OVERVIEW

In this chapter we provide an introduction to two common extensions of multiple regression models. The first deals with cross-sectional models where the dependent variable is categorical rather than continuous. The second involves time-series models, where the variables of interest are measured repeatedly over time. Throughout the chapter, we use examples from a variety of research situations to illustrate the important issues that must be addressed in each research situation.

EXTENSIONS OF OLS

We have come a long way in the understanding and use of regression analysis in political science. We have learned, mathematically, where OLS coefficients come from; we have learned how to interpret those coefficients substantively; and we have learned how to use OLS to control for other possible causes of the dependent variable. In Chapter 10, we introduced dummy variables, having used them as independent variables in our regression models. In this chapter, we extend this focus to research situations in which the dependent variable is a dummy variable. Such situations are common in political science, as many of the dependent variables that we find ourselves interested in – such as, whether or not an individual voted in a particular election, or whether or not two countries engaged in a dispute escalate the situation to open warfare – are dummy variables.