Resource list
Readings, courses, and materials that go beyond this book
Readings this book cites and builds on, plus courses and tools: where to go next when the primer’s research-design level is not deep enough.
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.
Every book and article below is one the book actually cites and builds on. The courses, tools, and data sources at the end are practical pointers rather than sources.
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; the source of the five-task framework in Chapter 1 and the structural-versus-agnostic distinction in Chapter 7.
- Gentzkow & Shapiro, Code and Data for the Social Sciences (2014): free PDF. Short and practical; the backbone of Chapter 4’s workflow section.
- Kirk, Data Visualisation: A Handbook for Data Driven Design (2019): the four-stage design process that structures the visualization work in Chapters 9 and 10.
- Schwabish, Better Data Visualizations (2021): the source of the visual encoding hierarchy in Chapter 9.
- Franconeri et al., “The Science of Visual Data Communication” (Psychological Science in the Public Interest, 2021): DOI. The evidence behind Chapter 9 on encoding accuracy, truncated axes, and uncertainty displays.
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): DOI. The source of the promptbook framing in Chapter 6.
- Bisbee et al., “Synthetic Replacements for Human Survey Data?” (Political Analysis, 2024): DOI. The variance-collapse finding in Chapter 7.
- Broska, Howes & van Loon, “The Mixed Subjects Design” (SMR, 2025): DOI. 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.