CDA @ IU: Stat 503 and Soc 650: Categorical Data Analysis is a second course in applied regression model that is normally taught Fall Semester. This course assumes a prior course, such as Soc 554 or Stat 501, that deals with models for dependent variable that are continuous. These include the linear regression model, seemingly unrelated regressions, and systems of simultaneous equations. Soc 650 and Stat 503 deals with regression models in which the dependent variable is categorical. Such models include probit and logit for binary outcomes, ordered logit and ordered probit for ordinal outcomes, multinomial logit for nominal outcomes, and Poisson regression and related models for count outcomes. The prerequisite for this class is a prior course in regression. The class uses Stata. Sometimes there are more students who want to take this class than there are seats in the class; for enrollment information click here.

ICPSR workshop on Categorical Data Analysis: I regularly teach a one-week class on categorical data analysis as part of ICPSR's Summer Program. The workshop examines the most important regression models for binary, ordinal, nominal and count outcomes. A variety of practical methods for interpreting the nonlinear models are presented. Statistical testing and assessing fit is also illustrated with a series of real-world examples.

Short courses and workshops: I frequently teach short courses on categorical data analysis, Stata, the workflow of data analysis and other topics. Contact me for further details.

Soc 651 Multivariate Analysis: Planned for Fall 2011.

ICPSR workshop on Categorical Data Analysis - the right-hand-side of the model: Binary logit and probit are the most commonly used regression models for categorical outcomes. While these models are similar in many respects to linear regression, interpretation is more complicated since the models are nonlinear. This workshop begins by considering the general objectives of interpretation for any regression-type model and then considers why achieving these objectives is more difficult when models are nonlinear. Building on this framework, the binary regression model is developed as a nonlinear model for predicting the probability of a binary outcome. After a review of methods of estimation, the focus turns to interpretation, specification, and testing. Basic methods of interpretation, including odds ratios, ideal types, predicted probabilities, and graphical methods, are illustrated with real-world examples. We begin by applying these methods to simple models that include only binary and continuous explanatory variables. These models are used to illustrate interpretation, testing, and assessing fit. With the fundamental tools of interpretation and testing in hand, we examine a set of increasingly complex specifications that are often encountered in substantive research. These includes the use of nominal and ordinal explanatory variables, group comparisons, interactions, and additional forms of nonlinearity. While these extensions to model specification are illustrated with binary models, the ideas can be readily adapted to other regression models. Indeed, the binary model is an essential building block for creating models such as ordinal regression, multinomial logit, item response models, bivariate probit, and latent mixture models. These models will be briefly discussed the last day of class. The labs will show how to apply each of the methods discussed in lecture. While no prior knowledge of Stata is assumed, attendees should be familiar with linear regression. Last taught in 2002.