Multilevel modeling of ordinal responses
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.
When requesting a correction, please mention this item's handle: RePEc:boc:msug09:04. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.