POLS 2072Q: Quantitative Analysis in Political Science

Author

Jason Byers

Published

August 25, 2024

Welcome to our course website! Here you can find links to everything you’ll need this semester:

The course syllabus
Submit assignments at our course HuskyCT page (mortarboard icon in the upper right).
I will post code, data, and lecture notes on the course GitHub repository (the octocat icon in the upper right).

Course Description

Scholars in political science and in disciplines across the social sciences are increasingly relying on quantitative, data-driven methods to answer important questions in their field. This course provides an introduction to the study of politics through quantitative reasoning and data analysis. Like a traditional research methods course, we will cover the fundamentals of empirical research in political science including causal inference, summary statistics, data visualization, and regression. However, unlike a traditional research methods course, this course places a particular emphasis on developing technical skills used to conduct real world data analysis. Therefore, a significant amount of the coursework will be dedicated to learning how to program in the statistical computing environment, R. The goal is for you to gain a valuable skillset in data analysis that you can use in your political science classes and, more importantly, in your future careers.

Course Objectives

Together, we will strive for your individual and collective success in achieving the learning outcomes of this course. At the conclusion of this course, students will be able to:

Define and describe the varied nature of quantitative analysis in political science
Understand each step of the data lifecycle, identify potential sources of statistical and human bias, and determine their implications on the scope of inference.
“Think with data” by using statistical software to explore, analyze, visualize, and interpret nontrivial datasets with relevance and importance to the social sciences.
Identify and evaluate misuses, distortions, and misrepresentations of data and statistics.
Apply your statistical literacy to issues within political science.
Communicate clearly and persuasively with data

Course Materials

To maximize access to this class, we will use some textbooks, videos, and other resources, with a focus on the following:

Primary Text (DASS): Llaudet, Elena and Kosuke Imai. 2022. Data Analysis for Social Science: A Friendly and Practical Introduction. Princeton University Press.
Primary Text (R4DS): Wickham, Hadley and Garrett Grolemund. 2016. R for Data Science: Import Tidy, Transform, Visualize, and Model Data. O’Reilly Media. This book is freely available online. It is also available in paperback, if you prefer a hard copy. Warning: some content and the numbering system differs between print and online versions; I will exclusively refer to the free online version.

Reference Texts

Reference Text (HOPR): Grolemund, Garrett. 2014. Hands-On Programming with R: Write Your Own Functions and Simulations. O’Reilly Media. This book is freely available online. It is also available in paperback, if you prefer a hard copy. Warning: some content and the numbering system differs between print and online versions; I will exclusively refer to the free online version.
Reference Text (DSB): Cetinkaya-Rundel, Mine. 2021. Data Science in a Box. This book is freely available online.