This is an undergraduate level method course. I’m the teaching assistance for Professor Jude Hays. My main duties are two folds: 1) organizing students’ discussions during the class, 2) teaching students to use R during recitations each weak.

Course Description

This course introduces students to research methods in political science. Students will learn how political scientists draw statistical and causal inferences from experimental and observational data to evaluate positive political theory, theory intended to explain the politics we observe. Do first-past-the-post electoral rules produce two party systems? Do voters rely on partisan cues? Do democracies go to war with other democracies? The learning objectives are to 1) understand how political scientists use theory and evidence (i.e., data) to answer important questions like these, and 2) develop basic skills for data management and analysis.

The course is organized into four main parts. Part I provides foundational knowledge regarding positive political theory, types of inference, and probability. Part II focuses on random samples and statistical inference. Part III explores random assignment and causal inference with experiments. Part IV presents the idea of random disturbances and develops the model-based approach to statistical and causal inference. A more detailed course schedule with nested subtopics is presented below.

Course Schedule

• PART I: Foundations

• Week 1 Course Intro
• Week 2 Political Science Theory
• Week 3 Causal vs. Statistical Inference
• Week 4 Probability Theory
• Week 5 Random Variables

• PART II: Random Samples

• Week 6 Populations and Random Samples
• Week 7 Hypothesis Testing with Random Samples
• Week 8 Tabular Analysis

• PART III: Random Assignments

• Week 9 Experimental Research in Political Science
• Week 10 Hypothesis Testing with Random Assignment

• PART IV: Random Disturbances

• Week 11 Research Design for Observational Studies
• Week 12 Matching Methods
• Week 13 Hypothesis Testing with Random Disturbances
• Week 14 Multiple Regression Models

• FINAL EXAM