Conspiracy theories and political in USA

Init

library(kirkegaard)

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load_packages(
  haven,
  mirt
)

## Loading required package: stats4
## Loading required package: lattice

theme_set(theme_bw())

options(
    digits = 3
)

Data

list = read_csv("data/list.csv") %>% df_legalize_names() %>% 
  mutate(
    Question_wording2 = Question_wording %>% str_remove_all("\\*") %>% str_trim(),
    Question_wording2 = Question_wording2 %>% str_wrap(width = 80),
  )

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

## Rows: 39 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Question wording, code
## dbl (2): Percent Believe, reverse
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

d = read_dta("data/May 2021 clean.dta")

#more variables
d %<>% mutate(
  net_trump = trumpft - bidenft
)

theories = d %>% 
  select(aids1:cexaggerate)

#mean by theory
theory_means = theories %>% 
  colMeans(na.rm = T) %>%
  as.list() %>% 
  as.data.frame() %>% 
  gather() %>% 
  inner_join(
    list %>% 
      select(Question_wording, code),
    by = c("key" = "code")
  )

Analysis

#factor loadings
mirt_fit = mirt(
  theories,
  1,
  itemtype = case_when(
    map_lgl(theories, ~(max(.x, na.rm = T) == 1)) ~ "2PL",
    TRUE ~ "graded"
  )
)

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Log-Lik: -65550.010, Max-Change: 0.00019Iteration: 270, Log-Lik: -65550.010, Max-Change: 0.00021Iteration: 271, Log-Lik: -65550.010, Max-Change: 0.00016Iteration: 272, Log-Lik: -65550.010, Max-Change: 0.00018Iteration: 273, Log-Lik: -65550.010, Max-Change: 0.00022Iteration: 274, Log-Lik: -65550.010, Max-Change: 0.00015Iteration: 275, Log-Lik: -65550.009, Max-Change: 0.00019Iteration: 276, Log-Lik: -65550.009, Max-Change: 0.00020Iteration: 277, Log-Lik: -65550.009, Max-Change: 0.00016Iteration: 278, Log-Lik: -65550.009, Max-Change: 0.00017Iteration: 279, Log-Lik: -65550.009, Max-Change: 0.00021Iteration: 280, Log-Lik: -65550.009, Max-Change: 0.00014Iteration: 281, Log-Lik: -65550.009, Max-Change: 0.00018Iteration: 282, Log-Lik: -65550.009, Max-Change: 0.00019Iteration: 283, Log-Lik: -65550.009, Max-Change: 0.00015Iteration: 284, Log-Lik: -65550.009, Max-Change: 0.00016Iteration: 285, Log-Lik: -65550.009, Max-Change: 0.00020Iteration: 286, Log-Lik: -65550.009, Max-Change: 0.00014Iteration: 287, Log-Lik: -65550.009, Max-Change: 0.00017Iteration: 288, Log-Lik: -65550.009, Max-Change: 0.00019Iteration: 289, Log-Lik: -65550.009, Max-Change: 0.00015Iteration: 290, Log-Lik: -65550.009, Max-Change: 0.00016Iteration: 291, Log-Lik: -65550.009, Max-Change: 0.00020Iteration: 292, Log-Lik: -65550.009, Max-Change: 0.00013Iteration: 293, Log-Lik: -65550.009, Max-Change: 0.00017Iteration: 294, Log-Lik: -65550.009, Max-Change: 0.00018Iteration: 295, Log-Lik: -65550.009, Max-Change: 0.00014Iteration: 296, Log-Lik: -65550.008, Max-Change: 0.00015Iteration: 297, Log-Lik: -65550.008, Max-Change: 0.00019Iteration: 298, Log-Lik: -65550.008, Max-Change: 0.00013Iteration: 299, Log-Lik: -65550.008, Max-Change: 0.00016Iteration: 300, Log-Lik: -65550.008, Max-Change: 0.00017Iteration: 301, Log-Lik: -65550.008, Max-Change: 0.00014Iteration: 302, Log-Lik: -65550.008, Max-Change: 0.00015Iteration: 303, Log-Lik: -65550.008, Max-Change: 0.00018Iteration: 304, Log-Lik: -65550.008, Max-Change: 0.00012Iteration: 305, Log-Lik: -65550.008, Max-Change: 0.00015Iteration: 306, Log-Lik: -65550.008, Max-Change: 0.00017Iteration: 307, Log-Lik: -65550.008, Max-Change: 0.00013Iteration: 308, Log-Lik: -65550.008, Max-Change: 0.00014Iteration: 309, Log-Lik: -65550.008, Max-Change: 0.00017Iteration: 310, Log-Lik: -65550.008, Max-Change: 0.00012Iteration: 311, Log-Lik: -65550.008, Max-Change: 0.00015Iteration: 312, Log-Lik: -65550.008, Max-Change: 0.00016Iteration: 313, Log-Lik: -65550.008, Max-Change: 0.00013Iteration: 314, Log-Lik: -65550.008, Max-Change: 0.00014Iteration: 315, Log-Lik: -65550.008, Max-Change: 0.00017Iteration: 316, Log-Lik: -65550.008, Max-Change: 0.00011Iteration: 317, Log-Lik: -65550.008, Max-Change: 0.00014Iteration: 318, Log-Lik: -65550.008, Max-Change: 0.00016Iteration: 319, Log-Lik: -65550.008, Max-Change: 0.00012Iteration: 320, Log-Lik: -65550.008, Max-Change: 0.00013Iteration: 321, Log-Lik: -65550.008, Max-Change: 0.00016Iteration: 322, Log-Lik: -65550.008, Max-Change: 0.00011Iteration: 323, Log-Lik: -65550.008, Max-Change: 0.00014Iteration: 324, Log-Lik: -65550.007, Max-Change: 0.00015Iteration: 325, Log-Lik: -65550.007, Max-Change: 0.00012Iteration: 326, Log-Lik: -65550.007, Max-Change: 0.00013Iteration: 327, Log-Lik: -65550.007, Max-Change: 0.00016Iteration: 328, Log-Lik: -65550.007, Max-Change: 0.00011Iteration: 329, Log-Lik: -65550.007, Max-Change: 0.00013Iteration: 330, Log-Lik: -65550.007, Max-Change: 0.00014Iteration: 331, Log-Lik: -65550.007, Max-Change: 0.00011Iteration: 332, Log-Lik: -65550.007, Max-Change: 0.00012Iteration: 333, Log-Lik: -65550.007, Max-Change: 0.00015Iteration: 334, Log-Lik: -65550.007, Max-Change: 0.00010Iteration: 335, Log-Lik: -65550.007, Max-Change: 0.00013Iteration: 336, Log-Lik: -65550.007, Max-Change: 0.00014Iteration: 337, Log-Lik: -65550.007, Max-Change: 0.00011Iteration: 338, Log-Lik: -65550.007, Max-Change: 0.00012Iteration: 339, Log-Lik: -65550.007, Max-Change: 0.00014Iteration: 340, Log-Lik: -65550.007, Max-Change: 0.00010

mirt_fit

## 
## Call:
## mirt(data = theories, model = 1, itemtype = case_when(map_lgl(theories, 
##     ~(max(.x, na.rm = T) == 1)) ~ "2PL", TRUE ~ "graded"))
## 
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 340 EM iterations.
## mirt version: 1.42 
## M-step optimizer: BFGS 
## EM acceleration: Ramsay 
## Number of rectangular quadrature: 61
## Latent density type: Gaussian 
## 
## Log-likelihood = -65550
## Estimated parameters: 145 
## AIC = 131390
## BIC = 132204; SABIC = 131743

mirt_fit %>% summary()

##                  F1     h2
## aids1         0.210 0.0441
## hostage       0.555 0.3078
## jfk           0.385 0.1480
## moon          0.714 0.5095
## govassass     0.727 0.5281
## mlk           0.372 0.1385
## bobby         0.446 0.1987
## believer      0.676 0.4570
## cpurpose      0.858 0.7353
## hollywood     0.780 0.6082
## clintonnuke   0.764 0.5843
## rothschilds   0.782 0.6112
## vaccine       0.884 0.7821
## epstein       0.564 0.3181
## simpson       0.587 0.3444
## fluoride      0.839 0.7035
## globalwarming 0.621 0.3862
## ufo           0.497 0.2468
## mindcontrol   0.804 0.6464
## binladen      0.736 0.5420
## pharma        0.840 0.7057
## fdrlied       0.656 0.4310
## sandyhook     0.742 0.5507
## birther       0.682 0.4646
## deepstate     0.746 0.5558
## onepercent    0.501 0.2510
## killpolice    0.660 0.4357
## tracking      0.855 0.7310
## repsteal      0.273 0.0746
## billgates     0.856 0.7331
## fda           0.797 0.6355
## vaccine2      0.862 0.7432
## lightbulbs    0.795 0.6318
## truther       0.798 0.6363
## cellphone2    0.772 0.5962
## gmos          0.741 0.5493
## soros         0.803 0.6449
## cellphone     0.736 0.5419
## cexaggerate   0.737 0.5427
## 
## SS loadings:  19.3 
## Proportion Var:  0.495 
## 
## Factor correlations: 
## 
##    F1
## F1  1

#save scores
mirt_fit_scores = fscores(mirt_fit, full.scores = T, full.scores.SE = T)

#reliability
empirical_rxx(mirt_fit_scores)

##    F1 
## 0.957

d$general_conspiracy = mirt_fit_scores[, 1] %>% standardize()

#plot distribution
d$general_conspiracy %>% 
  GG_denhist()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#ideology correlation
GG_scatter(d, "ideo", "general_conspiracy")

## `geom_smooth()` using formula = 'y ~ x'

#trump
GG_scatter(d, "trumpft", "general_conspiracy") +
  labs(title = "Trump support and belief in conspiracy theories", x = "Trump support (feeling thermometer)", y = "Belief in conspiracy theories, general score")

## `geom_smooth()` using formula = 'y ~ x'

GG_save("figs/trump_conspiracy.png")

## `geom_smooth()` using formula = 'y ~ x'

#net trump vs. biden
GG_scatter(d, "net_trump", "general_conspiracy") +
  labs(title = "Net Trump vs. Biden support and belief in conspiracy theories", x = "Net Trump support (feeling thermometer)", y = "Belief in conspiracy theories, general score")

## `geom_smooth()` using formula = 'y ~ x'

#ACTS and Trump
GG_scatter(d, "trumpft", "conthink")

## `geom_smooth()` using formula = 'y ~ x'

#correlations
d %>% 
  select(
    trumpft, bidenft, net_trump,
    conthink, general_conspiracy
  ) %>% 
  GG_heatmap()

# list %>% 
#   mutate(
#     Question_wording2 = Question_wording2 %>% fct_reorder(Percent_Believe2)
#   ) %>% 
#   ggplot(aes(Percent_Believe2, Question_wording2)) +
#   geom_point() +
#   labs(title = "Belief in conspiracy theories in USA", x = "Belief %", y = "Theory") +
#   theme_minimal()
# 
# #belief plot
# theories %>% 
#   map_dfc(~kirkegaard::rescale(.x, new_min = 0, new_max = 1)) %>%
#   gather() %>% 
#   ggplot(aes(value, key)) +
#   #a historam on each line
#   geom_histogram(stat = "identity", fill = "grey", alpha = 0.5) +
#   labs(title = "Belief in conspiracy theories in USA", x = "Belief", y = "Count") +
#   theme_minimal()

Meta

#versions
write_sessioninfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Linux Mint 21.1
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_DK.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_DK.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Berlin
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] mirt_1.42             lattice_0.22-5        haven_2.5.4          
##  [4] kirkegaard_2024-06-07 psych_2.4.6.26        assertthat_0.2.1     
##  [7] weights_1.0.4         Hmisc_5.1-3           magrittr_2.0.3       
## [10] lubridate_1.9.3       forcats_1.0.0         stringr_1.5.1        
## [13] dplyr_1.1.4           purrr_1.0.2           readr_2.1.5          
## [16] tidyr_1.3.1           tibble_3.2.1          ggplot2_3.5.1        
## [19] tidyverse_2.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] mnormt_2.1.1         pbapply_1.7-2        gridExtra_2.3       
##   [4] testthat_3.2.1.1     permute_0.9-7        rlang_1.1.4         
##   [7] compiler_4.4.1       mgcv_1.9-1           gdata_3.0.0         
##  [10] systemfonts_1.1.0    vctrs_0.6.5          crayon_1.5.3        
##  [13] pkgconfig_2.0.3      shape_1.4.6.1        fastmap_1.2.0       
##  [16] backports_1.5.0      labeling_0.4.3       utf8_1.2.4          
##  [19] rmarkdown_2.28       sessioninfo_1.2.2    tzdb_0.4.0          
##  [22] nloptr_2.1.1         ragg_1.3.2           bit_4.0.5           
##  [25] xfun_0.47            glmnet_4.1-8         jomo_2.7-6          
##  [28] cachem_1.1.0         jsonlite_1.8.8       highr_0.11          
##  [31] pan_1.9              Deriv_4.1.3          broom_1.0.6         
##  [34] parallel_4.4.1       cluster_2.1.6        R6_2.5.1            
##  [37] bslib_0.8.0          stringi_1.8.4        parallelly_1.38.0   
##  [40] boot_1.3-30          rpart_4.1.23         brio_1.1.5          
##  [43] jquerylib_0.1.4      Rcpp_1.0.13          iterators_1.0.14    
##  [46] knitr_1.48           future.apply_1.11.2  snow_0.4-4          
##  [49] audio_0.1-11         base64enc_0.1-3      R.utils_2.12.3      
##  [52] Matrix_1.6-5         splines_4.4.1        nnet_7.3-19         
##  [55] timechange_0.3.0     tidyselect_1.2.1     rstudioapi_0.16.0   
##  [58] yaml_2.3.10          vegan_2.6-6.1        codetools_0.2-19    
##  [61] dcurver_0.9.2        curl_5.2.2           listenv_0.9.1       
##  [64] withr_3.0.1          evaluate_0.24.0      foreign_0.8-86      
##  [67] future_1.34.0        survival_3.7-0       pillar_1.9.0        
##  [70] mice_3.16.0          checkmate_2.3.2      foreach_1.5.2       
##  [73] generics_0.1.3       vroom_1.6.5          hms_1.1.3           
##  [76] munsell_0.5.1        scales_1.3.0         minqa_1.2.8         
##  [79] globals_0.16.3       gtools_3.9.5         glue_1.7.0          
##  [82] RPushbullet_0.3.4    tools_4.4.1          data.table_1.16.0   
##  [85] lme4_1.1-35.5        beepr_2.0            SimDesign_2.17.1    
##  [88] grid_4.4.1           colorspace_2.1-1     nlme_3.1-165        
##  [91] htmlTable_2.4.3      Formula_1.2-5        cli_3.6.3           
##  [94] textshaping_0.4.0    fansi_1.0.6          gtable_0.3.5        
##  [97] R.methodsS3_1.8.2    sass_0.4.9           digest_0.6.37       
## [100] progressr_0.14.0     GPArotation_2024.3-1 farver_2.1.2        
## [103] htmlwidgets_1.6.4    htmltools_0.5.8.1    R.oo_1.26.0         
## [106] lifecycle_1.0.4      mitml_0.4-5          bit64_4.0.5         
## [109] MASS_7.3-61

#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"
    )
}