Resource list
Readings, courses, and materials that go beyond this book
The book is a primer: it teaches research design for computational and AI-assisted methods, not the full technical depth of any one method. This page points to where to go next.
Foundational texts
- Salganik, Bit by Bit: Social Research in the Digital Age (2017): free online. The best complement to this book; read it alongside Chapters 3 and 4.
- Grimmer, Roberts & Stewart, Text as Data (2022): the standard reference for computational text analysis; extends Chapters 5 and 6.
- Gentzkow & Shapiro, Code and Data for the Social Sciences (2014): free PDF. Short and practical; the backbone of Chapter 4’s workflow section.
- Healy, Data Visualization: A Practical Introduction (2018): free online. Pairs with Chapter 9.
Methods and technique
- Barabási, Network Science: free online.
- Kirk, Data Visualisation: A Handbook for Data Driven Design: the design process used in Chapters 9 and 10.
- James et al., An Introduction to Statistical Learning: free PDF, Python and R editions.
AI-assisted research
The methods literature here is moving quickly; these are stable starting points rather than a current survey.
- Stuhler, Ton & Ollion, “From Codebooks to Promptbooks” (SMR, 2025): the source of the promptbook framing in Chapter 6.
- Bisbee et al., “Synthetic Replacements for Human Survey Data?” (Political Analysis, 2024): the variance-collapse finding in Chapter 7.
- Egami et al., “Using Imperfect Surrogates for Downstream Inference” (NeurIPS, 2023): what to do when model-coded variables enter an estimate.
- Broska, Howes & van Loon, “The Mixed Subjects Design” (SMR, 2025): combining human and model observations without pretending they are interchangeable.
Courses and tutorials
- SICSS: the Summer Institutes in Computational Social Science. Materials from past years are free and comprehensive.
- The Turing Way: a community handbook on reproducible research.
Data sources
See the data management tool guide for the maintained repository list.