Here are all the target words:

here("analyses/study1c/iat_stuff/all_target_words.csv") %>%
  read_csv() %>%
  arrange(domain, cat_id) %>%
  select(domain, cat_id, stim_name) %>%
  DT::datatable()
ES_PATH <- here("data/study1c/bnc_vs_coca_es.csv")
IAT_PATH <- here("data/study1c/AIID_subset_exploratory.csv")

lang_es <- read_csv(ES_PATH) %>%
  select(-bias_type)
raw_exp <- read_csv(IAT_PATH)
BAD_DOMAINS1 <- c("Skeptical - Trusting", "Avoiding - Approaching", 
                  "Determinism - Free Will", "Lawyers - Politicians",
                  "Speed - Accuracy", "Organized Labor - Management")

BAD_DOMAINS2 <- c("Skeptical - Trusting", "Avoiding - Approaching", 
                  "Determinism - Free Will", "Lawyers - Politicians",
                  "Speed - Accuracy", "Organized Labor - Management", 
                  "State - Church", "Chaos - Order")

# get columsn we care about at drop NAs
exp_filtered <- raw_exp %>%
  mutate(domain = case_when(domain == "Determinism - Free will" ~ "Determinism - Free Will",
                            TRUE ~ domain)) %>%
  filter(domain %in% lang_es$test) %>%
  mutate_if(is.character, as.factor)  %>%
  select(1,5,D,residence, sex, age, block_order, domain, education, income, exclude_iat) %>%
 # filter(!exclude_iat) %>%
  drop_na()

exp_filtered_countries <- exp_filtered %>%
  filter(residence %in% c("us",  "uk"))
 # mutate(residence = case_when(residence == "us" ~ "us", TRUE ~ "uk_au")) %>%

Domains all

subj_counts <- exp_filtered_countries %>%
  count(residence, domain) %>%
  arrange(n) %>%
  data.frame()

kable(subj_counts)
residence domain n
uk Lawyers - Politicians 3
uk Organized Labor - Management 4
uk Skeptical - Trusting 6
uk National Defense - Education 7
uk Team - Individual 7
uk Determinism - Free Will 9
uk Chaos - Order 10
uk Rich People - Beautiful People 10
uk Technology - Nature 10
uk Tradition - Progress 11
uk Winter - Summer 11
uk Cold - Hot 12
uk State - Church 12
uk Urban - Rural 12
uk Avoiding - Approaching 14
uk Money - Love 14
uk Security - Freedom 14
uk Speed - Accuracy 14
uk Reason - Emotions 15
uk Career - Family 19
uk Jocks - Nerds 19
us Lawyers - Politicians 140
us Determinism - Free Will 148
us Tradition - Progress 148
us Rich People - Beautiful People 152
us Skeptical - Trusting 152
us Team - Individual 155
us Organized Labor - Management 156
us Technology - Nature 156
us Chaos - Order 164
us Urban - Rural 164
us State - Church 168
us Avoiding - Approaching 175
us Money - Love 178
us National Defense - Education 194
us Career - Family 198
us Cold - Hot 200
us Security - Freedom 207
us Jocks - Nerds 220
us Reason - Emotions 221
us Speed - Accuracy 224
us Winter - Summer 234 
mean_es <- exp_filtered_countries %>%
  add_residuals(lm(D ~ task_order + sex + age + block_order + education ,
                   data = exp_filtered)) %>%
  group_by(residence, domain) %>%
  multi_boot_standard(col = "resid", na.rm = T) 

mean_es_wide <- mean_es %>%
  left_join(lang_es, by = c("domain" = "test")) %>%
  select(-ci_lower, -ci_upper) %>%
  spread(residence, mean)

BNC

ggplot(mean_es_wide, aes(x = effect_size_bnc, y = uk)) +
  geom_label(aes(label = domain)) +
  geom_point() +
  ggtitle("BNC") + 
  ylab("UK participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

COCA

ggplot(mean_es_wide, aes(x = effect_size_coca, y = us)) +
  geom_label(aes(label = domain)) +
  geom_point() +
  ggtitle("COCA") + 
  ylab("US participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

Correlation plot

make_corr_plot(mean_es_wide[,-1])

Domains1

EXCLUDING: Skeptical - Trusting, Avoiding - Approaching, Determinism - Free Will, Lawyers - Politicians, Speed - Accuracy, Organized Labor - Management

subj_counts <- exp_filtered_countries %>%
  filter(!(domain %in% BAD_DOMAINS1)) %>%
  count(residence, domain) %>%
  arrange(n) %>%
  data.frame()

kable(subj_counts)
residence domain n
uk National Defense - Education 7
uk Team - Individual 7
uk Chaos - Order 10
uk Rich People - Beautiful People 10
uk Technology - Nature 10
uk Tradition - Progress 11
uk Winter - Summer 11
uk Cold - Hot 12
uk State - Church 12
uk Urban - Rural 12
uk Money - Love 14
uk Security - Freedom 14
uk Reason - Emotions 15
uk Career - Family 19
uk Jocks - Nerds 19
us Tradition - Progress 148
us Rich People - Beautiful People 152
us Team - Individual 155
us Technology - Nature 156
us Chaos - Order 164
us Urban - Rural 164
us State - Church 168
us Money - Love 178
us National Defense - Education 194
us Career - Family 198
us Cold - Hot 200
us Security - Freedom 207
us Jocks - Nerds 220
us Reason - Emotions 221
us Winter - Summer 234 
mean_es <- exp_filtered_countries %>%
  filter(!(domain %in% BAD_DOMAINS1)) %>%
  add_residuals(lm(D ~ task_order + sex + age + block_order + education,
                   data = exp_filtered)) %>%
  group_by(residence, domain) %>%
  multi_boot_standard(col = "resid", na.rm = T) 

mean_es_wide <- mean_es %>%
  left_join(lang_es, by = c("domain" = "test")) %>%
  select(-ci_lower, -ci_upper) %>%
  spread(residence, mean)

BNC

ggplot(mean_es_wide, aes(x = effect_size_bnc, y = uk)) +
  geom_label(aes(label = domain)) +
  geom_point() +
  ggtitle("BNC") + 
  ylab("UK participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

COCA

ggplot(mean_es_wide, aes(x = effect_size_coca, y = us)) +
  geom_label(aes(label = domain)) +
  geom_point() +
  ggtitle("COCA") + 
  ylab("US participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

Correlation plot

make_corr_plot(mean_es_wide[,-1])

Domains2

EXCLUDING: Skeptical - Trusting, Avoiding - Approaching, Determinism - Free Will, Lawyers - Politicians, Speed - Accuracy, Organized Labor - Management, State - Church, Chaos - Order

subj_counts <- exp_filtered_countries %>%
  filter(!(domain %in% BAD_DOMAINS2)) %>%
  count(residence, domain) %>%
  arrange(n) %>%
  data.frame()

kable(subj_counts)
residence domain n
uk National Defense - Education 7
uk Team - Individual 7
uk Rich People - Beautiful People 10
uk Technology - Nature 10
uk Tradition - Progress 11
uk Winter - Summer 11
uk Cold - Hot 12
uk Urban - Rural 12
uk Money - Love 14
uk Security - Freedom 14
uk Reason - Emotions 15
uk Career - Family 19
uk Jocks - Nerds 19
us Tradition - Progress 148
us Rich People - Beautiful People 152
us Team - Individual 155
us Technology - Nature 156
us Urban - Rural 164
us Money - Love 178
us National Defense - Education 194
us Career - Family 198
us Cold - Hot 200
us Security - Freedom 207
us Jocks - Nerds 220
us Reason - Emotions 221
us Winter - Summer 234 
mean_es <- exp_filtered_countries %>%
  filter(!(domain %in% BAD_DOMAINS2)) %>%
  add_residuals(lm(D ~ task_order + sex + age + block_order + education,
                   data = exp_filtered)) %>%
  group_by(residence, domain) %>%
  multi_boot_standard(col = "resid", na.rm = T) 

mean_es_wide <- mean_es %>%
  left_join(lang_es, by = c("domain" = "test")) %>%
  select(-ci_lower, -ci_upper) %>%
  spread(residence, mean)

BNC

ggplot(mean_es_wide, aes(x = effect_size_bnc, y = uk)) +
  geom_label(aes(label = domain)) +
  geom_point() +
  ggtitle("BNC") + 
  ylab("UK participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

COCA

ggplot(mean_es_wide, aes(x = effect_size_coca, y = us)) +
  geom_label(aes(label = domain)) +
  geom_point() +
    ggtitle("COCA") + 
  ylab("US participants IAT") +
  geom_smooth(method = "lm") +
  theme_classic()

Correlation plot

make_corr_plot(mean_es_wide[,-1])