First, we got all data from the repository. You check the code below
ds <- step_processed
Then we realized that the number of cases were different
ds %>% count(country) %>% janitor::adorn_totals()
country n
Armenia 2992
Bolivia 2433
Colombia 2617
Georgia 2996
Ghana 2987
Kenya 3894
Laos 2845
Macedonia 4009
Philippines 3000
Serbia 3344
Sri_Lanka 2989
Ukraine 2389
Vietnam 3405
Yunnan 2017
Total 41917
We found the same sampel size when filtering the missing cases in acquiescence_bias_cor (please see the code below):
#filtering the dataset
ds %>%
filter(!is.na(acquiescence_bias_cor)) -> step_published_ds
Now everything seems to be ok and we can check the Table S1. Descriptive statistics of STEP data. in the manuscript.
We also checked the range within all items:
And now we were confident to run the Cronbach’s alpha computation. But the values were not the same. We got 0.66, but in the manuscript this value was 0.49 (Table 2.)
#Alpha
step_published_ds %>% dplyr::select(step_bfi1_ab_cor:step_bfi39_ab_cor) %>% psych::alpha(.)
Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Reliability analysis
Call: psych::alpha(x = .)
lower alpha upper 95% confidence boundaries
0.66 0.66 0.67
Reliability if an item is dropped:
Item statistics