Study2 (Appendix)
Yuya Endo
2020-11-12
## My Session info ##
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8
attached base packages:
grid stats graphics grDevices utils datasets methods
base
other attached packages:
patchwork_1.0.0 ggstance_0.3.4 cjoint_2.1.0 survey_4.0
survival_3.1-12 Matrix_1.2-18 lmtest_0.9-37 zoo_1.8-7
sandwich_2.5-1 gt_0.2.0.5 forcats_0.5.0 stringr_1.4.0
dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0library(tidyverse)
library(gt)
library(cjoint)
library(ggstance)
library(patchwork)df_type1 <- read_csv("data/study2_data_type1.csv")
df_type2 <- read_csv("data/study2_data_type2.csv") df_conjoint <- bind_rows(df_type1, df_type2) %>%
mutate(across(where(is.character), as.factor))Sample Characteristics
df_samp_freq <- df_type2 %>%
select(Age = agegroup, Race = R_race, Hispanic, Male) %>%
mutate(
Ethnicity = if_else(Hispanic == 1, "Hispanic", "Non-Hispanic"),
Gender = if_else(Male == 1, "Male", "Female")
) %>%
select(Age, Race, Ethnicity, Gender) %>%
pivot_longer(Age:Gender,
names_to = "variable", values_to = "value") %>%
group_by(variable) %>%
count(value) %>%
mutate(
freq　=　n/sum(n)*100,
variable = factor(variable, levels = c("Age", "Race",
"Ethnicity", "Gender"))
)tb_samp_freq <- df_samp_freq %>%
select(variable, value, freq) %>%
mutate(freq = round(freq, 2)) %>%
rename(Characteristic = value, `Proportion of Sample` = freq) %>%
　mutate(Characteristic = fct_relevel(Characteristic,
　 c("18-25", "26-35", "36-45", "46-55",
　 "56-65", "66 or older", "White",
　 "Black", "Hispanic", "Asian",
　 "Other", "Hispanic", "Non-Hispanic",
　 "Male", "Female"))) %>%
arrange(Characteristic) %>%
gt(rowname_col = "row",
groupname_col = "variable") %>%
cols_label(`Proportion of Sample` = html("Proportion of <br> Sample")) %>%
row_group_order(groups = c("Age", "Race", "Ethnicity", "Gender"))
tb_samp_freq | Characteristic | Proportion of Sample |
|---|---|
| Age | |
| 18-25 | 10.03 |
| 26-35 | 37.83 |
| 36-45 | 23.83 |
| 46-55 | 15.02 |
| 56-65 | 9.73 |
| 66 or older | 3.56 |
| Race | |
| White | 72.14 |
| Black | 7.93 |
| Hispanic | 12.71 |
| Asian | 5.49 |
| Other | 1.73 |
| Ethnicity | |
| Hispanic | 12.71 |
| Non-Hispanic | 87.29 |
| Gender | |
| Male | 44.75 |
| Female | 55.25 |
attribute_list <- list()
attribute_list[["Sex"]] <- c("Male", "Female")
attribute_list[["Age"]] <- c("44 years old","52 years old","60 years old",
"68 years old","76 years old")
attribute_list[["Race/Ethnicity"]] <- c("White","Black",
"Hispanic","Asian American")
attribute_list[["Marital status"]] <- c("Single","Married")
attribute_list[["Parental status"]] <- c("No children","1 child","2 children")
attribute_list[["Experience in legal profession"]] <- c("No experience",
"5 years",
"10 years",
"15 years",
"20 years")
attribute_list[["Law school ranking"]] <- c("Top 10 (Tier 1)",
"50-100 (Tier 2)","151-200 (Tier 4)")
attribute_list[["Party affiliation"]] <- c("Democratic Party","Republican Party")conjoint_design <- makeDesign(type = "constraints",
attribute.levels = attribute_list)baselines <- list()
baselines[["Sex"]] <- c("Male")
baselines[["Age"]] <- c("44 years old")
baselines[["Race/Ethnicity"]] <- c("White")
baselines[["Marital status"]] <- c("Single")
baselines[["Parental status"]] <- c("No children")
baselines[["Experience in legal profession"]] <- c("No experience")
baselines[["Law school ranking"]] <- c("Top 10 (Tier 1)")
baselines[["Party affiliation"]] <- c("Democratic Party")Part 1: Pooled Conjoint Results by Respondent’s Partisanship (Figure 2)
acie_partisan <- df_conjoint %>%
drop_na(Partisanship) %>%
split(.$type) %>%
map(
~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * Partisanship,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("Partisanship"))
)base_level <- function(data, mod){
df_base_pa <- summary(mod)$baselines_amce %>%
mutate(
Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")")
)
pa <- data %>%
mutate(
lwr = Estimate - 1.96 * `Std. Err`,
upr = Estimate + 1.96 * `Std. Err`
) %>%
bind_rows(df_base_pa) %>%
mutate(
Level = as.factor(Level),
Level = factor(Level,
levels = c("Republican Party",
"Party affiliation:\n(Baseline = Democratic Party)",
"151-200 (Tier 4)",
"50-100 (Tier 2)",
"Law school ranking:\n(Baseline = Top 10 (Tier 1))",
"20 years",
"15 years",
"10 years",
"5 years",
"Experience in legal profession:\n(Baseline = No experience)",
"2 children",
"1 child",
"Parental status:\n(Baseline = No children)",
"Married",
"Marital status:\n(Baseline = Single)",
"76 years old",
"68 years old",
"60 years old",
"52 years old",
"Age:\n(Baseline = 44 years old)",
"Hispanic",
"Black",
"Asian American",
"Race/Ethnicity:\n(Baseline = White)",
"Female",
"Sex:\n(Baseline = Male)"))
)
return(pa)
}summary(acie_partisan$type1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" acie_mutate <- function(dat1, dat2, dat3, mod){
d1 <- dat1 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Democrat")
d2 <- dat2 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Independent")
d3 <- dat3 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Republican")
dd <- bind_rows(d1, d2, d3) %>%
mutate(Partisanship = factor(Partisanship, levels = c("Republican",
"Independent",
"Democrat")))
return(dd)
}df_acie_partisan1 <- acie_mutate(summary(acie_partisan$type1)$Partisanship1amce,
summary(acie_partisan$type1)$Partisanship2amce,
summary(acie_partisan$type1)$Partisanship3amce,
mod = acie_partisan$type1) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)conjoint_plot <- function(data,
ylim, xlim, xlab,
facet_vari = NULL,
text_x, text_y){
pl <- data %>%
ggplot(., aes(x = Estimate, y = Level,
xmin = lwr, xmax = upr), col = "black") +
geom_vline(xintercept = 0, size = .5,
colour = "black", linetype = "dotted") +
geom_pointrange() +
coord_cartesian(ylim = ylim, xlim = xlim, clip = 'off') +
labs(x = xlab, y = NULL) +
theme(legend.position = "none",
axis.text = element_text(size = 11),
axis.title = element_text(size = 12),
plot.margin = unit(c(1, 1, 0, 1), "lines"))
if(is.null(facet_vari)){
pl <- pl +
geom_text(x = text_x, y = text_y, hjust = 0, col = "black",
size = 4,
label = "Judge's attributes", show.legend = FALSE)
return(pl)
} else {
facet_vari <- sym(facet_vari)
pl <- pl +
facet_wrap(facet_vari, ncol = 3) +
geom_text(data = data,
x = text_x, y = text_y, hjust = 0, col = "black",
size = 4,
label = data$judge_lab, show.legend = FALSE)
return(pl)
}
}pl_acie_partisan1 <- df_acie_partisan1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .2),
xlab = "Change in Pr(Self value influence judge)",
facet_vari = "Partisanship",
text_x = -.38, text_y = 26.7)
pl_acie_partisan1
ggsave("output_figure/appendix/Part1.png", pl_acie_partisan1, width = 10, height = 10)Part 2: Separated Conjoint Results for Abortion Case (Figure 3)
acie_partisan_case <- df_conjoint %>%
drop_na(Partisanship) %>%
split(list(.$type ,.$Condition)) %>%
map(
~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * Partisanship,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("Partisanship"))
)conjoint_mutate <- function(data,
p1 = "", p2 = "", p3 = "",
xlim, xlab = ""){
cm <- data %>%
mutate(
Partisanship = c(rep(p1, nrow(.) / 3),
rep(p2, nrow(.) / 3),
rep(p3, nrow(.) / 3)),
Partisanship = factor(Partisanship, levels = c("Republican",
"Independent",
"Democrat")),
lwr = Estimate - 1.96 * `Std. Err`,
upr = Estimate + 1.96 * `Std. Err`
) %>%
filter(Level %in% c("Female", "Hispanic")) %>%
mutate(
judge = if_else(Level == "Female", "Female Judge", "Hispanic Judge")
)
pl <- cm %>%
mutate(judge = fct_rev(fct_inorder(judge))) %>%
ggplot(aes(x = Estimate, y = Partisanship, shape = judge, color = judge,
xmin = lwr, xmax = upr)) +
geom_vline(xintercept = 0, size = 1,
colour = "gray75", linetype = "solid") +
geom_pointrangeh(position = position_dodgev(height = .75), size = .65) +
labs(x = xlab, y = NULL) +
xlim(xlim) +
scale_colour_manual(name = "Judge",
values = c(`Female Judge` = "black",
`Hispanic Judge` = "grey60"),
guide = guide_legend(reverse = TRUE)) +
scale_shape_manual(name = "Judge",
values = c(`Female Judge` = 15,
`Hispanic Judge` = 17),
guide = guide_legend(reverse = TRUE)) +
theme(legend.position = "bottom",
legend.key = element_rect(fill = "white"),
axis.text = element_text(size = 11))
return(pl)
}summary(acie_partisan_case$type1.Conjoint1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" pl_part2 <- bind_rows(summary(acie_partisan_case$type1.Conjoint1)$Partisanship1amce,
summary(acie_partisan_case$type1.Conjoint1)$Partisanship2amce,
summary(acie_partisan_case$type1.Conjoint1)$Partisanship3amce) %>%
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican",
xlim = c(-.11, .11), xlab = "Change in Pr(Personal values)")
pl_part2
ggsave("output_figure/appendix/Part2.png", pl_part2, width = 6, height = 2.8)Part 3: Separated Conjoint Results for Immigration Case (Figure 4)
summary(acie_partisan_case$type1.Conjoint2)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" pl_part3 <- bind_rows(summary(acie_partisan_case$type1.Conjoint2)$Partisanship1amce,
summary(acie_partisan_case$type1.Conjoint2)$Partisanship2amce,
summary(acie_partisan_case$type1.Conjoint2)$Partisanship3amce) %>%
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican",
xlim = c(-.2, .2), xlab = "Change in Pr(Personal values)")
pl_part3
ggsave("output_figure/appendix/Part3.png", pl_part3, width = 6, height = 2.8)Part 4: Pooled AMCEs
acie_pool <- df_conjoint %>%
split(.$type) %>%
map(
~ amce(selected ~ Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines)
)df_acie_pool1 <- summary(acie_pool$type1)$amce %>%
base_level(mod = acie_pool$type1) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool1 <- df_acie_pool1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1),
xlab = "Change in Pr(Self value influence judge)",
text_x = -.38, text_y = 26.7)
pl_acie_pool1
ggsave("output_figure/appendix/Part4_a.png", pl_acie_pool1, width = 10, height = 10)(b) Average Marginal Component Effects (judge is biased)
df_acie_pool2 <- summary(acie_pool$type2)$amce %>%
base_level(mod = acie_pool$type2) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool2 <- df_acie_pool2 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .15),
xlab = "Change in Pr(Biased judge)",
text_x = -.22, text_y = 26.7)
pl_acie_pool2
ggsave("output_figure/appendix/Part4_b.png", pl_acie_pool2, width = 10, height = 10)Part 5: Separated AMCEs – Abortion Case
acie_pool_case <- df_conjoint %>%
split(list(.$type, .$Condition)) %>%
map(
~ amce(selected ~ Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines)
)(a) Average Marginal Component Effects in Abortion Case (personal values influence decisions)
df_acie_pool_case1 <- summary(acie_pool_case$type1.Conjoint1)$amce %>%
base_level(mod = acie_pool_case$type1.Conjoint1) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool_case1 <- df_acie_pool_case1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1),
xlab = "Change in Pr(Self value influence judge)",
text_x = -.147, text_y = 26.7)
pl_acie_pool_case1
ggsave("output_figure/appendix/Part5_a.png", pl_acie_pool_case1, width = 10, height = 10)(b) Average Marginal Component Effects in Abortion Case (judge is biased)
df_acie_pool_case2 <- summary(acie_pool_case$type2.Conjoint1)$amce %>%
base_level(mod = acie_pool_case$type2.Conjoint1) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool_case2<- df_acie_pool_case2 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .1),
xlab = "Change in Pr(Biased judge)",
text_x = -.21, text_y = 26.7)
pl_acie_pool_case2
ggsave("output_figure/appendix/Part5_b.png", pl_acie_pool_case2, width = 10, height = 10)Part 6: Separated AMCEs – Immigration Case
(a) Average Marginal Component Effects in Immigration Case (personal values influence decisions)
df_acie_pool_case11 <- summary(acie_pool_case$type1.Conjoint2)$amce %>%
base_level(mod = acie_pool_case$type1.Conjoint2) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool_case11 <- df_acie_pool_case11 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1),
xlab = "Change in Pr(Self value influence judge)",
text_x = -.147, text_y = 26.7)
pl_acie_pool_case11
ggsave("output_figure/appendix/Part6_a.png", pl_acie_pool_case11, width = 10, height = 10)(b) Average Marginal Component Effects in Immigration Case (judge is biased)
df_acie_pool_case22 <- summary(acie_pool_case$type2.Conjoint2)$amce %>%
base_level(mod = acie_pool_case$type2.Conjoint2) %>%
mutate(
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_)
)pl_acie_pool_case22 <- df_acie_pool_case22 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .1),
xlab = "Change in Pr(Biased judge)",
text_x = -.21, text_y = 26.7)
pl_acie_pool_case22
ggsave("output_figure/appendix/Part6_b.png", pl_acie_pool_case22, width = 10, height = 10)Part 7: Full ACIEs for Figure 3
(a) Average Component Interactive Effects in Abortion Case (personal values influence decisions)
summary(acie_partisan_case$type1.Conjoint1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" df_acie_partisan_case1 <- acie_mutate(
summary(acie_partisan_case$type1.Conjoint1)$Partisanship1amce,
summary(acie_partisan_case$type1.Conjoint1)$Partisanship2amce,
summary(acie_partisan_case$type1.Conjoint1)$Partisanship3amce,
mod = acie_partisan_case$type1.Conjoint1
) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)pl_acie_partisan_case1 <- df_acie_partisan_case1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2),
xlab = "Change in Pr(Self value influence judge)",
facet_vari = "Partisanship",
text_x = -.335, text_y = 26.7)
pl_acie_partisan_case1
ggsave("output_figure/appendix/Part7_a.png",
pl_acie_partisan_case1, width = 10, height = 10)(b) Average Component Interactive Effects in Abortion Case (judge is biased)
summary(acie_partisan_case$type2.Conjoint1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" df_acie_partisan_case2 <- acie_mutate(
summary(acie_partisan_case$type2.Conjoint1)$Partisanship1amce,
summary(acie_partisan_case$type2.Conjoint1)$Partisanship2amce,
summary(acie_partisan_case$type2.Conjoint1)$Partisanship3amce,
mod = acie_partisan_case$type2.Conjoint1
) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)pl_acie_partisan_case2 <- df_acie_partisan_case2 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.2, .3),
xlab = "Change in Pr(Biased judge)",
facet_vari = "Partisanship",
text_x = -.52, text_y = 26.7)
pl_acie_partisan_case2
ggsave("output_figure/appendix/Part7_b.png",
pl_acie_partisan_case2, width = 10, height = 10)Part 8: Full ACIEs for Figure 4
(a) Average Component Interactive Effects in Immigration Case (personal values influence decisions)
summary(acie_partisan_case$type1.Conjoint2)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" df_acie_partisan_case11 <- acie_mutate(
summary(acie_partisan_case$type1.Conjoint2)$Partisanship1amce,
summary(acie_partisan_case$type1.Conjoint2)$Partisanship2amce,
summary(acie_partisan_case$type1.Conjoint2)$Partisanship3amce,
mod = acie_partisan_case$type1.Conjoint2
) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)pl_acie_partisan_case11 <- df_acie_partisan_case11 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2),
xlab = "Change in Pr(Self value influence judge)",
facet_vari = "Partisanship",
text_x = -.34, text_y = 26.7)
pl_acie_partisan_case11
ggsave("output_figure/appendix/Part8_a.png",
pl_acie_partisan_case11, width = 10, height = 10)(b) Average Component Interactive Effects in Immigration Case (judge is biased)
summary(acie_partisan_case$type2.Conjoint2)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" df_acie_partisan_case22 <- acie_mutate(
summary(acie_partisan_case$type2.Conjoint2)$Partisanship1amce,
summary(acie_partisan_case$type2.Conjoint2)$Partisanship2amce,
summary(acie_partisan_case$type2.Conjoint2)$Partisanship3amce,
mod = acie_partisan_case$type2.Conjoint2
) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)pl_acie_partisan_case22 <- df_acie_partisan_case22 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.2, .3),
xlab = "Change in Pr(Biased judge)",
facet_vari = "Partisanship",
text_x = -.52, text_y = 26.7)
pl_acie_partisan_case22
ggsave("output_figure/appendix/Part8_b.png",
pl_acie_partisan_case22, width = 10, height = 10)Part 9: Comparison between a Republican Judge and a Democratic Judge
acie_pa <- df_conjoint %>%
split(.$type) %>%
map(
~ amce(selected ~ Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`+
Sex * `Party affiliation` +
Age * `Party affiliation` +
`Race/Ethnicity` * `Party affiliation` +
`Marital status` * `Party affiliation` +
`Parental status` * `Party affiliation` +
`Experience in legal profession` * `Party affiliation` +
`Law school ranking` * `Party affiliation`,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines)
)base_level_pa <- function(data, type = ""){
df_base_pa <- summary(acie_pa$type1)$baselines_acie %>%
mutate(
Attribute = str_remove_all(Attribute, c("Party affiliation" = "", ":" = "")),
Level = str_remove_all(Level, c("Democratic Party" = "", ":" = "")),
Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")")
)
pa <- data %>%
mutate(
Attribute = str_remove_all(Attribute, c("Party affiliation" = "", ":" = "")),
Level = str_remove_all(Level, c("Republican Party" = "", ":" = "")),
lwr = Estimate - 1.96 * `Std. Err`,
upr = Estimate + 1.96 * `Std. Err`
) %>%
bind_rows(df_base_pa) %>%
mutate(
Level = as.factor(Level),
Level = factor(Level,
levels = c("151-200 (Tier 4)",
"50-100 (Tier 2)",
"Law school ranking:\n(Baseline = Top 10 (Tier 1))",
"20 years",
"15 years",
"10 years",
"5 years",
"Experience in legal profession:\n(Baseline = No experience)",
"2 children",
"1 child",
"Parental status:\n(Baseline = No children)",
"Married",
"Marital status:\n(Baseline = Single)",
"76 years old",
"68 years old",
"60 years old",
"52 years old",
"Age:\n(Baseline = 44 years old)",
"Hispanic",
"Black",
"Asian American",
"Race/Ethnicity:\n(Baseline = White)",
"Female",
"Sex:\n(Baseline = Male)")),
type = type
)
return(pa)
}df_acie_pa1 <- summary(acie_pa$type1)$acie %>%
base_level_pa(type = "(a) ACIE (personal values influence decisions)")
df_acie_pa2 <- summary(acie_pa$type2)$acie %>%
base_level_pa(type = "(b) ACIE (judge is biased)")
df_acie_pa <- bind_rows(df_acie_pa1, df_acie_pa2) %>%
mutate(
judge_lab = if_else(Level == "Female",
"Judge's attributes", NA_character_),
Party_affiliation = "ACIE\n Party affiliation = Republican Party"
)pl_acie_pa <- df_acie_pa %>%
group_nest(type) %>%
mutate(
gg = map(data, ~ ggplot(., aes(x = Estimate, y = Level,
xmin = lwr, xmax = upr), col = "black") +
facet_wrap(~ Party_affiliation, ncol = 4) +
geom_vline(xintercept = 0, size = .5,
colour = "black", linetype = "dotted") +
geom_pointrange() +
coord_cartesian(ylim = c(1, 24.5),
xlim = c(-.1, .1), clip = 'off') +
ylab(NULL) +
theme(legend.position = "none",
axis.text = element_text(size = 11),
plot.margin = unit(c(1, 1, 0, 1), "lines"),
plot.title = element_text(size = 12, hjust = 0.5))),
gg = map2(gg, type, ~ .x + labs(title = .y))
)
pl_acie_pa <- wrap_plots(pl_acie_pa$gg)
pl_acie_pa[[1]] <- pl_acie_pa[[1]] +
geom_text(data = df_acie_partisan_case22,
x = -.19, y = 24.7, hjust = 0, col = "black",
size = 4,
label = df_acie_partisan_case22$judge_lab,
show.legend = FALSE) +
xlab("Change in Pr(Self value influence judge)")
pl_acie_pa[[2]] <- pl_acie_pa[[2]] +
xlab("Change in Pr(Biased judge)") +
theme(axis.text.y = element_blank())
pl_acie_pa
ggsave("output_figure/appendix/Part9_ab.png", pl_acie_pa, width = 10, height = 10)Average Component Interactive Effects (personal values influence decisions)
acie_context <- df_conjoint %>%
drop_na(Context2) %>%
split(.$type) %>%
map(
~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * Context2,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("Context2"))
)summary(acie_context$type1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Context21amce" "Context2" "Interparty"
[2,] "Context22amce" "Context2" "IntrapartyD"
[3,] "Context23amce" "Context2" "IntrapartyR"mutate_context <- function(dat1, dat2, dat3, mod){
d1 <- dat1 %>%
base_level(mod = mod) %>%
mutate(Context2 = "Interparty")
d2 <- dat2 %>%
base_level(mod = mod) %>%
mutate(Context2 = "IntrapartyD")
d3 <- dat3 %>%
base_level(mod = mod) %>%
mutate(Context2 = "IntrapartyR")
dd <- bind_rows(d1, d2, d3) %>%
mutate(Context2 = factor(Context2, levels = c("Interparty",
"IntrapartyD",
"IntrapartyR")))
return(dd)
}df_acie_context1 <- mutate_context(
summary(acie_context$type1)$Context21amce,
summary(acie_context$type1)$Context22amce,
summary(acie_context$type1)$Context23amce,
mod = acie_context$type1
) %>%
mutate(
judge_lab = if_else(Context2 == "Interparty" &
Level == "Female",
"Judge's attributes", NA_character_),
Context2 = str_c("Context = ", Context2)
)pl_acie_context1 <- df_acie_context1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2),
xlab = "Change in Pr(Self value influence judge)",
facet_vari = "Context2",
text_x = -.34, text_y = 26.7)
pl_acie_context1
ggsave("output_figure/appendix/Part9_c.png", pl_acie_context1, width = 10, height = 10)df_acie_context2 <- mutate_context(
summary(acie_context$type2)$Context21amce,
summary(acie_context$type2)$Context22amce,
summary(acie_context$type2)$Context23amce,
mod = acie_context$type2
) %>%
mutate(
judge_lab = if_else(Context2 == "Interparty" &
Level == "Female",
"Judge's attributes", NA_character_),
Context2 = str_c("Context = ", Context2)
)pl_acie_context2 <- df_acie_context2 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2),
xlab = "Change in Pr(Biased judge)",
facet_vari = "Context2",
text_x = -.34, text_y = 26.7)
pl_acie_context2
ggsave("output_figure/appendix/Part9_d.png", pl_acie_context2, width = 10, height = 10)Part 10: Gender affinity effects
acie_rsex <- df_conjoint %>%
drop_na(Rsex) %>%
split(.$type) %>%
map(
~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * Rsex,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("Rsex"))
)summary(acie_rsex$type1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Rsex1amce" "Rsex" "Female"
[2,] "Rsex2amce" "Rsex" "Male" mutate_resex <- function(dat1, dat2, mod){
d1 <- dat1 %>%
base_level(mod = mod) %>%
mutate(Rsex = "Female")
d2 <- dat2 %>%
base_level(mod = mod) %>%
mutate(Rsex = "Male")
dd <- bind_rows(d1, d2) %>%
mutate(Rsex = factor(Rsex, levels = c("Female", "Male")))
return(dd)
}df_acie_rsex1 <- mutate_resex(
summary(acie_rsex$type1)$Rsex1amce,
summary(acie_rsex$type1)$Rsex2amce,
mod = acie_rsex$type1
) %>%
mutate(
judge_lab = if_else(Rsex == "Female" & Level == "Female",
"Judge's attributes", NA_character_),
Rsex = str_c("Respondent's\n Gender = ", Rsex),
type = "(a) ACIE (personal values influence decisions)"
)
df_acie_rsex2 <- mutate_resex(
summary(acie_rsex$type2)$Rsex1amce,
summary(acie_rsex$type2)$Rsex2amce,
mod = acie_rsex$type2
) %>%
mutate(
judge_lab = if_else(Level == "Female",
"Judge's attributes", NA_character_),
Rsex = str_c("Respondent's\n Gender = ", Rsex),
type = "(b) ACIE (judge is biased)"
)
df_acie_rsex <- bind_rows(df_acie_rsex1, df_acie_rsex2)pl_acie_rsex <- df_acie_rsex %>%
group_nest(type) %>%
mutate(
gg = map(data, ~ ggplot(., aes(x = Estimate, y = Level,
xmin = lwr, xmax = upr), color = "black") +
facet_wrap(~ Rsex, ncol = 4) +
geom_vline(xintercept = 0, size = .5,
colour = "black", linetype = "dotted") +
geom_pointrange() +
coord_cartesian(ylim = c(1, 26.5),
xlim = c(-.15, .1), clip = 'off') +
ylab(NULL) +
theme(legend.position = "none",
axis.text = element_text(size = 11),
plot.margin = unit(c(1, 1, 0, 1), "lines"),
plot.title = element_text(size = 12, hjust = 0.5))),
gg = map2(gg, type, ~ .x + labs(title = .y))
)
pl_acie_rsex <- wrap_plots(pl_acie_rsex$gg)
pl_acie_rsex[[1]] <- pl_acie_rsex[[1]] +
geom_text(data = df_acie_rsex1,
x = -.305, y = 26.7, hjust = 0, col = "black",
size = 4,
label = df_acie_rsex1$judge_lab,
show.legend = FALSE) +
xlab("Change in Pr(Self value influence judge)")
pl_acie_rsex[[2]] <- pl_acie_rsex[[2]] +
xlab("Change in Pr(Biased judge)") +
theme(axis.text.y = element_blank())
pl_acie_rsex
ggsave("output_figure/appendix/Part10.png", pl_acie_rsex, width = 14, height = 10)Part 11: Race affinity effects
acie_rrace <- df_conjoint %>%
drop_na(R_race) %>%
split(.$type) %>%
map(
~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * R_race,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("R_race"))
)summary(acie_rrace$type1)$table_values_amce Table.Name Level.Name Level.Value
[1,] "Rrace1amce" "Rrace" "Asian"
[2,] "Rrace2amce" "Rrace" "Black"
[3,] "Rrace3amce" "Rrace" "Hispanic"
[4,] "Rrace4amce" "Rrace" "Other"
[5,] "Rrace5amce" "Rrace" "White" mutate_rrace <- function(dat1, dat2, dat3, dat4,dat5, mod){
d1 <- dat1 %>%
base_level(mod = mod) %>%
mutate(Rrace = "Asian")
d2 <- dat2 %>%
base_level(mod = mod) %>%
mutate(Rrace = "Black")
d3 <- dat3 %>%
base_level(mod = mod) %>%
mutate(Rrace = "Hispanic")
d4 <- dat4 %>%
base_level(mod = mod) %>%
mutate(Rrace = "Other")
d5 <- dat5 %>%
base_level(mod = mod) %>%
mutate(Rrace = "White")
dd <- bind_rows(d1, d2, d3, d4, d5) %>%
mutate(Rsex = factor(Rrace, levels = c("Asian", "Black", "Hispanic",
"White", "Other")))
return(dd)
}df_acie_rrace1 <- mutate_rrace(
summary(acie_rrace$type1)$Rrace1amce,
summary(acie_rrace$type1)$Rrace2amce,
summary(acie_rrace$type1)$Rrace3amce,
summary(acie_rrace$type1)$Rrace4amce,
summary(acie_rrace$type1)$Rrace5amce,
mod = acie_rrace$type1
) %>%
mutate(
judge_lab = if_else(Rrace == "Asian" &
Level == "Female",
"Judge's attributes", NA_character_),
Rrace = str_c("Respondent's\n Race = ", Rrace)
)
df_acie_rrace2 <- mutate_rrace(
summary(acie_rrace$type2)$Rrace1amce,
summary(acie_rrace$type2)$Rrace2amce,
summary(acie_rrace$type2)$Rrace3amce,
summary(acie_rrace$type2)$Rrace4amce,
summary(acie_rrace$type2)$Rrace5amce,
mod = acie_rrace$type2
) %>%
mutate(
judge_lab = if_else(Rrace == "Asian" &
Level == "Female",
"Judge's attributes", NA_character_),
Rrace = str_c("Respondent's\n Race = ", Rrace)
)pl_acie_rrace1 <- df_acie_rrace1 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.3, .3),
xlab = "Change in Pr(Self value influence judge)",
facet_vari = "Rrace",
text_x = -.6, text_y = 27)
pl_acie_rrace1
ggsave("output_figure/appendix/Part11_a.png", pl_acie_rrace1, width = 13, height = 15)pl_acie_rrace2 <- df_acie_rrace2 %>%
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.3, .3),
xlab = "Change in Pr(Biased judge)",
facet_vari = "Rrace",
text_x = -.6, text_y = 27)
pl_acie_rrace2
ggsave("output_figure/appendix/Part11_b.png", pl_acie_rrace2, width = 13, height = 15)