Hi everyone,

I ran an ordinal mixed model (cumulative(“probit”), model below) on my data and I looked at the probabilites for each category for two factor levels (subject shift = 0 and subject shift = 1) for two separate groups (diagnosis = 0 and diagnosis = 1):

I can see a tendency for the probabilites on level “subject shift = 0” to be less diverse in the group “diagnosis = 1” compared to the group “diagnosis = 0”, i.e. the ratings (ratings 1 to 7) are more likely to occur at guessing probability in the “diagnosis=1”-group, whereas the “diagnosis=0”- group seems to make more of a distinction by giving higher ratings more often. However, I want to investigate, to what extent this is statistically reliable. Does somebody have an idea, if it makes sense to run an F-test of variances of these probabilites, and how to access these proabilities? Or does somebody have a different, maybe easier way of investigating this?

Thank you very much for advice.

Juliane

model:

```
model_1_brm = brm(answer ~ 1 + subject_shift * diagnosis + # 1 is included for clarity, it will be included anyway
(1 + subject_shift|subj_uid) + (1 + subject_shift|item),
data = df_all_subjects %>% filter(type %in% c('a', 'b')),
family = cumulative("probit"),
warmup = 2000,
iter = 12000, # reduce iterations to e.g. 5000 and warmup to 1000, if you want to roughly get an impression.
save_all_pars = TRUE,# this is needed for later calculating Bayes factor
set.seed(555)
)
```