Are the explicit measures correlated with language bias?

Read in behavior and language biases

countries_langs <- read_csv("../../data/other/countries_lang.csv") %>%
  mutate(language_name = ifelse(language_name == "Spanish; Castilian", "Spanish", language_name),
         language_name = ifelse(language_name == "Dutch; Flemish", "Dutch", language_name))


IAT_behavior_measures <- read_csv("IAT_behavior_measures.csv") %>%
  left_join(countries_langs %>% select(country_name, language_name, language_code)) %>%
  filter(type %in% c("country_fam_male", 
             "country_career_male", 
             "career_importance", 
             "family_importance",
             "actual_duties")) %>%
  group_by(type, language_code, sex) %>%
  summarize(mean = mean(mean)) 

lang_bias <- read_csv("career_effect_sizes.csv")

lang_bias_google <- read_csv("google_translate/career_effect_sizes_google.csv",
                             col_names = c("language_code",   "test",  "test_name", "lang_bias")) %>%
  filter(language_code != "he", language_code != "zu", language_code != "th") 
#,language_code !=  "ha",language_code !=  "id",language_code !=  "zh")


d <- left_join(IAT_behavior_measures, lang_bias) %>%
  mutate_if(is.character, as.factor) %>%
  filter(!is.na(lang_bias))

There are 22 languages represented here.

Measures

Behavior

ggplot(d, aes(x = mean)) +
  geom_histogram() +
  facet_wrap(~type, scales = "free_x")+
  theme_bw()

Correlation between measures - Hand-translations

d %>%
  mutate(type2 = ifelse(type %in% c("country_gender_D_score_diff", "country_means_D_score", "country_gender_D_score"), "D-score", ifelse(type %in% c("country_RT_ratio", "country_RT_mean"), "RT", "explicit"))) %>%
ggplot( aes(x = mean, y = lang_bias, group = sex, color = sex, shape = type2)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_grid(.~type2 + type, scales = "free_x", drop = T) +
  theme_bw()

d %>%
  group_by(type, sex) %>%
  do(tidy(cor.test(.$mean, .$lang_bias))) %>%
  arrange(p.value) %>%
  select(-parameter, -method, -alternative) %>%
  kable()
type sex estimate statistic p.value conf.low conf.high
country_fam_male m -0.4218447 -2.0807468 0.0505227 -0.7160935 -0.0002873
country_fam_male all -0.3369457 -1.6004551 0.1251758 -0.6641987 0.0986813
actual_duties f 0.3007289 1.4101781 0.1738504 -0.1384312 0.6410577
country_career_male m -0.2294514 -1.0542654 0.3043319 -0.5936326 0.2127370
country_fam_male f -0.1772190 -0.8052942 0.4301204 -0.5571955 0.2641240
actual_duties m 0.1680040 0.7621699 0.4548538 -0.2729371 0.5506116
family_importance f 0.1572155 0.7119426 0.4847230 -0.2831619 0.5428425
career_importance f 0.1314582 0.5930457 0.5597977 -0.3071751 0.5240233
family_importance m 0.0334524 0.1496876 0.8825103 -0.3937088 0.4487320
career_importance m 0.0235743 0.1054570 0.9170637 -0.4020299 0.4408016
country_career_male f -0.0030871 -0.0138060 0.9891216 -0.4241435 0.4190668

Correlation between measures - Google-translations

d <- left_join(IAT_behavior_measures, lang_bias_google) %>%
  mutate_if(is.character, as.factor) %>%
  filter(!is.na(lang_bias))

There are 24 languages represented here.

d %>%
  mutate(type2 = ifelse(type %in% c("country_gender_D_score_diff", "country_means_D_score", "country_gender_D_score"), "D-score", ifelse(type %in% c("country_RT_ratio", "country_RT_mean"), "RT", "explicit"))) %>%
ggplot( aes(x = mean, y = lang_bias, 
            group = sex, color = sex, shape = type2)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  facet_grid(.~type2 + type, scales = "free_x", drop = T) +
  theme_bw()

d %>%
  group_by(type, sex) %>%
  do(tidy(cor.test(.$mean, .$lang_bias))) %>%
  arrange(p.value) %>%
  select(-parameter, -method, -alternative) %>%
  kable()
type sex estimate statistic p.value conf.low conf.high
country_career_male m -0.2693992 -1.3121047 0.2030113 -0.6068470 0.1503351
country_fam_male m -0.1911728 -0.9135289 0.3708681 -0.5520001 0.2299579
actual_duties m 0.1191114 0.5626880 0.5793369 -0.2986344 0.4985531
career_importance f 0.0756430 0.3558165 0.7253672 -0.3380696 0.4648551
actual_duties f -0.0534211 -0.2509256 0.8042004 -0.4471811 0.3576836
country_fam_male all -0.0524387 -0.2462984 0.8077348 -0.4463926 0.3585424
country_career_male f -0.0522501 -0.2454098 0.8084140 -0.4462411 0.3587073
family_importance f -0.0421212 -0.1977415 0.8450630 -0.4380744 0.3675202
career_importance m -0.0281069 -0.1318850 0.8962740 -0.4266660 0.3795938
family_importance m 0.0248419 0.1165550 0.9082701 -0.3823867 0.4239898
country_fam_male f 0.0121356 0.0569254 0.9551185 -0.3931859 0.4135080