I have categorical survey data on people's attitudes towards a certain policy area from 13 countries. The response variable is categorical, and includes 4 distinct answers that cannot be ordered.
I would like to build a multi-level random-intercept and random-slope multinomial model. The problem is, that the number of level-2 cases is just 13, and the model does not converge, at least not in its multinomial form.
So, as a second-best option, I am thinking about recoding the response variable into a binary form, run a series of multilevel logistic regressions, and then use predicted probabilities to show how the probability that a certain category of interest will be selected depends on my explanatory variables. This, apparently, is just a second-best option. I would like to know what the possible risks are of taking this approach, and what objections (from reviewers, supervisors etc.) should I expect.