1 Introduction

Science involves using observations of the world to make conclusions about the world. In political science, these observations are related to politics, and, in quantitative political science, these observations are analyzed using numbers. This course is focused on quantitative political science and is designed to train you to:

  1. Understand published quantitative political science,
  2. Conduct your own quantitative political science, and
  3. Communicate the results of your quantitative political science verbally and visually.

For example, we would like you to be able to explain statistical output like this:

Figure 3 of Jaclyn Kaslovsky. 2022. “Senators at home: Local attentiveness and policy representation in Congress”. American Political Science Review 116(2): 645-661.

And to create visualizations like this, using a program called Stata:

Or visualizations such as this, using a program called R:

This course will focus on making correct inferences. An inference is a conclusion about something that is not observed, based on things that are observed. Causal inference is about what causes what, such as the extent, if any, to which a U.S. resident’s attitudes about gun policy influence their vote for U.S. president. Descriptive inference does not concern causality, such as the association between a U.S. resident’s attitudes about gun policy and their vote for U.S. president.