Gologit2: Generalized Logistic Regression Models for Ordinal Dependent Variables

-gologit2- is a user-written program that estimates generalized logistic regression models for ordinal dependent variables. The actual values taken on by the dependent variable are irrelevant except that larger values are assumed to correspond to "higher" outcomes. A major strength of -gologit2- is that it can also estimate two special cases of the generalized model: the proportional odds model and the partial proportional odds model. Hence, -gologit2- can estimate models that are less restrictive than the proportional odds/parallel lines models estimated by –ologit- (whose assumptions are often violated) but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression. The –autofit- option greatly simplifies the process of identifying partial proportional odds models that fit the data. Two alternative but equivalent parameterizations of the model that have appeared in the literature are both supported. Other key advantages of -gologit2- include support for linear constraints, Stata 8.2 survey data (svy) estimation, and the computation of estimated probabilities via the –predict- command. -gologit2- is inspired by Vincent Fu’s –gologit- program and is backward compatible with it but offers several additional powerful options.