Abstract. This chapter considers situations where the response of interest, y, takes on values 0 or 1, a binary dependent variable. To illustrate, one could use y to indicate whether or not a subject possesses an attribute or to indicate a choice made, for example, whether or not a taxpayer employs a professional tax preparer to file income tax returns.

Regression models that describe the behavior of binary dependent variables are more complex than linear regression models. Thus, Section 9.1 reviews basic modeling and inferential techniques without the heterogeneity components (so-called homogeneous models). Sections 9.2 and 9.3 include heterogeneity components by describing random- and fixed-effects models. Section 9.4 introduces a broader class of models known as marginal models, which can be estimated using a moment-based procedure known as generalized estimating equations.