Hi everyone,

I am not a statistician and not sure how to proceed with my analysis. I wanted to run an ordinal model in brms (cumulative(probit)) on data from a 7-point-likert-scale. I have two factors (subject shift and group) and two random terms. My main focus is to see if one of the factors (subject shift) has a negative effect or not (if it has a positive effect, it does not matter). However, the proportional odds assumption is violated. In trying to use a different method to test for this violation, I ran an adjacent category model using acat(probit). Here, I find that my factor of interest behaves differently for one threshold.

This is the output:

```
Family: acat
Links: mu = probit; disc = identity
Formula: answer ~ cs(subject_shift) + cs(diagnosis) + (1 | subj_uid) + (1 | item)
Data: df_all_subjects %>% filter(type %in% c("c", "d")) (Number of observations: 1066)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
Group-Level Effects:
~item (Number of levels: 24)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.10 0.02 0.07 0.15 1.00 1452 2139
~subj_uid (Number of levels: 90)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.14 0.02 0.10 0.18 1.00 1426 2376
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] -0.21 0.10 -0.40 -0.02 1.00 3536 2811
Intercept[2] 0.09 0.10 -0.11 0.29 1.00 2886 3034
Intercept[3] -0.13 0.11 -0.35 0.07 1.00 2426 2972
Intercept[4] -0.33 0.09 -0.51 -0.15 1.00 2554 2594
Intercept[5] -0.29 0.07 -0.43 -0.15 1.00 2771 2703
Intercept[6] -0.27 0.06 -0.39 -0.15 1.00 2742 3024
subject_shift1[1] -0.36 0.18 -0.72 0.00 1.00 3392 2892
subject_shift1[2] 0.49 0.19 0.11 0.88 1.00 2876 2951
subject_shift1[3] -0.01 0.21 -0.41 0.40 1.00 2741 2768
subject_shift1[4] -0.08 0.18 -0.42 0.27 1.00 2529 3220
subject_shift1[5] -0.24 0.14 -0.51 0.02 1.00 2692 2797
subject_shift1[6] -0.10 0.11 -0.31 0.11 1.00 3328 2767
diagnosis1[1] -0.58 0.19 -0.95 -0.21 1.00 3724 2875
diagnosis1[2] 0.15 0.19 -0.22 0.53 1.00 3060 3040
diagnosis1[3] -0.17 0.20 -0.56 0.21 1.00 2775 3013
diagnosis1[4] 0.00 0.17 -0.33 0.34 1.00 2759 2817
diagnosis1[5] 0.03 0.14 -0.24 0.30 1.00 3272 3145
diagnosis1[6] -0.01 0.11 -0.23 0.20 1.00 4171 3363
Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
is a crude measure of effective sample size, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```

If I read the output correcty, I would say, subject shift has a statistically robust positive effect on category 3 as opposed to category 2. I am not sure how to proceed from here. Does it make sense to report this effect and run the ordinal model nevertheless, because the positive effect is not interesting to me? But it might still influence the models output. Or is there a way to run a partial proportional odds model in brms?

All the best and thanks for your help,

Juliane