Download my Fall 2024 syllabus

Course Description

Political science and public policy researchers increasingly rely on quantitative analysis to understand political phenomena and solve policy problems. Quantitative analysis skills are also in high demand across the economy — in the private, public, and non-profit sectors. This course will introduce students to data analysis and statistical applications in political research. Topics include data processing, inferential statistics, correlation and regression, multivariate analysis, causal inference, and coding. It introduces students to the basic statistical techniques used to study politics quantitatively.

Schedule

Week 1: Introduction

• August 27: Introduction to the course and each other (Slides)
• August 29: Software
  • R and RStudio
  • Posit
  • Quarto

Week 2: Introduction

• September 3: Software Continued
  • We’ll play with R, Posit, and Quarto
• September 5: APSA
  • No Class

Week 3: The Basics

• September 10: Research Overview (Slides)
  • The Effect Chapter 1 and Chapter 2
• September 12: Probability (Slides)
  • No reading

Week 4: The Basics

• September 17: Variables (Slides)
  • The Effect Chapter 3 and Chapter 4
• September 19: Variables and Identification (Slides)
  • The Effect Chapter 5

Week 5: Regression

• September 24: Lab Day
  • Homework 1 Due
• September 26: Regression (Slides)
  • The Effect Chapter 12 and Chapter 13
  • Introduction to Modern Statistics Chapter 7

Week 6: Regression

• October 1: Lab Day
  • Homework 2 Due
• October 3: Regression (Slides)
  • Introduction to Modern Statistics Chapter 8

Week 7: Inference

• October 8: Lab Day
  • Homework 3 Due
• October 10: Inference (Slides)
  
  • Introduction to Modern Statistics Chapter 11, skim Chapter 13, Chapter 14, Chapter 15

Week 8: Inference

• October 15: Lab Day
  • Homework 4 Due
• October 17: Inference (Slides)
  • Introduction to Modern Statistics Chapter 16, Chapter 17, Chapter 18, Chapter 19

Week 9: Inference

• October 22: Lab Day
  • Homework 5 Due
• October 24: Inference (Slides)
  • Introduction to Modern Statistics Chapter 20, Chapter 21, Chapter 22, Chapter 23

Week 10: Inferential Modeling

• October 29: Lab Day
  • Homework 6 Due
• October 31: Inferential Modeling (Slides)
  • Introduction to Modern Statistics Chapter 24, Chapter 25, Chapter 26, Chapter 27

Week 11: Check In

• November 5: Election Day and Lab Day
  • Homework 7 Due
• November 7: Checking in and Final Project Planning (Slides)

Week 12: Difference-in-Differences

• November 12: Final project planning
  • Final project source due
• November 14: Difference-in-Differences (Slides)
  • The Effect Chapter 18

Week 13: Fixed Effects

• November 19: Lab Day
  • Homework 8 Due
• November 21: Fixed Effects (Slides)
  • The Effect Chapter 16, skim Causal Inference: The Mixtape Chapter 10

Week 14: Thanksgiving Break

• November 26 and 28
  • No Class

Week 15: Regression Discontinuity

• December 3: Lab Day
  • Homework 9 Due
• December 5: Regression Discontinuity (Slides)
  • The Effect Chapter 20

Week 16: Wrapping Up

• December 10: Lab Day
  • Homework 10 Due
• December 12: Wrapping Up (Slides)
• December 14: All make-up work due

Week 17: Finals Week

• Final project and presentation due before our assigned final exam time (Noon – 1:50pm on December 17)