General resources

Textbooks

• Kosuke Imai, Quantitative Social Science: An Introduction (Princeton UP, 2017)
• Diez, Cetinkaya-Rundel, and Barr, OpenIntro Statistics (OpenIntro, 4th ed, 2019)
• Francisco Urdinez and Andres Cruz, R for Political Data Science: A Practical Guide (CRC, 2020)
• Ismay, Kim, and Valdivia, Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Online/CRC, 2025)

Git and Version Control

• Happy Git with R - Essential guide for R users
• Git Exercises - Interactive exercises to practice Git
• GitHub’s Official Guides
• Pro Git Book (free online)

R Resources

• Wickham, Cetinkaya-Rundel, and Grolemund, R for Data Science (Online/O’Reilly, 2nd ed, 2023)
• Arel-Bundock, Greifer, and Heiss, Model to Meaning: How to Interpret Statistical Models Using marginaleffects for R and Python (online, 2025)
• Rodrigues, Building reproducible analytical pipelines with R (online, 2023)

Causal Inference

• Nick Huntington-Klein, The Effect: An Introduction to Research Design and Causality (Chapman and Hall/CRC, 2021)
• Scott Cunningham, Causal Inference: The Mixtape (Yale University Press, 2021)

Data Sources

• World Bank Open Data
• Quality of Government Institute
• V-Dem: Varieties of Democracy
• ANES: American National Election Studies
• ESS: European Social Survey