss_pid)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2cc_iss)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2cc_ideo)$coef)[4,1:2], as.data.frame(summary(r1_nfc_e2cc_pid)$coef)[4,1:2] ) r1_ti_bin <- rbind( as.data.frame(summary(r1_ti_e1_iss)$coef)[4,1:2], as.data.frame(summary(r1_ti_e1_ideo)$coef)[4,1:2], as.data.frame(summary(r1_ti_e1_pid)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2ss_iss)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2ss_ideo)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2ss_pid)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2cc_iss)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2cc_ideo)$coef)[4,1:2], as.data.frame(summary(r1_ti_e2cc_pid)$coef)[4,1:2] ) # NFC Plot-------------------------------------------------------------------------------- pdf("lucid_figures/r1_nfc_b.pdf", height = 4, width = 8) par(mfrow = c(2,3), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Evidence Interpretation") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_nfc_bin[1:3, 1] + 1.96*r1_nfc_bin[1:3,2], y1 = r1_nfc_bin[1:3, 1] - 1.96*r1_nfc_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, r1_nfc_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Need for Closure", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Sample Size") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_nfc_bin[4:6, 1] + 1.96*r1_nfc_bin[4:6,2], y1 = r1_nfc_bin[4:6, 1] - 1.96*r1_nfc_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, r1_nfc_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "Causality") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_nfc_bin[7:9, 1] + 1.96*r1_nfc_bin[7:9,2], y1 = r1_nfc_bin[7:9, 1] - 1.96*r1_nfc_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, r1_nfc_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) # Trait Index Plot----------------------------------------------------------------------- #par(mfrow = c(2,3), pch = 16, mar = c(0,2,1,0), oma = c(.5,5,1,0)) # E1 plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_ti_bin[1:3, 1] + 1.96*r1_ti_bin[1:3,2], y1 = r1_ti_bin[1:3, 1] - 1.96*r1_ti_bin[1:3,2], lwd = 3, col = "gray40") points(1:3, r1_ti_bin[1:3,1], cex = 2, pch = c(15, 16, 17)) axis(2, at = seq(-.4, .4, .10)) # common y axis mtext(text = "Openness Index", side = 2, padj = -5) # common y axis label # E2ss plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_ti_bin[4:6, 1] + 1.96*r1_ti_bin[4:6,2], y1 = r1_ti_bin[4:6, 1] - 1.96*r1_ti_bin[4:6,2], lwd = 3, col = "gray40") points(1:3, r1_ti_bin[4:6,1], cex = 2, pch = c(15, 16, 17)) legend("bottom", legend = c("Issue Position", "Ideology" , "Party ID"), pch = c(15, 16, 17), cex =1.5, ncol = 3, xpd = NA) # E2cc plot(1,1, col = "white", bty = "n", xlim = c(.5,3.5), ylim = c(-.5,.5), xaxt = "n", yaxt = "n", xlab = "", ylab = "", main = "") segments(x0 = .5, x1 = 3.5, y0 = 0, y1 = 0, col = "gray", lty = 2, lwd = 1) segments(x0 = 1:3, x1 = 1:3, y0 = r1_ti_bin[7:9, 1] + 1.96*r1_ti_bin[7:9,2], y1 = r1_ti_bin[7:9, 1] - 1.96*r1_ti_bin[7:9,2], lwd = 3, col = "gray40") points(1:3, r1_ti_bin[7:9,1], cex = 2, pch = c(15, 16, 17)) dev.off() sink() ```