Hello,

I’m working with an ordered categorical outcome variable in my model and am trying to interpret the results. Specifically, I’m analyzing how workload (measured as `antwoordtekst`

) affects the employee loyalty index (`eNPS`

), which is categorized into three ordered levels.

Here’s the model I used:

formula ← bf(category ~ antwoordtekst + (1 + antwoordtekst || technische_sleutel), family = cumulative(“logit”))

fit_workload_1 ← brm(formula = formula, data = testset_workload, family = cumulative(“logit”), iter = 1000, chains = 2, warmup = 500, cores = parallel::detectCores())

In the output, I have the following regression coefficients:

Regression Coefficients:

Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS

Intercept[1] -6.94 0.17 -7.28 -6.62 1.01 251 437

Intercept[2] -3.50 0.15 -3.80 -3.20 1.01 321 477

antwoordtekst -1.13 0.05 -1.22 -1.04 1.01 295 491

I understand that the `Intercept[1]`

and `Intercept[2]`

represent the thresholds for the categories. Specifically, does `Intercept[1]`

indicate the logit of being in category 2 or higher, and `Intercept[2]`

indicate the logit of being in category 2 or higher? But it is very strange that both of the intercepts are negative. In the data most of the people are in category 3. How do I calculate the probability correctly?