ABSTRACT
This chapter introduces 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. The multinomial logit regression model is the natural extension of the simple binary logistic regression model to survey responses that have three or more distinct categories. The interpretation of the parameter estimates in a multinomial logit regression model is a natural extension of the interpretation of effects in the simple logistic regression model. Regression models for dependent variables that are discrete counts of events or outcomes are also important in the analysis of survey data. The chapter considers four related generalized linear models (GLMs) for regression modeling of count data: the Poisson regression model, the negative binomial regression model, and "zero-inflated" versions of both the Poisson and negative binomial models.
