Init

library(pacman)
p_load(kirkegaard, haven)
options(digits = 3)

Data

d = read_spss("data/GSS_panel_2010_2014.sav") %>% df_legalize_names()

Subsets

#split by names into year waves
waves = list(
  year_2010 = d["word" + letters[1:10] + "_1"],
  year_2012 = d["word" + letters[1:10] + "_2"],
  year_2014 = d["word" + letters[1:10] + "_3"]
)

IRT

#IRT fit by wave
IRTs = map(waves, irt.fa)

#score
IRT_score_dfs = map(1:3, function(i) {
  x = scoreIrt(IRTs[[i]], waves[[i]])
  
  x2 = data_frame(
    x = x$theta1 %>% standardize()
  )
  names(x2) = names(waves)[i]
  x2
})

#combine
IRT_scores = IRT_score_dfs %>% as.data.frame() %>% as_data_frame()

#cors
wtd.cors(IRT_scores)

##           year_2010 year_2012 year_2014
## year_2010     1.000     0.678     0.700
## year_2012     0.678     1.000     0.676
## year_2014     0.700     0.676     1.000

#plots
GG_scatter(IRT_scores, "year_2010", "year_2012")

GG_scatter(IRT_scores, "year_2010", "year_2014")

GG_scatter(IRT_scores, "year_2012", "year_2014")

#write data
IRT_scores %>% write_csv("data/IRT_scores.csv", na = "")