OVERVIEW

In this chapter, we show how political scientists across a variety of subfields have used multiple regression to test theories about causal processes involving politics. In particular, we show how clever research design and accompanying statistical analyses uncover interesting patterns of causal dynamics in the domain of American presidential popularity, in economic voting in European democracies, and in patterns of international conflict and cooperation. One of the core principles of this chapter – and of the book – is that solid research design is a prerequisite for insightful data analysis.

WHY CONTROLLING FOR Z MATTERS

Thus far, the discussion of the effects of multiple regression has been mostly abstract, referring to X, Y, and Z instead of to real social phenomena that most political scientists – and most political science students – care about. Some of this abstraction is necessary, of course, but we want you to see examples from actual research on how multiple regression is used, and how including new variables can change our theoretical conclusions. That is the goal of this chapter. What you will see, in these varying examples, is how introducing new controls for variables can change our inferences about the causal structure of the political world, which is what we have emphasized throughout this book.

To highlight the changes that result to parameter estimates when we change our model by adding a new variable, we show only a small portion of the results from the different models that we feature in this chapter.