Indian IQ in the GSS (Wordsum)
16 December, 2025
Update note
This was updated to add more stringent Hispanic filtering and age normed IQs.
Init
options(digits = 2)
#remotes::install_github("tidyverse/haven")
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Data
d = read_stata("data/GSS_stata/gss7224_r1.dta")
d_vars = df_var_table(d)Recode
#race
d$race %>% as_factor() %>% table2(include_NA = F)d$race_3way = d$race %>% as_factor() %>% fct_drop()
d$race_3way %>% table2()#hispanic
d$hispanic %>% as_factor() %>% table2()d$not_hispanic = d$hispanic==1
d$not_hispanic %>% table2()d$ethnic2 = d$ethnic %>% as_factor() %>% as.character()
#too little data
#recode from ethnic
d$hispanic_ethnic = d$ethnic2 %in% c("mexico", "american indian", "puerto rico", "cuba", "other central america", "ecuador", "peru", "venezuela", "honduras", "panama", "nicaragua", "argentina", "costa rica", "bolivia", "guyana", "belize", "chile", "french guiana", "uruguay", "paraguay", "suriname", "other spanish")
d$hispanic_ethnic %>% table2()#ethnic origin
d$ethnic2 %>% table2() %>% print(n=20)## # A tibble: 107 × 3
## Group Count Percent
## <chr> <dbl> <dbl>
## 1 uncodeable 13257 17.5
## 2 germany 9936 13.1
## 3 england & wales 7883 10.4
## 4 ireland 7147 9.44
## 5 africa (general) 4779 6.31
## 6 italy 3407 4.50
## 7 mexico 3011 3.98
## 8 american indian 2764 3.65
## 9 scotland 2021 2.67
## 10 poland 1763 2.33
## 11 american only 1460 1.93
## 12 france 1234 1.63
## 13 norway 1048 1.38
## 14 sweden 951 1.26
## 15 netherlands 887 1.17
## 16 russia 840 1.11
## 17 other spanish 729 0.963
## 18 czechoslovakia 723 0.955
## 19 don't know 722 0.954
## 20 puerto rico 716 0.946
## # ℹ 87 more rows#sex
d %<>% mutate(
sex = sex %>% as_factor(),
sex_2way = case_when(
sex %in% c("female", "male") ~ sex,
.default = NA
) %>% fct_drop()
)
#year and age
d$year %<>% as.numeric()
d$age %<>% as.numeric()
#filter out hispanics
d %<>% filter(not_hispanic, !hispanic_ethnic)
#catch indians in Ethnic variable
#but we can't use this for whites, too many countries
d$Indian = (d$ethnic2 == "india")
d$Indian %>% sum()## [1] 310Analysis
Score WORDSUM
#wordsum items
wordsum_items = d %>% select(worda:wordj)
#IRT fit
wordsum_irt = mirt(
wordsum_items %>% map_df(as.numeric)
)## Warning: data contains response patterns with only NAs#scores
wordsum_irt_scores = fscores(wordsum_irt)
#save the white standard scores
d$wordsum_g = wordsum_irt_scores[, 1] %>% standardize()
#age controlled
wordsum_age_adj = kirkegaard::make_norms(
d$wordsum_g,
age = d$age,
norm_group = d$race_3way=="white"
)## Detected linear effect of age on the score (p = <0.001***). Model used.## Detected variance effect of age on the score (p = <0.001***). Model used.d$wordsum_IQ = wordsum_age_adj$data$IQIndian IQ
#examine the missing data structure
d %>% select(
Indian, race_3way, age, born, wordsum_IQ, sex_2way, year, hispanic_ethnic
) %>%
miss_patterns()#then subset to variables with no missing
d_indian = d %>% select(
Indian, race_3way, age, born, wordsum_IQ, year, sex_2way, hispanic_ethnic
) %>%
miss_filter() %>%
filter(
Indian | race_3way == "white"
)
#we need special encoding for race here
d_indian$Indian2 = tibble(
Indian = d_indian$Indian,
White = d_indian$race_3way=="white",
US_born = d_indian$born==1
) %>%
encode_combinations() %>%
fct_relevel("White, US_born")
#counts
d_indian$Indian2 %>% table2()#desc by group
describe2(
d_indian$wordsum_IQ,
d_indian$Indian2
)## New names:
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ols_list = list(
ols(wordsum_IQ ~ Indian2, data = d_indian),
ols(wordsum_IQ ~ Indian2 + sex_2way, data = d_indian),
ols(wordsum_IQ ~ Indian2 + year + sex_2way, data = d_indian)
)
ols_list %>% summarize_models()ols_list[[3]] %>%
ggaverage(terms = c("Indian2")) %>%
plot()
Meta
#versions
write_sessioninfo()## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Linux Mint 21.1
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## [115] mnormt_2.1.1 mice_3.18.0#write data to file for reuse
# d %>% write_rds("data/data_for_reuse.rds")
#OSF
if (F) {
library(osfr)
#login
osf_auth(readr::read_lines("~/.config/osf_token"))
#the project we will use
osf_proj = osf_retrieve_node("https://osf.io/XXX/")
#upload all files in project
#overwrite existing (versioning)
osf_upload(
osf_proj,
path = c("data", "figures", "papers", "notebook.Rmd", "notebook.html", "sessions_info.txt"),
conflicts = "overwrite"
)
}