Ordered categorical responses can be analyzed with different kinds of logistic regression models, the most popular being the cumulative logit or proportional odds model. Alternatively, ordinal probit models can be specified. When the data have a nested structure, with repeated observations for the same individual (as in longitudinal or panel data), or students nested in schools, these models can be extended by including random effects. I will describe the models and show how they can be estimated using gllamm. I will mention some elaborations of the models such as nonproportional odds and heteroskedastic errors. Finally, I will discuss how to obtain different types of predicted probabilities for these models to assess model fit, to visualize the model graphically, and to make inferences for individual units.
