OVERVIEW

In this chapter we provide introductory discussions of and advice for commonly encountered research scenarios involving multiple regression models. Issues covered include dummy independent variables, interactive specifications, influential cases, and multicollinearity.

EXTENSIONS OF OLS

In the previous two chapters we discussed in detail various aspects of the estimation and interpretation of OLS regression models. In this chapter we go through a series of research scenarios commonly encountered by political science researchers as they attempt to test their hypotheses within the OLS framework. The purpose of this chapter is twofold – first, to help you to identify when you encounter these issues and, second, to help you to figure out what to do to continue on your way.

We begin with a discussion of “dummy” independent variables and how to properly use them to make inferences. We then discuss how to test interactive hypotheses with dummy variables. We next turn our attention to two frequently encountered problems in OLS – outliers and multicollinearity. With both of these topics, at least half of the battle is identifying that you have the problem.

BEING SMART WITH DUMMY INDEPENDENT VARIABLES IN OLS

In Chapter 5 we discussed how an important part of knowing your data involves knowing the metric in which each of your variables is measured. Throughout the examples that we have examined thus far, almost all of the variables, both the independent and dependent variables, have been continuous. This is not by accident.