Chapter 8 covered generalized linear models (GLMs) for survey variables that are measured on a binary or dichotomous scale. The aim of this chapter is to introduce generalized linear modeling techniques for three other types of dependent variables that are common in survey data sets: nominal categorical variables, ordinal categorical variables, and counts of events or outcomes. Chapter 8 laid the foundation for generalized linear modeling, and this chapter will emphasize specific methods and software applications for three principal methods. Section 9.2 will introduce the “baseline” multinomial logit regression model for a survey variable with three or more nominal response categories. The cumulative logit model for dependent variables that are measured on an ordinal scale will be covered in Section 9.3. Regression methods for dependent variables that are counts (e.g., number of events, attributes), including Poisson regression models and negative binomial regression models, are presented in Section 9.4. Stata software will be used to illustrate the applications of these methods, but the reader is encouraged to visit the companion Web site for this text to find each example replicated in the other major software systems that support these advanced modeling procedures.