Replication Code for Lucid Survey Data Analysis
Ideological Asymmetries and the Determinants of Politically-Motivated Reasoning (American Journal of Political Science, 2020)
Brian Guay & Christopher Johnston
6/18/2020
- 1 Introduction
- 2 Setup
- 3 Data Cleanining
- 3.1 Attention Checks
- 3.2 Party ID and Ideology
- 3.3 Experiment 1 (Evidence Interpretation Task)
- 3.4 Experiment 2
- 3.5 Create Congeniality Variables For Experiments 1 and 2
- 3.6 Standardize Outcomes for Experiment 2
- 3.7 Put Continuous Congeniality Measures on 0-1 Scale
- 3.8 Need for Closure
- 3.9 5-item Mini IPIP
- 3.10 Schwartz Values
- 3.11 Create Openness Index (NFC, IPIP, & Schwartz)
- 3.12 Political Identity
- 3.13 Partisan Identity
- 3.14 Numeracy
- 3.15 Political Knowledge
- 3.16 Demographics
- 4 Analysis: Ideological Asymmetries in PMR
- 4.1 Model Nominclature
- 4.2 Create common set of controls for all models
- 4.3 Create Variable for Whether R Has Liberal or Conservative View on the Issue They Received Evidence About
- 4.4 Binary Models (For Main Analysis)
- 4.5 Binary Post-Estimation
- 4.6 Binary Figures
- 4.7 Continuous Models (For Appendix)
- 4.8 Continuous Post-Estimation
- 4.9 Continuous Figures
- 5 Analysis: The Moderating Effect of Epistemic Needs
- 6 Ideological Differences in NFC and Openess Trait Index
- 7 R&R Pooled 3-way Interactions
- 8 R&R NFC and Trait Index Moderation Analysis with No Controls
1 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.
2 Setup
# 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"))3 Data Cleanining
3.1 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).
##
## numeracy qualtrics_detected_mobile trump
## 145 29 124##
## numeracy qualtrics_detected_mobile trump
## 6.859035 1.371807 5.865658
## <NA>
## 85.903500## [1] 18163.2 Party ID and Ideology
# 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") ##
## 0 1 <NA>
## 1 360 0 0
## 2 242 0 0
## 3 122 0 0
## 4 0 0 264
## 5 0 130 0
## 6 0 189 0
## 7 0 274 0
## <NA> 0 0 235# Ideology -------------------------------------------------------------------------------
data$ideo7 <- data$ideo
data$ideo7[data$ideo7 == -99] <- NA
table(data$ideo, data$ideo7, useNA = "always")##
## 1 2 3 4 5 6 7 <NA>
## -99 0 0 0 0 0 0 0 7
## 1 117 0 0 0 0 0 0 0
## 2 0 249 0 0 0 0 0 0
## 3 0 0 172 0 0 0 0 0
## 4 0 0 0 574 0 0 0 0
## 5 0 0 0 0 164 0 0 0
## 6 0 0 0 0 0 280 0 0
## 7 0 0 0 0 0 0 144 0
## <NA> 0 0 0 0 0 0 0 109# Indicator for Conservative (vs. Liberal) -----------------------------------------------
data$con_lib <- ifelse(data$ideo7 < 4, 0,
ifelse(data$ideo7 > 4, 1, NA))
table(data$ideo7, data$con_lib, useNA = "always") ##
## 0 1 <NA>
## 1 117 0 0
## 2 249 0 0
## 3 172 0 0
## 4 0 0 574
## 5 0 164 0
## 6 0 280 0
## 7 0 144 0
## <NA> 0 0 1163.3 Experiment 1 (Evidence Interpretation Task)
Respondents were randomly assigned to received evidence about one of five salient political issues (: a concealed handgun ban, a minimum wage increase, affirmative action, sanctuary cities, or abortion. Respondents were also randomized to receive evidence that supported the liberal or conservative position on the assigned issue, which was achieved by changing the words “increase” and “decrease” in the column names in the evidence table (, see Appendix for full question wording).
3.3.1 Create Variables Indicating Issue and Evidence Direction Condition Assignment
# Create variable for evidence direction condition ---------------------------------------
data$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")##
## Direction Experiments-- Condition B Direction Experiments--Condition A
## A 0 786
## B 784 0
## <NA> 0 0
##
## <NA>
## A 0
## B 0
## <NA> 246# Create variable for issue condition assignment -----------------------------------------
table(data$pol_issue, useNA = "always") # distribution of issue asssignment variable##
## abortion affirmative action
## 364 366
## carrying concealed handguns raising the minimum wage
## 364 357
## sanctuary cities <NA>
## 365 0# Create character versoion of issue assignment variable
data$pol_issue.ch <- as.character(data$pol_issue)
table(data$pol_issue, data$pol_issue.ch, useNA = "always")##
## abortion affirmative action
## abortion 364 0
## affirmative action 0 366
## carrying concealed handguns 0 0
## raising the minimum wage 0 0
## sanctuary cities 0 0
## <NA> 0 0
##
## carrying concealed handguns
## abortion 0
## affirmative action 0
## carrying concealed handguns 364
## raising the minimum wage 0
## sanctuary cities 0
## <NA> 0
##
## raising the minimum wage sanctuary cities <NA>
## abortion 0 0 0
## affirmative action 0 0 0
## carrying concealed handguns 0 0 0
## raising the minimum wage 357 0 0
## sanctuary cities 0 365 0
## <NA> 0 0 0# drop original pol_issue variable
data <- select(data, -c(pol_issue))
# create final issue assignment variable with abbreviated level names
data$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$pol_issue.ch, useNA = "always")##
## abortion affirmative action carrying concealed handguns
## aa 0 366 0
## abort 364 0 0
## gun 0 0 364
## imm 0 0 0
## wage 0 0 0
## <NA> 0 0 0
##
## raising the minimum wage sanctuary cities <NA>
## aa 0 0 0
## abort 0 0 0
## gun 0 0 0
## imm 0 365 0
## wage 357 0 0
## <NA> 0 0 0# remove pol_issue.ch
data <- select(data,-c(pol_issue.ch))
# Create indicator for whether evidence points toward liberal position -------------------
data$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)## , , = aa
##
##
## A B
## 0 160 0
## 1 0 164
##
## , , = abort
##
##
## A B
## 0 164 0
## 1 0 151
##
## , , = gun
##
##
## A B
## 0 0 153
## 1 159 0
##
## , , = imm
##
##
## A B
## 0 0 158
## 1 150 0
##
## , , = wage
##
##
## A B
## 0 0 158
## 1 153 03.3.2 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.
# 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)## exp1_abort_A exp1_aa_A exp1_gun_A exp1_wage_A exp1_imm_A exp1_abort_B
## "integer" "integer" "integer" "integer" "integer" "integer"
## exp1_aa_B exp1_gun_B exp1_wage_B exp1_imm_B
## "integer" "integer" "integer" "integer"# 4 unique values of outcome variables
apply(data[,c(exp1_A, exp1_B)], 2, unique)## $exp1_abort_A
## [1] NA 1 2
##
## $exp1_aa_A
## [1] 2 NA 1 -99
##
## $exp1_gun_A
## [1] NA 2 1 -99
##
## $exp1_wage_A
## [1] NA 2 1 -99
##
## $exp1_imm_A
## [1] NA 1 2 -99
##
## $exp1_abort_B
## [1] NA 1 2
##
## $exp1_aa_B
## [1] NA 1 2 -99
##
## $exp1_gun_B
## [1] NA 2 1 -99
##
## $exp1_wage_B
## [1] NA 2 1 -99
##
## $exp1_imm_B
## [1] NA 1 2 -99# Sum across columns for respondents in evidence direction condition A
data$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## exp1_abort_A exp1_aa_A exp1_gun_A exp1_wage_A exp1_imm_A response_1A
## 3 NA 2 NA NA NA 2
## 5 NA NA NA NA NA NA
## 9 NA 1 NA NA NA 1
## 10 NA NA NA NA NA NA
## 15 NA NA NA NA NA NA
## 18 NA NA 2 NA NA 2# 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$response_1B == 0] <- NA # recode 0 as NA, since NA + NA = 0
head(data[1:200,c(exp1_B, "response_1B")]) # verify method## exp1_abort_B exp1_aa_B exp1_gun_B exp1_wage_B exp1_imm_B response_1B
## 3 NA NA NA NA NA NA
## 5 1 NA NA NA NA 1
## 9 NA NA NA NA NA NA
## 10 NA NA 2 NA NA 2
## 15 NA NA 1 NA NA 1
## 18 NA NA NA NA NA NA# Create one response variable for experiment 1, combining direction conditions A and B.
data$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")##
## 1 2 <NA>
## 1 493 0 403
## 2 0 280 371
## <NA> 0 0 269table(data$exp1_response, data$response_1B, useNA = "always")##
## 1 2 <NA>
## 1 403 0 493
## 2 0 371 280
## <NA> 0 0 2693.3.3 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.
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")##
## 0 1 <NA>
## 864 683 2693.4 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.
3.4.1 Create Variables Indicating Issue and Evidence Direction Condition Assignment
# 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$exp2_condition)##
## A B
## Quality Experiments-- Condition A 778 0
## Quality Experiments-- Condition B 0 781# Create variable indicating the issue each respondent received
data$pol_issue_2.ch <- as.character(data$pol_issue_2)
table(data$pol_issue_2, data$pol_issue_2.ch)##
## abortion affirmative action
## abortion 353 0
## affirmative action 0 369
## carrying concealed handguns 0 0
## raising the minimum wage 0 0
## sanctuary cities 0 0
##
## carrying concealed handguns
## abortion 0
## affirmative action 0
## carrying concealed handguns 362
## raising the minimum wage 0
## sanctuary cities 0
##
## raising the minimum wage sanctuary cities
## abortion 0 0
## affirmative action 0 0
## carrying concealed handguns 0 0
## raising the minimum wage 361 0
## sanctuary cities 0 371# Drop original pol_issue variable
data <- select(data, -c(pol_issue_2))
data$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$exp2_issue)##
## aa abort gun imm wage
## abortion 0 353 0 0 0
## affirmative action 369 0 0 0 0
## carrying concealed handguns 0 0 362 0 0
## raising the minimum wage 0 0 0 0 361
## sanctuary cities 0 0 0 371 0# Drop original pol_issue.ch variable
data <- select(data, -c(pol_issue_2.ch))
# Create indicator for whether evidence points toward liberal position
data$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))3.4.2 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.
# 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$exp2_sample == -99] <- NA
head(data[1:200,c(exp2_A_sample, exp2_B_sample, "exp2_sample")]) # verify method## exp2_abort_A_1 exp2_aa_A_1 exp2_gun_A_1 exp2_wage_A_1 exp2_imm_A_1
## 3 NA NA NA NA NA
## 5 NA NA NA 4 NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## 15 NA NA NA 3 NA
## 18 NA 6 NA NA NA
## exp2_abort_B_1 exp2_aa_B_1 exp2_gun_B_1 exp2_wage_B_1 exp2_imm_B_1
## 3 NA NA 4 NA NA
## 5 NA NA NA NA NA
## 9 NA NA NA 4 NA
## 10 4 NA NA NA NA
## 15 NA NA NA NA NA
## 18 NA NA NA NA NA
## exp2_sample
## 3 4
## 5 4
## 9 4
## 10 4
## 15 3
## 18 6# Reverse code sample size outcome, such that high values indicate size is sufficient
# (note: causal outcome is already coded such that higher values indicate that a causal
# claim can be made)
table(data$exp2_sample)##
## 1 2 3 4 5 6 7
## 69 79 93 698 246 159 207data$exp2_goodSample <- 8 - data$exp2_sample
table(data$exp2_sample, data$exp2_goodSample) ##
## 1 2 3 4 5 6 7
## 1 0 0 0 0 0 0 69
## 2 0 0 0 0 0 79 0
## 3 0 0 0 0 93 0 0
## 4 0 0 0 698 0 0 0
## 5 0 0 246 0 0 0 0
## 6 0 159 0 0 0 0 0
## 7 207 0 0 0 0 0 0# 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$exp2_goodCausal == -99] <- NA #recode seen skipped responses as NA
head(data[1:200,c(exp2_A_causal, exp2_B_causal, "exp2_goodCausal")]) # verify method## exp2_abort_A_2 exp2_aa_A_2 exp2_gun_A_2 exp2_wage_A_2 exp2_imm_A_2
## 3 NA NA NA NA NA
## 5 NA NA NA 5 NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## 15 NA NA NA 6 NA
## 18 NA 7 NA NA NA
## exp2_abort_B_2 exp2_aa_B_2 exp2_gun_B_2 exp2_wage_B_2 exp2_imm_B_2
## 3 NA NA 7 NA NA
## 5 NA NA NA NA NA
## 9 NA NA NA 5 NA
## 10 4 NA NA NA NA
## 15 NA NA NA NA NA
## 18 NA NA NA NA NA
## exp2_goodCausal
## 3 7
## 5 5
## 9 5
## 10 4
## 15 6
## 18 73.5 Create Congeniality Variables For Experiments 1 and 2
Now that we have collapsed outcome variables into a single outcome variable for Experiment 1 and two outcome variables for Experiment 2 (sample size and causal claim outcomes), we next determine whether each respondent received congenial or uncongenial information, based upon both the direction of the information they were randomly assigned to receive and their prior beliefs. As explained in the manuscript and appendix, we operationalize prior beliefs separately in three ways: the respondent’s party identification, self-reported ideology, and position on the issue they received information about. For each operationalization, we create two congeniality variables: a continuous version (e.g., 1 = very uncongenial, 7 = very congenial) and a binary version (e.g., 0 = uncongenial, 1 = congenial). For the binary measure, moderates and non-leaning Independents (for the party ID and ideology operationalizations of congeniality, respectively), are coded as NA. For the continuous measures, they are coded as 4, at the center of the continuous measures, which range from 1-7. Issue positions are measured on a 6-point scale, so the issue position operationalization of congeniality does not face a similar issue.
3.5.1 Experiment 1
# Party ID -------------------------------------------
# Binary
data$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$pid7 == 4, NA, 0)))
head(data[,c("exp1_congenial_pid_binary", "exp1_liberal_evidence", "pid7")]) # verify## exp1_congenial_pid_binary exp1_liberal_evidence pid7
## 3 1 0 5
## 5 0 1 6
## 9 NA 0 4
## 10 0 0 1
## 15 NA 0 4
## 18 1 1 2# Continuous
data$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## exp1_congenial_pid_cont exp1_liberal_evidence pid7
## 3 5 0 5
## 5 2 1 6
## 9 4 0 4
## 10 1 0 1
## 15 4 0 4
## 18 6 1 2# 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## exp1_congenial_ideo_binary exp1_liberal_evidence ideo7
## 3 0 0 1
## 5 0 1 5
## 9 0 0 1
## 10 0 0 1
## 15 0 0 3
## 18 1 1 3
## 19 1 1 3
## 23 1 1 2
## 25 1 1 3
## 28 NA 0 4# 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$exp1_congenial_ideo_cont))
data[1:10,c("exp1_congenial_ideo_cont", "exp1_liberal_evidence", "ideo7")] # verify## exp1_congenial_ideo_cont exp1_liberal_evidence ideo7
## 3 1 0 1
## 5 3 1 5
## 9 1 0 1
## 10 1 0 1
## 15 3 0 3
## 18 5 1 3
## 19 5 1 3
## 23 6 1 2
## 25 5 1 3
## 28 4 0 4# Issue Position -------------------------------------------------------------------------
# Continuous
# distributions of issue position variables (note that skipped responses = -99)
apply(data[,c("issue_gun", "issue_aa", "issue_abort", "issue_imm", "issue_wage")],
2, function(x) table(x, useNA = "always"))## issue_gun issue_aa issue_abort issue_imm issue_wage
## -99 3 5 3 3 4
## 1 581 394 566 435 198
## 2 172 186 185 181 116
## 3 189 270 211 215 180
## 4 220 337 191 203 228
## 5 242 240 183 178 261
## 6 312 287 380 504 732
## <NA> 97 97 97 97 97# recode NAs
data$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$issue_wage == -99] <- NA
# verify
apply(data[,c("issue_gun", "issue_aa", "issue_abort", "issue_imm", "issue_wage")],
2, function(x) table(x, useNA = "always"))## issue_gun issue_aa issue_abort issue_imm issue_wage
## 1 581 394 566 435 198
## 2 172 186 185 181 116
## 3 189 270 211 215 180
## 4 220 337 191 203 228
## 5 242 240 183 178 261
## 6 312 287 380 504 732
## <NA> 100 102 100 100 101# recode such that higher values indicate liberal positions
data$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## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0##
## Reliability analysis
## Call: psych::alpha(x = data[, issue_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.69 0.42 3.6 0.0085 3.9 1.3 0.35
##
## lower alpha upper 95% confidence boundaries
## 0.76 0.78 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## gun_lib 0.77 0.78 0.62 0.47 3.6 0.0091 0.070 0.38
## wage_lib 0.69 0.69 0.65 0.35 2.2 0.0120 0.012 0.33
## aa_lib 0.78 0.78 0.65 0.47 3.6 0.0093 0.073 0.35
## imm_lib 0.76 0.76 0.60 0.45 3.2 0.0098 0.080 0.37
## wage_lib.1 0.69 0.69 0.65 0.35 2.2 0.0120 0.012 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## gun_lib 1716 0.66 0.65 0.55 0.45 3.8 1.9
## wage_lib 1715 0.83 0.84 0.68 0.71 4.4 1.8
## aa_lib 1714 0.63 0.64 0.51 0.43 3.4 1.8
## imm_lib 1716 0.71 0.69 0.61 0.50 3.4 2.0
## wage_lib.1 1715 0.83 0.84 0.68 0.71 4.4 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## gun_lib 0.18 0.14 0.13 0.11 0.10 0.34 0.06
## wage_lib 0.12 0.07 0.10 0.13 0.15 0.43 0.06
## aa_lib 0.23 0.11 0.16 0.20 0.14 0.17 0.06
## imm_lib 0.29 0.10 0.12 0.13 0.11 0.25 0.06
## wage_lib.1 0.12 0.07 0.10 0.13 0.15 0.43 0.06# 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##
## 1 2 3 4 5 6
## 0 398 195 205 0 0 0
## 1 0 0 0 182 190 396# 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")## [1] 0.6472544cor(data$exp1_congenial_pid_cont.s,
data$exp1_congenial_issue_cont.s,
use = "complete.obs")## [1] 0.4415604cor(data$exp1_congenial_ideo_cont.s,
data$exp1_congenial_issue_cont.s,
use = "complete.obs")## [1] 0.40171063.5.2 Experiment 2
# 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$exp2_congenial_issue_cont < 4, 0, NA))3.6 Standardize Outcomes for Experiment 2
# Unstandardized version of DVs
data$exp2_goodSample_uns <- data$exp2_goodSample
data$exp2_goodCausal_uns <- data$exp2_goodCausal
# Drop original variables
data <- data %>% dplyr::select(-exp2_goodSample, -exp2_goodCausal)
colnames(data)[grep(colnames(data), pattern = "good")] # verify## [1] "exp2_goodSample_uns" "exp2_goodCausal_uns"# Standardize DVs
data$exp2_goodSample <- as.numeric(scale(data$exp2_goodSample_uns))
data$exp2_goodCausal <- as.numeric(scale(data$exp2_goodCausal_uns))
table(data$exp2_goodSample, data$exp2_goodSample_uns)##
## 1 2 3 4 5 6 7
## -1.70055014543059 207 0 0 0 0 0 0
## -1.02873321329538 0 159 0 0 0 0 0
## -0.356916281160165 0 0 246 0 0 0 0
## 0.314900650975048 0 0 0 698 0 0 0
## 0.986717583110261 0 0 0 0 93 0 0
## 1.65853451524547 0 0 0 0 0 79 0
## 2.33035144738069 0 0 0 0 0 0 69table(data$exp2_goodCausal, data$exp2_goodCausal_uns)##
## 1 2 3 4 5 6 7
## -2.32254579302489 85 0 0 0 0 0 0
## -1.71020223496628 0 90 0 0 0 0 0
## -1.09785867690767 0 0 96 0 0 0 0
## -0.485515118849059 0 0 0 338 0 0 0
## 0.12682843920955 0 0 0 0 352 0 0
## 0.739171997268159 0 0 0 0 0 348 0
## 1.35151555532677 0 0 0 0 0 0 2363.7 Put Continuous Congeniality Measures on 0-1 Scale
data$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))## exp1_congenial_pid_cont
## exp1_congenial_pid_cont.01 1 2 3 4 5 6 7
## 0 320 0 0 0 0 0 0
## 0.166666666666667 0 227 0 0 0 0 0
## 0.333333333333333 0 0 108 0 0 0 0
## 0.5 0 0 0 261 0 0 0
## 0.666666666666667 0 0 0 0 143 0 0
## 0.833333333333333 0 0 0 0 0 202 0
## 1 0 0 0 0 0 0 309with(data,table(exp1_congenial_ideo_cont.01, exp1_congenial_ideo_cont))## exp1_congenial_ideo_cont
## exp1_congenial_ideo_cont.01 1 2 3 4 5 6 7
## 0 107 0 0 0 0 0 0
## 0.166666666666667 0 256 0 0 0 0 0
## 0.333333333333333 0 0 153 0 0 0 0
## 0.5 0 0 0 519 0 0 0
## 0.666666666666667 0 0 0 0 161 0 0
## 0.833333333333333 0 0 0 0 0 232 0
## 1 0 0 0 0 0 0 138with(data,table(exp1_congenial_issue_cont.01, exp1_congenial_issue_cont))## exp1_congenial_issue_cont
## exp1_congenial_issue_cont.01 1 2 3 4 5 6
## 0 398 0 0 0 0 0
## 0.2 0 195 0 0 0 0
## 0.4 0 0 205 0 0 0
## 0.6 0 0 0 182 0 0
## 0.8 0 0 0 0 190 0
## 1 0 0 0 0 0 396with(data,table(exp2_congenial_pid_cont.01, exp2_congenial_pid_cont))## exp2_congenial_pid_cont
## exp2_congenial_pid_cont.01 1 2 3 4 5 6 7
## 0 324 0 0 0 0 0 0
## 0.166666666666667 0 204 0 0 0 0 0
## 0.333333333333333 0 0 123 0 0 0 0
## 0.5 0 0 0 260 0 0 0
## 0.666666666666667 0 0 0 0 128 0 0
## 0.833333333333333 0 0 0 0 0 220 0
## 1 0 0 0 0 0 0 300with(data,table(exp2_congenial_ideo_cont.01, exp2_congenial_ideo_cont))## exp2_congenial_ideo_cont
## exp2_congenial_ideo_cont.01 1 2 3 4 5 6 7
## 0 125 0 0 0 0 0 0
## 0.166666666666667 0 232 0 0 0 0 0
## 0.333333333333333 0 0 159 0 0 0 0
## 0.5 0 0 0 516 0 0 0
## 0.666666666666667 0 0 0 0 152 0 0
## 0.833333333333333 0 0 0 0 0 253 0
## 1 0 0 0 0 0 0 118with(data,table(exp2_congenial_issue_cont.01, exp2_congenial_issue_cont))## exp2_congenial_issue_cont
## exp2_congenial_issue_cont.01 1 2 3 4 5 6
## 0 413 0 0 0 0 0
## 0.2 0 155 0 0 0 0
## 0.4 0 0 196 0 0 0
## 0.6 0 0 0 204 0 0
## 0.8 0 0 0 0 175 0
## 1 0 0 0 0 0 4133.8 Need for Closure
# 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"))## nfc_1 nfc_2 nfc_3 nfc_4 nfc_5 nfc_6 nfc_7 nfc_8 nfc_9 nfc_10
## -99 5 4 1 2 3 5 7 4 16 2
## 1 70 137 56 48 169 78 65 40 31 48
## 2 85 178 109 87 258 123 90 73 71 101
## 3 163 309 197 182 374 285 223 176 155 241
## 4 415 409 437 409 446 490 538 492 478 489
## 5 472 285 428 449 221 306 360 452 443 366
## 6 368 256 350 400 106 290 294 340 383 330
## <NA> 238 238 238 239 239 239 239 239 239 239data[,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])##
## Reliability analysis
## Call: psych::alpha(x = data[, nfc_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.85 0.35 5.4 0.0056 4.2 0.85 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.84 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## nfc_1 0.83 0.84 0.84 0.36 5.1 0.0060 0.0128 0.34
## nfc_2 0.83 0.83 0.84 0.36 5.1 0.0059 0.0132 0.35
## nfc_3 0.82 0.82 0.83 0.34 4.7 0.0063 0.0102 0.33
## nfc_4 0.82 0.82 0.83 0.34 4.6 0.0064 0.0130 0.32
## nfc_5 0.84 0.85 0.85 0.38 5.5 0.0056 0.0101 0.35
## nfc_6 0.83 0.83 0.83 0.35 4.9 0.0061 0.0133 0.33
## nfc_7 0.83 0.83 0.83 0.35 4.9 0.0061 0.0135 0.34
## nfc_8 0.82 0.82 0.82 0.34 4.6 0.0064 0.0089 0.33
## nfc_9 0.82 0.82 0.82 0.34 4.6 0.0064 0.0079 0.33
## nfc_10 0.82 0.82 0.82 0.34 4.5 0.0065 0.0126 0.32
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## nfc_1 1573 0.58 0.58 0.51 0.46 4.4 1.3
## nfc_2 1574 0.61 0.59 0.52 0.47 3.8 1.5
## nfc_3 1577 0.68 0.68 0.65 0.58 4.3 1.3
## nfc_4 1575 0.70 0.70 0.66 0.60 4.5 1.3
## nfc_5 1574 0.49 0.48 0.38 0.35 3.4 1.4
## nfc_6 1572 0.63 0.63 0.57 0.52 4.1 1.4
## nfc_7 1570 0.63 0.63 0.57 0.53 4.2 1.3
## nfc_8 1573 0.69 0.70 0.69 0.60 4.4 1.2
## nfc_9 1561 0.71 0.72 0.71 0.62 4.5 1.2
## nfc_10 1575 0.72 0.72 0.69 0.64 4.3 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## nfc_1 0.04 0.05 0.10 0.26 0.30 0.23 0.13
## nfc_2 0.09 0.11 0.20 0.26 0.18 0.16 0.13
## nfc_3 0.04 0.07 0.12 0.28 0.27 0.22 0.13
## nfc_4 0.03 0.06 0.12 0.26 0.29 0.25 0.13
## nfc_5 0.11 0.16 0.24 0.28 0.14 0.07 0.13
## nfc_6 0.05 0.08 0.18 0.31 0.19 0.18 0.13
## nfc_7 0.04 0.06 0.14 0.34 0.23 0.19 0.14
## nfc_8 0.03 0.05 0.11 0.31 0.29 0.22 0.13
## nfc_9 0.02 0.05 0.10 0.31 0.28 0.25 0.14
## nfc_10 0.03 0.06 0.15 0.31 0.23 0.21 0.133.9 5-item Mini IPIP
pip_vars_original <- paste("pip", 1:5, sep = "_")
apply(data[,pip_vars_original], 2, function(x) table(x, useNA = "always"))## pip_1 pip_2 pip_3 pip_4 pip_5
## -99 28 10 34 30 6
## 1 42 245 248 553 30
## 2 129 427 414 504 85
## 3 305 511 500 217 276
## 4 636 281 313 196 736
## 5 435 101 65 74 441
## <NA> 241 241 242 242 242# 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"))## pip_1 pip_2 pip_3 pip_4 pip_5
## 1 42 245 248 553 30
## 2 129 427 414 504 85
## 3 305 511 500 217 276
## 4 636 281 313 196 736
## 5 435 101 65 74 441
## <NA> 269 251 276 272 248# 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##
## 1 2 3 4 5
## 1 0 0 0 0 42
## 2 0 0 0 129 0
## 3 0 0 305 0 0
## 4 0 636 0 0 0
## 5 435 0 0 0 0# 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##
## 1 2 3 4 5
## 1 0 0 0 0 30
## 2 0 0 0 85 0
## 3 0 0 276 0 0
## 4 0 736 0 0 0
## 5 441 0 0 0 0# 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##
## Reliability analysis
## Call: psych::alpha(x = data[, pip_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.73 0.71 0.35 2.7 0.01 2.4 0.74 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.73 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pip_1.r 0.68 0.68 0.64 0.35 2.1 0.012 0.0108 0.31
## pip_2 0.69 0.69 0.64 0.36 2.3 0.012 0.0067 0.35
## pip_3 0.66 0.67 0.62 0.33 2.0 0.013 0.0107 0.31
## pip_4 0.67 0.67 0.63 0.33 2.0 0.013 0.0159 0.28
## pip_5.r 0.70 0.70 0.67 0.36 2.3 0.012 0.0168 0.34
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pip_1.r 1547 0.68 0.69 0.59 0.48 2.2 1.01
## pip_2 1565 0.68 0.67 0.56 0.46 2.7 1.12
## pip_3 1540 0.73 0.72 0.63 0.53 2.7 1.09
## pip_4 1544 0.74 0.72 0.62 0.52 2.2 1.19
## pip_5.r 1568 0.63 0.66 0.52 0.44 2.1 0.92
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pip_1.r 0.28 0.41 0.20 0.08 0.03 0.15
## pip_2 0.16 0.27 0.33 0.18 0.06 0.14
## pip_3 0.16 0.27 0.32 0.20 0.04 0.15
## pip_4 0.36 0.33 0.14 0.13 0.05 0.15
## pip_5.r 0.28 0.47 0.18 0.05 0.02 0.14# Create mean mini-IPIP score
data$pip_mean <- rowMeans(data[,pip_vars], na.rm=T)3.10 Schwartz Values
# Direction of original coding: higher values correspond to...
# sv1 = open
# sv2 = closed
# sv3 = closed
# sv4 = open
# sv5 = closed
sv_vars_original <- c("sv_1", "sv_2", "sv_3", "sv_4", "sv_5")
apply(data[,sv_vars_original], 2, table)## sv_1 sv_2 sv_3 sv_4 sv_5
## -99 7 3 7 4 6
## 1 91 74 149 412 206
## 2 273 215 284 361 284
## 3 502 396 436 390 376
## 4 409 439 376 259 364
## 5 289 444 318 144 334data[,sv_vars_original][data[,sv_vars_original] == -99] <- NA
apply(data[,sv_vars_original], 2, table) # verify## sv_1 sv_2 sv_3 sv_4 sv_5
## 1 91 74 149 412 206
## 2 273 215 284 361 284
## 3 502 396 436 390 376
## 4 409 439 376 259 364
## 5 289 444 318 144 334# Variables that need to be recoded are 1 and 4:
data$sv_1.r <- 6 - data$sv_1
table(data$sv_1, data$sv_1.r)##
## 1 2 3 4 5
## 1 0 0 0 0 91
## 2 0 0 0 273 0
## 3 0 0 502 0 0
## 4 0 409 0 0 0
## 5 289 0 0 0 0data$sv_4.r <- 6 - data$sv_4
table(data$sv_4, data$sv_4.r)##
## 1 2 3 4 5
## 1 0 0 0 0 412
## 2 0 0 0 361 0
## 3 0 0 390 0 0
## 4 0 259 0 0 0
## 5 144 0 0 0 0sv_vars <- c("sv_1.r", "sv_2", "sv_3", "sv_4.r", "sv_5")
psych::alpha(data[,sv_vars])## Warning in psych::alpha(data[, sv_vars]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( sv_1.r ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data[, sv_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.43 0.43 0.48 0.13 0.76 0.021 3.2 0.68 0.1
##
## lower alpha upper 95% confidence boundaries
## 0.39 0.43 0.47
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sv_1.r 0.48 0.49 0.47 0.194 0.96 0.020 0.033 0.2040
## sv_2 0.30 0.30 0.34 0.096 0.42 0.027 0.047 -0.0027
## sv_3 0.30 0.30 0.34 0.098 0.44 0.027 0.040 0.0497
## sv_4.r 0.39 0.37 0.39 0.130 0.60 0.023 0.056 0.0979
## sv_5 0.40 0.39 0.43 0.140 0.65 0.023 0.053 0.1034
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sv_1.r 1564 0.40 0.42 0.17 0.066 2.7 1.1
## sv_2 1568 0.62 0.63 0.51 0.338 3.6 1.2
## sv_3 1563 0.63 0.63 0.51 0.326 3.3 1.2
## sv_4.r 1566 0.56 0.56 0.39 0.210 3.4 1.3
## sv_5 1564 0.56 0.53 0.32 0.199 3.2 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## sv_1.r 0.18 0.26 0.32 0.17 0.06 0.14
## sv_2 0.05 0.14 0.25 0.28 0.28 0.14
## sv_3 0.10 0.18 0.28 0.24 0.20 0.14
## sv_4.r 0.09 0.17 0.25 0.23 0.26 0.14
## sv_5 0.13 0.18 0.24 0.23 0.21 0.14# Create mean variable
data$sv_mean <- rowMeans(data[,sv_vars], na.rm=T)3.11 Create Openness Index (NFC, IPIP, & Schwartz)
We create a mean openness trait index by averaging all items from each scale.
# Check consistency among 3 mean variables
psych::alpha(data[,c("nfc_mean", "pip_mean", "sv_mean")])##
## Reliability analysis
## Call: psych::alpha(x = data[, c("nfc_mean", "pip_mean", "sv_mean")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.61 0.53 0.34 1.5 0.017 3.3 0.57 0.37
##
## lower alpha upper 95% confidence boundaries
## 0.56 0.6 0.63
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## nfc_mean 0.54 0.54 0.37 0.37 1.17 0.022 NA 0.37
## pip_mean 0.61 0.61 0.44 0.44 1.60 0.018 NA 0.44
## sv_mean 0.34 0.34 0.20 0.20 0.51 0.031 NA 0.20
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## nfc_mean 1578 0.77 0.73 0.52 0.38 4.2 0.85
## pip_mean 1575 0.69 0.70 0.44 0.33 2.4 0.74
## sv_mean 1569 0.79 0.81 0.67 0.53 3.2 0.68# 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$trait_index))3.12 Political Identity
# recode NAs
data[,paste("pol_id_", 1:4, sep = "")][data[,paste("pol_id_", 1:4, sep = "")] == -99] <- NA
# high values correspond to strong political identity for items 2 and 4. So recode items
# 1 and 3.
data$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$pol_id_4
pol_id_vars <- c("pol_id_1.r", "pol_id_2.r", "pol_id_3.r", "pol_id_4.r")
psych::alpha(data[,pol_id_vars]) #good##
## Reliability analysis
## Call: psych::alpha(x = data[, pol_id_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.77 0.51 4.1 0.0077 3 1 0.47
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pol_id_1.r 0.77 0.78 0.73 0.54 3.5 0.0095 0.02510 0.46
## pol_id_2.r 0.73 0.73 0.64 0.47 2.7 0.0112 0.00028 0.46
## pol_id_3.r 0.79 0.79 0.74 0.56 3.8 0.0086 0.01861 0.49
## pol_id_4.r 0.71 0.71 0.63 0.46 2.5 0.0116 0.00052 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pol_id_1.r 1572 0.76 0.76 0.63 0.57 2.7 1.2
## pol_id_2.r 1573 0.82 0.83 0.78 0.67 3.3 1.3
## pol_id_3.r 1564 0.75 0.74 0.59 0.53 3.0 1.3
## pol_id_4.r 1567 0.84 0.84 0.80 0.69 3.1 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## pol_id_1.r 0.21 0.24 0.28 0.17 0.10 0.13
## pol_id_2.r 0.12 0.15 0.25 0.31 0.18 0.13
## pol_id_3.r 0.17 0.22 0.24 0.23 0.15 0.14
## pol_id_4.r 0.13 0.17 0.26 0.29 0.14 0.14# create mean score
data$pol_id <- rowMeans(data[,pol_id_vars], na.rm=T)
data$pol_id.s <- as.numeric(scale(data$pol_id))3.13 Partisan Identity
# 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"))## $huddy_import
## x
## -99 1 2 3 4 <NA>
## 2 90 411 505 308 500
##
## $huddy_describe
## x
## -99 1 2 3 4 <NA>
## 7 43 363 629 274 500
##
## $huddy_we
## x
## -99 1 2 3 4 5 <NA>
## 2 211 256 341 320 186 500
##
## $huddy_think
## x
## -99 1 2 3 4 <NA>
## 3 44 253 556 460 500# recode NAs
data[,huddy_vars][data[,huddy_vars] < 0] <- NA
# create standardized mean scale
data$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$huddy_think, max = 4)
# verify
apply(data[,paste(huddy_vars, ".01", sep = "")], 2, range, na.rm=T)## huddy_import.01 huddy_describe.01 huddy_we.01 huddy_think.01
## [1,] 0 0 0 0
## [2,] 1 1 1 1psych::alpha(data[,c("huddy_import.01", "huddy_describe.01",
"huddy_we.01", "huddy_think.01")])##
## Reliability analysis
## Call: psych::alpha(x = data[, c("huddy_import.01", "huddy_describe.01",
## "huddy_we.01", "huddy_think.01")])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.89 0.87 0.66 7.9 0.0046 0.6 0.25 0.65
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## huddy_import.01 0.82 0.83 0.78 0.62 5.0 0.0072 0.0056
## huddy_describe.01 0.84 0.84 0.78 0.64 5.3 0.0067 0.0048
## huddy_we.01 0.90 0.90 0.86 0.74 8.7 0.0042 0.0028
## huddy_think.01 0.84 0.85 0.81 0.65 5.7 0.0066 0.0170
## med.r
## huddy_import.01 0.60
## huddy_describe.01 0.60
## huddy_we.01 0.72
## huddy_think.01 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## huddy_import.01 1314 0.90 0.90 0.88 0.81 0.59 0.29
## huddy_describe.01 1309 0.88 0.89 0.86 0.79 0.62 0.26
## huddy_we.01 1314 0.81 0.80 0.68 0.64 0.50 0.32
## huddy_think.01 1313 0.87 0.87 0.81 0.77 0.70 0.27
##
## Non missing response frequency for each item
## 0 0.25 0.333333333333333 0.5 0.666666666666667 0.75 1
## huddy_import.01 0.07 0.00 0.31 0.00 0.38 0.00 0.23
## huddy_describe.01 0.03 0.00 0.28 0.00 0.48 0.00 0.21
## huddy_we.01 0.16 0.19 0.00 0.26 0.00 0.24 0.14
## huddy_think.01 0.03 0.00 0.19 0.00 0.42 0.00 0.35
## miss
## huddy_import.01 0.28
## huddy_describe.01 0.28
## huddy_we.01 0.28
## huddy_think.01 0.28data$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))3.14 Numeracy
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)## rasch_1 rasch_2 rasch_3 rasch_4 rasch_5 rasch_6
## "character" "character" "character" "character" "character" "character"
## rasch_7
## "character"# recode character vectors as numeric vectors
data$rasch_1.r <- as.numeric(as.character(data$rasch_1))## Warning: NAs introduced by coerciondata$rasch_2.r <- as.numeric(as.character(data$rasch_2))## Warning: NAs introduced by coerciondata$rasch_3.r <- as.numeric(as.character(data$rasch_3))
data$rasch_4.r <- as.numeric(as.character(data$rasch_4))
data$rasch_5.r <- as.numeric(as.character(data$rasch_5))## Warning: NAs introduced by coerciondata$rasch_6.r <- as.numeric(as.character(data$rasch_6))
data$rasch_7.r <- as.numeric(as.character(data$rasch_7))## Warning: NAs introduced by coercionrasch_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))## rasch_1_corr rasch_2_corr rasch_3_corr rasch_4_corr rasch_5_corr rasch_6_corr
## 0 0.42 0.8 0.79 0.91 0.33 0.29
## 1 0.58 0.2 0.21 0.09 0.67 0.71
## rasch_7_corr
## 0 0.52
## 1 0.48psych::alpha(data[,rasch_corr_vars])##
## Reliability analysis
## Call: psych::alpha(x = data[, rasch_corr_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.68 0.69 0.66 0.24 2.2 0.011 0.42 0.26 0.23
##
## lower alpha upper 95% confidence boundaries
## 0.65 0.68 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## rasch_1_corr 0.64 0.65 0.62 0.24 1.9 0.013 0.0049 0.22
## rasch_2_corr 0.62 0.63 0.60 0.22 1.7 0.013 0.0043 0.22
## rasch_3_corr 0.64 0.65 0.62 0.24 1.9 0.013 0.0041 0.22
## rasch_4_corr 0.65 0.65 0.61 0.24 1.9 0.013 0.0036 0.23
## rasch_5_corr 0.67 0.68 0.65 0.26 2.1 0.012 0.0032 0.26
## rasch_6_corr 0.65 0.66 0.63 0.25 2.0 0.013 0.0055 0.26
## rasch_7_corr 0.61 0.63 0.59 0.22 1.7 0.014 0.0045 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## rasch_1_corr 1651 0.62 0.58 0.47 0.39 0.581 0.49
## rasch_2_corr 1650 0.62 0.65 0.56 0.46 0.203 0.40
## rasch_3_corr 1596 0.57 0.59 0.49 0.38 0.211 0.41
## rasch_4_corr 1579 0.52 0.59 0.49 0.39 0.087 0.28
## rasch_5_corr 1632 0.52 0.50 0.34 0.28 0.673 0.47
## rasch_6_corr 1633 0.58 0.56 0.43 0.36 0.708 0.45
## rasch_7_corr 1580 0.68 0.65 0.57 0.48 0.485 0.50
##
## Non missing response frequency for each item
## 0 1 miss
## rasch_1_corr 0.42 0.58 0.09
## rasch_2_corr 0.80 0.20 0.09
## rasch_3_corr 0.79 0.21 0.12
## rasch_4_corr 0.91 0.09 0.13
## rasch_5_corr 0.33 0.67 0.10
## rasch_6_corr 0.29 0.71 0.10
## rasch_7_corr 0.52 0.48 0.13# create mean numeracy score
data$numeracy <- rowMeans(data[,rasch_corr_vars], na.rm=T)
data$numeracy.s <- as.numeric(scale(data$numeracy))3.15 Political Knowledge
apply(data[,c("pk_speaker", "pk_senterm", "pk_cj", "pk_merkel", "pk_putin")], 2,
function(x) table(x, useNA = "always"))## pk_speaker pk_senterm pk_cj pk_merkel pk_putin
## -99 5 4 9 5 3
## 1 166 280 35 93 11
## 2 30 351 60 948 1556
## 3 27 756 817 33 17
## 4 1210 94 52 101 14
## 888 272 225 734 527 106
## <NA> 106 106 109 109 109data$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)))## pk_speaker_corr pk_senterm_corr pk_cj_corr pk_merkel_corr pk_putin_corr
## 0 0.2903226 0.5568581 0.5188457 0.4430082 0.08685446
## 1 0.7096774 0.4431419 0.4811543 0.5569918 0.91314554data$pk_mean <- rowMeans(data[,pk_vars])
data$pk_mean.s <- as.numeric(scale(data$pk_mean))
cor(data$pk_mean, data$pk_mean.s, use = "complete.obs")## [1] 1psych::alpha(data[,pk_vars])##
## Reliability analysis
## Call: psych::alpha(x = data[, pk_vars])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.72 0.7 0.34 2.6 0.0095 0.62 0.31 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.73 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## pk_speaker_corr 0.68 0.67 0.63 0.34 2.0 0.011 0.0225 0.32
## pk_senterm_corr 0.71 0.70 0.66 0.37 2.4 0.010 0.0163 0.35
## pk_cj_corr 0.63 0.64 0.58 0.30 1.8 0.013 0.0075 0.30
## pk_merkel_corr 0.62 0.63 0.57 0.30 1.7 0.014 0.0075 0.30
## pk_putin_corr 0.73 0.73 0.69 0.40 2.7 0.010 0.0113 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## pk_speaker_corr 1705 0.69 0.70 0.58 0.48 0.71 0.45
## pk_senterm_corr 1706 0.67 0.64 0.48 0.42 0.44 0.50
## pk_cj_corr 1698 0.78 0.76 0.70 0.60 0.48 0.50
## pk_merkel_corr 1702 0.79 0.77 0.72 0.61 0.56 0.50
## pk_putin_corr 1704 0.49 0.58 0.40 0.34 0.91 0.28
##
## Non missing response frequency for each item
## 0 1 miss
## pk_speaker_corr 0.29 0.71 0.06
## pk_senterm_corr 0.56 0.44 0.06
## pk_cj_corr 0.52 0.48 0.06
## pk_merkel_corr 0.44 0.56 0.06
## pk_putin_corr 0.09 0.91 0.063.16 Demographics
# Gender
table(data$gender, useNA = "always")##
## -99 1 2 <NA>
## 2 725 821 268data$female <- ifelse(data$gender == 2, 1, 0)
data$female[data$female == -99] <- NA
table(data$female, useNA = "always")##
## 0 1 <NA>
## 727 821 268table(data$gender, data$female)##
## 0 1
## -99 2 0
## 1 725 0
## 2 0 821# Race
# note: respondents were instructed to check all that apply: 1 = White, 2 = Black,
# 3 = Hispanic, 4 = Asian, 5 = Native American, 6 = Middle Eastern,
# 7 = Mixed race, 8 = Other
# recode NAs
data$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))4 Analysis: Ideological Asymmetries in PMR
4.1 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
- where:
- 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
4.2 Create common set of controls for all models
4.3 Create Variable for Whether R Has Liberal or Conservative View on the Issue They Received Evidence About
Higher values indicate more conservative-leaning evidence.
# 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$exp1_issue_lr, useNA = "always")##
## 1 2 3 4 5 6 <NA>
## 522 222 231 199 188 353 101# binary
data$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")##
## 0 1 <NA>
## 1 522 0 0
## 2 222 0 0
## 3 231 0 0
## 4 0 199 0
## 5 0 188 0
## 6 0 353 0
## <NA> 0 0 101# 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")##
## 0 1 <NA>
## 1 534 0 0
## 2 196 0 0
## 3 221 0 0
## 4 0 219 0
## 5 0 173 0
## 6 0 373 0
## <NA> 0 0 1004.4 Binary Models (For Main Analysis)
4.5 Binary Post-Estimation
4.6 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.
## quartz_off_screen
## 2## quartz_off_screen
## 2## quartz_off_screen
## 2## quartz_off_screen
## 24.7 Continuous Models (For Appendix)
4.8 Continuous Post-Estimation
4.9 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.
## quartz_off_screen
## 2## quartz_off_screen
## 2## quartz_off_screen
## 2## quartz_off_screen
## 25 Analysis: The Moderating Effect of Epistemic Needs
5.1 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.
# 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)5.2 Trait Index
In these models we subsitute the Openness Trait Index for NFC.
# 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)5.3 Figures
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()## quartz_off_screen
## 2# 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()## quartz_off_screen
## 2# 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()## quartz_off_screen
## 2# 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()## quartz_off_screen
## 2# 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()## quartz_off_screen
## 26 Ideological Differences in NFC and Openess Trait Index
## [1] 0.1490705## [1] 0.2586452## [1] 0.06499643## [1] 0.14312157 R&R Pooled 3-way Interactions
# 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$out_num != "e1",], clusters = id)
summary(pooled_d_iss_robust)##
## Call:
## lm_robust(formula = outcome ~ congenial_issue_cont.s * nfc_mean.s +
## congenial_issue_cont.s * pol_id.s * pk_mean.s + age.s + female +
## edu_hs + edu_somecollege + edu_college + edu_grad + hispanic +
## nonhisp_black + income.s + issue, data = new[new$out_num !=
## "e1", ], clusters = id)
##
## Standard error type: CR2
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.390e-01 0.038909 13.853749
## congenial_issue_cont.s 4.242e-02 0.005662 7.492128
## nfc_mean.s 1.150e-02 0.005536 2.077016
## pol_id.s 1.205e-02 0.005440 2.216006
## pk_mean.s -6.893e-03 0.005642 -1.221719
## age.s -2.066e-03 0.005411 -0.381857
## female -3.897e-02 0.010410 -3.743837
## edu_hs 2.082e-02 0.038608 0.539288
## edu_somecollege 1.343e-02 0.038663 0.347292
## edu_college -1.637e-02 0.039605 -0.413303
## edu_grad -5.977e-03 0.042260 -0.141428
## hispanic 3.714e-02 0.016214 2.290416
## nonhisp_black 5.584e-02 0.017000 3.284586
## income.s 5.420e-03 0.005434 0.997285
## issueabort -6.607e-03 0.015353 -0.430323
## issuegun -6.192e-03 0.015195 -0.407514
## issueimm 1.142e-04 0.014839 0.007699
## issuewage -3.228e-03 0.015222 -0.212035
## congenial_issue_cont.s:nfc_mean.s -4.060e-03 0.005600 -0.724957
## congenial_issue_cont.s:pol_id.s 7.702e-05 0.005711 0.013485
## congenial_issue_cont.s:pk_mean.s 1.207e-02 0.005708 2.114640
## pol_id.s:pk_mean.s -7.940e-03 0.004924 -1.612377
## congenial_issue_cont.s:pol_id.s:pk_mean.s 1.282e-02 0.005371 2.386848
## Pr(>|t|) CI Lower CI Upper
## (Intercept) 1.149e-16 0.4603392 0.617729
## congenial_issue_cont.s 2.033e-13 0.0313018 0.053533
## nfc_mean.s 3.843e-02 0.0006156 0.022380
## pol_id.s 2.714e-02 0.0013670 0.022742
## pk_mean.s 2.223e-01 -0.0179740 0.004188
## age.s 7.027e-01 -0.0126916 0.008559
## female 1.915e-04 -0.0594030 -0.018546
## edu_hs 5.928e-01 -0.0573057 0.098948
## edu_somecollege 7.304e-01 -0.0649524 0.091807
## edu_college 6.816e-01 -0.0963857 0.063648
## edu_grad 8.881e-01 -0.0907827 0.078829
## hispanic 2.303e-02 0.0051660 0.069106
## nonhisp_black 1.173e-03 0.0223504 0.089324
## income.s 3.191e-01 -0.0052564 0.016096
## issueabort 6.671e-01 -0.0367640 0.023551
## issuegun 6.838e-01 -0.0360349 0.023650
## issueimm 9.939e-01 -0.0290288 0.029257
## issuewage 8.322e-01 -0.0331260 0.026671
## congenial_issue_cont.s:nfc_mean.s 4.691e-01 -0.0150845 0.006965
## congenial_issue_cont.s:pol_id.s 9.892e-01 -0.0111589 0.011313
## congenial_issue_cont.s:pk_mean.s 3.503e-02 0.0008519 0.023290
## pol_id.s:pk_mean.s 1.082e-01 -0.0176402 0.001760
## congenial_issue_cont.s:pol_id.s:pk_mean.s 1.801e-02 0.0022230 0.023418
## DF
## (Intercept) 39.09
## congenial_issue_cont.s 705.55
## nfc_mean.s 407.54
## pol_id.s 502.33
## pk_mean.s 582.27
## age.s 658.67
## female 1005.10
## edu_hs 38.48
## edu_somecollege 36.44
## edu_college 40.44
## edu_grad 51.89
## hispanic 201.68
## nonhisp_black 241.47
## income.s 518.44
## issueabort 553.84
## issuegun 596.81
## issueimm 593.42
## issuewage 569.81
## congenial_issue_cont.s:nfc_mean.s 273.19
## congenial_issue_cont.s:pol_id.s 325.40
## congenial_issue_cont.s:pk_mean.s 437.01
## pol_id.s:pk_mean.s 242.37
## congenial_issue_cont.s:pol_id.s:pk_mean.s 183.36
##
## Multiple R-squared: 0.04738 , Adjusted R-squared: 0.04007
## F-statistic: 6.427 on 22 and 1445 DF, p-value: < 2.2e-16summary(pooled_d_ideo_robust)##
## Call:
## lm_robust(formula = 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)
##
## Standard error type: CR2
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.5434384 0.039801 13.65384
## congenial_ideo_cont.s 0.0260594 0.005943 4.38457
## nfc_mean.s 0.0106721 0.005502 1.93972
## pol_id.s 0.0108347 0.005504 1.96855
## pk_mean.s -0.0061713 0.005692 -1.08415
## age.s -0.0005173 0.005503 -0.09401
## female -0.0362450 0.010608 -3.41665
## edu_hs 0.0143740 0.039365 0.36515
## edu_somecollege 0.0094565 0.039571 0.23898
## edu_college -0.0198356 0.040569 -0.48893
## edu_grad -0.0176657 0.043090 -0.40997
## hispanic 0.0475030 0.016398 2.89693
## nonhisp_black 0.0518889 0.017136 3.02804
## income.s 0.0047472 0.005480 0.86635
## issueabort -0.0065268 0.015607 -0.41820
## issuegun -0.0083017 0.015365 -0.54029
## issueimm -0.0002510 0.015105 -0.01662
## issuewage -0.0055326 0.015421 -0.35876
## congenial_ideo_cont.s:nfc_mean.s 0.0047641 0.005801 0.82131
## congenial_ideo_cont.s:pol_id.s 0.0080144 0.005750 1.39382
## congenial_ideo_cont.s:pk_mean.s 0.0176323 0.005908 2.98441
## pol_id.s:pk_mean.s -0.0087261 0.004955 -1.76101
## congenial_ideo_cont.s:pol_id.s:pk_mean.s 0.0011699 0.005460 0.21426
## Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 4.690e-16 4.628e-01 0.624072 37.16
## congenial_ideo_cont.s 1.442e-05 1.438e-02 0.037739 459.64
## nfc_mean.s 5.311e-02 -1.439e-04 0.021488 402.33
## pol_id.s 4.955e-02 2.122e-05 0.021648 502.71
## pk_mean.s 2.787e-01 -1.735e-02 0.005008 586.98
## age.s 9.251e-01 -1.132e-02 0.010288 654.50
## female 6.593e-04 -5.706e-02 -0.015428 1005.61
## edu_hs 7.171e-01 -6.541e-02 0.094156 36.72
## edu_somecollege 8.125e-01 -7.088e-02 0.089791 34.98
## edu_college 6.277e-01 -1.019e-01 0.062250 38.61
## edu_grad 6.836e-01 -1.042e-01 0.068900 49.60
## hispanic 4.182e-03 1.517e-02 0.079835 202.89
## nonhisp_black 2.733e-03 1.813e-02 0.085647 237.91
## income.s 3.867e-01 -6.018e-03 0.015512 521.27
## issueabort 6.760e-01 -3.718e-02 0.024129 554.65
## issuegun 5.892e-01 -3.848e-02 0.021875 595.88
## issueimm 9.867e-01 -2.992e-02 0.029414 592.38
## issuewage 7.199e-01 -3.582e-02 0.024757 569.34
## congenial_ideo_cont.s:nfc_mean.s 4.128e-01 -6.699e-03 0.016227 147.92
## congenial_ideo_cont.s:pol_id.s 1.650e-01 -3.328e-03 0.019357 187.84
## congenial_ideo_cont.s:pk_mean.s 3.090e-03 6.003e-03 0.029262 282.39
## pol_id.s:pk_mean.s 7.945e-02 -1.848e-02 0.001033 252.26
## congenial_ideo_cont.s:pol_id.s:pk_mean.s 8.308e-01 -9.658e-03 0.011998 103.86
##
## Multiple R-squared: 0.03383 , Adjusted R-squared: 0.02642
## F-statistic: 4.373 on 22 and 1444 DF, p-value: 7.763e-11summary(pooled_d_pid_robust)##
## Call:
## lm_robust(formula = 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)
##
## Standard error type: CR2
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.5208571 0.046757 11.13962
## congenial_pid_cont.s 0.0236879 0.005554 4.26466
## nfc_mean.s 0.0130715 0.006091 2.14588
## huddy_id.s 0.0285792 0.005876 4.86337
## pk_mean.s -0.0064709 0.006604 -0.97980
## age.s -0.0007553 0.006062 -0.12459
## female -0.0428045 0.011694 -3.66046
## edu_hs 0.0279551 0.047047 0.59419
## edu_somecollege 0.0367982 0.047187 0.77983
## edu_college 0.0017206 0.048360 0.03558
## edu_grad 0.0094507 0.050567 0.18689
## hispanic 0.0506643 0.018097 2.79955
## nonhisp_black 0.0423532 0.018422 2.29905
## income.s 0.0007516 0.006055 0.12413
## issueabort 0.0060268 0.017288 0.34860
## issuegun -0.0002843 0.017032 -0.01669
## issueimm 0.0141011 0.017138 0.82279
## issuewage 0.0013364 0.016771 0.07968
## congenial_pid_cont.s:nfc_mean.s 0.0013574 0.005518 0.24598
## congenial_pid_cont.s:huddy_id.s -0.0033797 0.005994 -0.56383
## congenial_pid_cont.s:pk_mean.s 0.0129580 0.005702 2.27239
## huddy_id.s:pk_mean.s -0.0190227 0.005871 -3.24026
## congenial_pid_cont.s:huddy_id.s:pk_mean.s 0.0071117 0.005833 1.21926
## Pr(>|t|) CI Lower CI Upper DF
## (Intercept) 1.803e-11 0.424810 0.616904 26.36
## congenial_pid_cont.s 2.254e-05 0.012784 0.034592 760.39
## nfc_mean.s 3.262e-02 0.001088 0.025055 328.37
## huddy_id.s 1.581e-06 0.017032 0.040127 466.17
## pk_mean.s 3.277e-01 -0.019448 0.006506 483.77
## age.s 9.009e-01 -0.012663 0.011152 544.04
## female 2.674e-04 -0.065757 -0.019852 846.51
## edu_hs 5.574e-01 -0.068646 0.124556 26.60
## edu_somecollege 4.427e-01 -0.060313 0.133909 25.38
## edu_college 9.719e-01 -0.097431 0.100872 27.44
## edu_grad 8.529e-01 -0.093310 0.112211 34.04
## hispanic 5.720e-03 0.014936 0.086393 167.25
## nonhisp_black 2.248e-02 0.006038 0.078668 210.97
## income.s 9.013e-01 -0.011149 0.012652 446.18
## issueabort 7.275e-01 -0.027944 0.039997 476.63
## issuegun 9.867e-01 -0.033748 0.033180 495.67
## issueimm 4.110e-01 -0.019574 0.047776 481.55
## issuewage 9.365e-01 -0.031620 0.034292 471.74
## congenial_pid_cont.s:nfc_mean.s 8.059e-01 -0.009512 0.012227 243.13
## congenial_pid_cont.s:huddy_id.s 5.731e-01 -0.015159 0.008400 454.28
## congenial_pid_cont.s:pk_mean.s 2.358e-02 0.001748 0.024168 409.38
## huddy_id.s:pk_mean.s 1.349e-03 -0.030583 -0.007463 261.86
## congenial_pid_cont.s:huddy_id.s:pk_mean.s 2.240e-01 -0.004381 0.018605 228.19
##
## Multiple R-squared: 0.04162 , Adjusted R-squared: 0.03281
## F-statistic: 5.122 on 22 and 1207 DF, p-value: 1.942e-13# To Plot:
tidy(pooled_a_iss_robust)[23, c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_issue_binary:pol_id.s:pk_mean.s 0.01430036 0.01122905tidy(pooled_a_ideo_robust)[23, c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_ideo_binary:pol_id.s:pk_mean.s 0.03283857 0.01467805tidy(pooled_a_pid_robust)[23, c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_pid_binary:huddy_id.s:pk_mean.s 0.0106842 0.0124378tidy(pooled_b_iss_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_issue_cont.s:pol_id.s:pk_mean.s 0.008536276 0.005771084tidy(pooled_b_ideo_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_ideo_cont.s:pol_id.s:pk_mean.s 0.01086174 0.00561971tidy(pooled_b_pid_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_pid_cont.s:huddy_id.s:pk_mean.s 0.00163644 0.006055017tidy(pooled_c_iss_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_issue_binary:pol_id.s:pk_mean.s 0.02771031 0.009991809tidy(pooled_c_ideo_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_ideo_binary:pol_id.s:pk_mean.s 0.007209673 0.01362074tidy(pooled_c_pid_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_pid_binary:huddy_id.s:pk_mean.s 0.01818385 0.0116276tidy(pooled_d_iss_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_issue_cont.s:pol_id.s:pk_mean.s 0.01282045 0.005371287tidy(pooled_d_ideo_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_ideo_cont.s:pol_id.s:pk_mean.s 0.001169943 0.005460296tidy(pooled_d_pid_robust)[23,c("term", "estimate", "std.error")]## term estimate std.error
## 23 congenial_pid_cont.s:huddy_id.s:pk_mean.s 0.007111718 0.005832831r1_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()## quartz_off_screen
## 28 R&R NFC and Trait Index Moderation Analysis with No Controls
8.1 Need for Closure
# 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)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_issue_binary * nfc_mean.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5994 -0.3798 -0.3563 0.4945 0.6521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.364789 0.017512 20.831 < 2e-16
## exp1_congenial_issue_binary 0.157813 0.024960 6.323 3.36e-10
## nfc_mean.s -0.007957 0.017522 -0.454 0.6498
## exp1_congenial_issue_binary:nfc_mean.s 0.044112 0.024943 1.768 0.0772
##
## (Intercept) ***
## exp1_congenial_issue_binary ***
## nfc_mean.s
## exp1_congenial_issue_binary:nfc_mean.s .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2404164)
##
## Null deviance: 380.87 on 1543 degrees of freedom
## Residual deviance: 370.24 on 1540 degrees of freedom
## (272 observations deleted due to missingness)
## AIC: 2186.9
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_nfc_e1_ideo <- glm(exp1_correct ~ exp1_congenial_ideo_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e1_ideo)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_ideo_binary * nfc_mean.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5177 -0.4023 -0.3559 0.4853 0.6683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37006 0.02184 16.942 < 2e-16 ***
## exp1_congenial_ideo_binary 0.14526 0.03068 4.735 2.5e-06 ***
## nfc_mean.s 0.01710 0.02228 0.767 0.443
## exp1_congenial_ideo_binary:nfc_mean.s -0.01598 0.03039 -0.526 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2423579)
##
## Null deviance: 254.23 on 1029 degrees of freedom
## Residual deviance: 248.66 on 1026 degrees of freedom
## (786 observations deleted due to missingness)
## AIC: 1469.1
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_nfc_e1_pid <- glm(exp1_correct ~ exp1_congenial_pid_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e1_pid)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_pid_binary * nfc_mean.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5400 -0.3866 -0.3754 0.5063 0.6282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37832 0.01942 19.483 < 2e-16 ***
## exp1_congenial_pid_binary 0.12691 0.02748 4.618 4.26e-06 ***
## nfc_mean.s -0.00306 0.01993 -0.153 0.878
## exp1_congenial_pid_binary:nfc_mean.s 0.01939 0.02745 0.706 0.480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2431863)
##
## Null deviance: 317.83 on 1288 degrees of freedom
## Residual deviance: 312.49 on 1285 degrees of freedom
## (527 observations deleted due to missingness)
## AIC: 1841.5
##
## Number of Fisher Scoring iterations: 2# 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)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_issue_binary *
## nfc_mean.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8263 -0.4773 0.2089 0.4210 2.5233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10610 0.03609 -2.940 0.00334 **
## exp2_congenial_issue_binary 0.20848 0.05059 4.121 3.97e-05 ***
## nfc_mean.s 0.02627 0.03704 0.709 0.47832
## exp2_congenial_issue_binary:nfc_mean.s -0.04151 0.05072 -0.818 0.41324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9899808)
##
## Null deviance: 1546.0 on 1547 degrees of freedom
## Residual deviance: 1528.5 on 1544 degrees of freedom
## (268 observations deleted due to missingness)
## AIC: 4383.4
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_nfc_e2ss_ideo <- glm(exp2_goodSample ~ exp2_congenial_ideo_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e2ss_ideo)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_ideo_binary *
## nfc_mean.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9169 -0.8104 0.1877 0.4634 2.5487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13578 0.04484 -3.028 0.002522 **
## exp2_congenial_ideo_binary 0.24390 0.06323 3.857 0.000122 ***
## nfc_mean.s -0.05379 0.04418 -1.218 0.223681
## exp2_congenial_ideo_binary:nfc_mean.s 0.10774 0.06264 1.720 0.085724 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.032988)
##
## Null deviance: 1082.4 on 1033 degrees of freedom
## Residual deviance: 1064.0 on 1030 degrees of freedom
## (782 observations deleted due to missingness)
## AIC: 2973.9
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_nfc_e2ss_pid <- glm(exp2_goodSample ~ exp2_congenial_pid_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e2ss_pid)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_pid_binary * nfc_mean.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8819 -0.4952 0.2052 0.4065 2.4551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08749 0.03988 -2.194 0.028431 *
## exp2_congenial_pid_binary 0.18760 0.05647 3.322 0.000919 ***
## nfc_mean.s -0.01753 0.03959 -0.443 0.657985
## exp2_congenial_pid_binary:nfc_mean.s 0.05800 0.05648 1.027 0.304644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.030652)
##
## Null deviance: 1341.0 on 1292 degrees of freedom
## Residual deviance: 1328.5 on 1289 degrees of freedom
## (523 observations deleted due to missingness)
## AIC: 3714.4
##
## Number of Fisher Scoring iterations: 2# 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)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_issue_binary *
## nfc_mean.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.62211 -0.62642 0.04762 0.67319 1.75900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.192442 0.035607 -5.405 7.52e-08
## exp2_congenial_issue_binary 0.377436 0.049954 7.556 7.11e-14
## nfc_mean.s 0.065048 0.036524 1.781 0.0751
## exp2_congenial_issue_binary:nfc_mean.s 0.009611 0.050037 0.192 0.8477
##
## (Intercept) ***
## exp2_congenial_issue_binary ***
## nfc_mean.s .
## exp2_congenial_issue_binary:nfc_mean.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9622066)
##
## Null deviance: 1542.9 on 1542 degrees of freedom
## Residual deviance: 1480.8 on 1539 degrees of freedom
## (273 observations deleted due to missingness)
## AIC: 4325.4
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_nfc_e2cc_ideo <- glm(exp2_goodCausal ~ exp2_congenial_ideo_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e2cc_ideo)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_ideo_binary *
## nfc_mean.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5612 -0.6374 0.2372 0.8263 1.6772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.162776 0.044814 -3.632 0.000295 ***
## exp2_congenial_ideo_binary 0.336964 0.063291 5.324 1.25e-07 ***
## nfc_mean.s 0.049283 0.044020 1.120 0.263163
## exp2_congenial_ideo_binary:nfc_mean.s -0.007309 0.062645 -0.117 0.907146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.031803)
##
## Null deviance: 1091.5 on 1030 degrees of freedom
## Residual deviance: 1059.7 on 1027 degrees of freedom
## (785 observations deleted due to missingness)
## AIC: 2964.1
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_nfc_e2cc_pid <- glm(exp2_goodCausal ~ exp2_congenial_pid_binary*nfc_mean.s,
data = data)
summary(r1_nfc_e2cc_pid)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_pid_binary * nfc_mean.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6103 -0.5588 0.1826 0.7877 1.6754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.12097 0.03927 -3.080 0.00211 **
## exp2_congenial_pid_binary 0.26933 0.05569 4.836 1.48e-06 ***
## nfc_mean.s 0.06138 0.03888 1.579 0.11468
## exp2_congenial_pid_binary:nfc_mean.s 0.02944 0.05565 0.529 0.59696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9991476)
##
## Null deviance: 1314.8 on 1288 degrees of freedom
## Residual deviance: 1283.9 on 1285 degrees of freedom
## (527 observations deleted due to missingness)
## AIC: 3662.9
##
## Number of Fisher Scoring iterations: 28.2 Trait Index
# 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)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_issue_binary * trait_index.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5771 -0.3951 -0.3480 0.4872 0.6948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36404 0.01754 20.754 < 2e-16
## exp1_congenial_issue_binary 0.15760 0.02500 6.305 3.76e-10
## trait_index.s -0.01766 0.01784 -0.989 0.323
## exp1_congenial_issue_binary:trait_index.s 0.03674 0.02500 1.470 0.142
##
## (Intercept) ***
## exp1_congenial_issue_binary ***
## trait_index.s
## exp1_congenial_issue_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2407573)
##
## Null deviance: 380.87 on 1543 degrees of freedom
## Residual deviance: 370.77 on 1540 degrees of freedom
## (272 observations deleted due to missingness)
## AIC: 2189.1
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_ti_e1_ideo <- glm(exp1_correct ~ exp1_congenial_ideo_binary*trait_index.s,
data = data)
summary(r1_ti_e1_ideo)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_ideo_binary * trait_index.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5529 -0.3749 -0.3682 0.4917 0.6355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.370045 0.021847 16.938 < 2e-16
## exp1_congenial_ideo_binary 0.145412 0.030687 4.739 2.46e-06
## trait_index.s 0.002193 0.022436 0.098 0.922
## exp1_congenial_ideo_binary:trait_index.s 0.010692 0.030225 0.354 0.724
##
## (Intercept) ***
## exp1_congenial_ideo_binary ***
## trait_index.s
## exp1_congenial_ideo_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2423998)
##
## Null deviance: 254.23 on 1029 degrees of freedom
## Residual deviance: 248.70 on 1026 degrees of freedom
## (786 observations deleted due to missingness)
## AIC: 1469.3
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_ti_e1_pid <- glm(exp1_correct ~ exp1_congenial_pid_binary*trait_index.s,
data = data)
summary(r1_ti_e1_pid)##
## Call:
## glm(formula = exp1_correct ~ exp1_congenial_pid_binary * trait_index.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5290 -0.3916 -0.3733 0.5005 0.6408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.378299 0.019421 19.479 < 2e-16
## exp1_congenial_pid_binary 0.126305 0.027476 4.597 4.71e-06
## trait_index.s -0.005722 0.020007 -0.286 0.775
## exp1_congenial_pid_binary:trait_index.s 0.014115 0.027396 0.515 0.606
##
## (Intercept) ***
## exp1_congenial_pid_binary ***
## trait_index.s
## exp1_congenial_pid_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2432789)
##
## Null deviance: 317.83 on 1288 degrees of freedom
## Residual deviance: 312.61 on 1285 degrees of freedom
## (527 observations deleted due to missingness)
## AIC: 1842
##
## Number of Fisher Scoring iterations: 2# 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)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_issue_binary *
## trait_index.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8064 -0.4605 0.2119 0.4193 2.5213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10556 0.03608 -2.926 0.00349
## exp2_congenial_issue_binary 0.20818 0.05057 4.117 4.05e-05
## trait_index.s 0.04717 0.03631 1.299 0.19409
## exp2_congenial_issue_binary:trait_index.s -0.04570 0.05055 -0.904 0.36608
##
## (Intercept) **
## exp2_congenial_issue_binary ***
## trait_index.s
## exp2_congenial_issue_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9893448)
##
## Null deviance: 1546.0 on 1547 degrees of freedom
## Residual deviance: 1527.5 on 1544 degrees of freedom
## (268 observations deleted due to missingness)
## AIC: 4382.4
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_ti_e2ss_ideo <- glm(exp2_goodSample ~ exp2_congenial_ideo_binary*trait_index.s,
data = data)
summary(r1_ti_e2ss_ideo)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_ideo_binary *
## trait_index.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9117 -0.8572 0.1936 0.4531 2.4858
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13415 0.04489 -2.989 0.002869
## exp2_congenial_ideo_binary 0.24476 0.06329 3.867 0.000117
## trait_index.s -0.01740 0.04348 -0.400 0.689140
## exp2_congenial_ideo_binary:trait_index.s 0.06381 0.06203 1.029 0.303880
##
## (Intercept) **
## exp2_congenial_ideo_binary ***
## trait_index.s
## exp2_congenial_ideo_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.034689)
##
## Null deviance: 1082.4 on 1033 degrees of freedom
## Residual deviance: 1065.7 on 1030 degrees of freedom
## (782 observations deleted due to missingness)
## AIC: 2975.6
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_ti_e2ss_pid <- glm(exp2_goodSample ~ exp2_congenial_pid_binary*trait_index.s,
data = data)
summary(r1_ti_e2ss_pid)##
## Call:
## glm(formula = exp2_goodSample ~ exp2_congenial_pid_binary * trait_index.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9354 -0.5179 0.2042 0.4023 2.4229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.087454 0.039890 -2.192 0.028531
## exp2_congenial_pid_binary 0.188662 0.056472 3.341 0.000859
## trait_index.s 0.002514 0.039936 0.063 0.949821
## exp2_congenial_pid_binary:trait_index.s 0.059189 0.056204 1.053 0.292485
##
## (Intercept) *
## exp2_congenial_pid_binary ***
## trait_index.s
## exp2_congenial_pid_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.029668)
##
## Null deviance: 1341.0 on 1292 degrees of freedom
## Residual deviance: 1327.2 on 1289 degrees of freedom
## (523 observations deleted due to missingness)
## AIC: 3713.2
##
## Number of Fisher Scoring iterations: 2# 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)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_issue_binary *
## trait_index.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.53814 -0.65342 -0.01552 0.58802 1.58359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19176 0.03569 -5.373 8.95e-08
## exp2_congenial_issue_binary 0.37556 0.05007 7.501 1.07e-13
## trait_index.s 0.01388 0.03590 0.387 0.699
## exp2_congenial_issue_binary:trait_index.s -0.03616 0.04996 -0.724 0.469
##
## (Intercept) ***
## exp2_congenial_issue_binary ***
## trait_index.s
## exp2_congenial_issue_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9668168)
##
## Null deviance: 1542.9 on 1542 degrees of freedom
## Residual deviance: 1487.9 on 1539 degrees of freedom
## (273 observations deleted due to missingness)
## AIC: 4332.8
##
## Number of Fisher Scoring iterations: 2# Ideology
r1_ti_e2cc_ideo <- glm(exp2_goodCausal ~ exp2_congenial_ideo_binary*trait_index.s,
data = data)
summary(r1_ti_e2cc_ideo)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_ideo_binary *
## trait_index.s, data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5552 -0.6211 0.2620 0.8714 1.5768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.16446 0.04484 -3.667 0.000258
## exp2_congenial_ideo_binary 0.33887 0.06333 5.351 1.08e-07
## trait_index.s 0.02093 0.04336 0.483 0.629337
## exp2_congenial_ideo_binary:trait_index.s -0.06106 0.06194 -0.986 0.324509
##
## (Intercept) ***
## exp2_congenial_ideo_binary ***
## trait_index.s
## exp2_congenial_ideo_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.032891)
##
## Null deviance: 1091.5 on 1030 degrees of freedom
## Residual deviance: 1060.8 on 1027 degrees of freedom
## (785 observations deleted due to missingness)
## AIC: 2965.2
##
## Number of Fisher Scoring iterations: 2# Party ID
r1_ti_e2cc_pid <- glm(exp2_goodCausal ~ exp2_congenial_pid_binary*trait_index.s,
data = data)
summary(r1_ti_e2cc_pid)##
## Call:
## glm(formula = exp2_goodCausal ~ exp2_congenial_pid_binary * trait_index.s,
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4745 -0.6310 0.2412 0.8539 1.4842
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.121883 0.039407 -3.093 0.00202
## exp2_congenial_pid_binary 0.268855 0.055879 4.811 1.68e-06
## trait_index.s 0.005371 0.039391 0.136 0.89156
## exp2_congenial_pid_binary:trait_index.s -0.002798 0.055507 -0.050 0.95980
##
## (Intercept) **
## exp2_congenial_pid_binary ***
## trait_index.s
## exp2_congenial_pid_binary:trait_index.s
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.005112)
##
## Null deviance: 1314.8 on 1288 degrees of freedom
## Residual deviance: 1291.6 on 1285 degrees of freedom
## (527 observations deleted due to missingness)
## AIC: 3670.6
##
## Number of Fisher Scoring iterations: 28.3 Figures
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()## quartz_off_screen
## 2sink()