In this talk, we will show how to use simulations in Stata to explore to what extent and under what circumstances a test is problematic. We will illustrate this for a set of tests of the parallel regression assumption in ordered logit and probit models: the Brant, likelihood ratio, Wald, score,...

As Long & Freese show, it can often be helpful to compute predicted/expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations – factor variables and the margins command. These can do many of the things that were previously done by...

Ordered logit/probit models are among the most popular ordinal regression techniques. However, these models often have serious problems. The proportional odds/parallel lines assumptions made by these methods are often violated. Further, because of the way these models are identified, they have...

The assumptions of the ordered logit/probit models estimated by ologit and oprobit are often violated. When an ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are...

When a binary or ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are biased. Heterogeneous choice/ location-scale models explicitly specify the determinants of...

-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-...