OVERVIEW

Given our focus on causality, what research strategies do political scientists use to investigate causal relationships? Generally speaking, the controlled experiment is the foundation for scientific research. And some political scientists use experiments in their work. However, owing to the nature of our subject matter, most political scientists adopt one of two types of “observational” research designs that are intended to mimic experiments. The cross-sectional observational study focuses on variation across individual units (like people or countries). The time-series observational study focuses on variation in aggregate quantities (like presidential popularity) over time. What is an “experiment” and why is it so useful? How do observational studies try to mimic experimental designs? Most important, what are the strengths and weaknesses of each of these three research designs in establishing causal relationships between concepts? That is, how does each one help us to get across the four causal hurdles identified in Chapter 3? Relatedly, we introduce issues relating to the selection of samples of cases to study in which we are not able to study the entire population of cases to which our theory applies. This is a subject that will feature prominently in many of the subsequent chapters.

COMPARISON AS THE KEY TO ESTABLISHING CAUSAL RELATIONSHIPS

So far, you have learned that political scientists care about causal relationships. You have learned that most phenomena we are interested in explaining have multiple causes, but our theories typically deal with only one of them while ignoring the others.