This workshop deals with the most important regression models for binary, ordinal, nominal and count outcomes. While advances in software make it simple to estimate these models, the effective interpretation of these nonlinear models is a vexingly difficult art that requires time, practice, and a firm grounding in the goals of your analysis and the characteristics of your model.

The workshop begins by discussing the general objectives for interpreting results from any regression model and considers why these objectives are more difficult in nonlinear models. Concepts of estimation, testing, and identification are introduced in a quick review of the linear regression model. These ideas are used to develop the binary logit and probit models. Advanced methods of interpretation are introduced using Stata's margins command, with detailed examples on how to compute and interpreting average marginal effects, the distribution of effects, and related methods. Concepts from the binary model are used to develop the multinomial logit model for nominal outcomes, followed by the development of several models for ordinal outcomes. Finally, models for count data, including Poisson regression, negative binomial regression, and zero modified models are presented.

Prerequisites: Participants must be thoroughly familiar with linear regression. While familiarity with Stata is recommended, the labs provide step by step instructions for those new to Stata.

This workshop is offered in collaboration with the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan.