I'm doing an ordinal regression (cumulative logit model), with a 4-point, self-report health assessment measure as my outcome.
My sample size is 8,070, and so far the model has 15 predictors: 8 binary/categorical and 7 continuous.
A few of the continuous variables however are scores (e.g. psychosocial risk & resilience measures), and can be broken down further into their individual subscales.
I'm wondering then how many variables would be too many for my model? The sample size shouldn't decrease, but currently there are only 169 respondents in the lowest level of the outcome variable.
tl;dr - Is there any rule of thumb that tells you how many predictors you can use in an ordinal regression? Thank you.