Computational & AI-Assisted Methods for Social Sciences
Companion website to the research design primer by Ji Ma
Need help preparing data, running code, or checking a result? This companion site provides setup guides, technique primers, annotated studies, and worked notebooks for the book’s exercises. You can read and download these materials without an account or a fee.
Jump to your chapter
Each chapter page brings together its assignment and relevant primers, examples, tools, or studies. Chapters 1, 11, and 12 draw on the shared materials rather than separate pages; Chapter materials explains how the theory and exercise chapters connect.
About the book
Computational & AI-Assisted Methods for Social Sciences: A Research Design Primer (Ji Ma, SAGE, forthcoming) approaches computational and AI-assisted methods through research design. Four stages organize the book: data management, concept representation, data analysis, and scientific communication. You apply them to a literature dataset from your own field, explaining your choices and checking the results, including those produced with AI.
The book is written for graduate students and researchers in the social sciences. You can begin the conceptual chapters without a programming background; the setup guide and primers help you prepare for the exercises.
Every exercise was tested by five cohorts of graduate students, and the studies database was compiled with three research assistants. They are thanked by name on the About page.
| What it does | Where it lives | |
|---|---|---|
| Theory chapters (1, 2, 3, 5, 7, 9, 11, 12) | Build the concepts and the reasoning: what a measure commits you to, what a method can and cannot claim, what makes a result trustworthy | The book |
| Exercise chapters (4, 6, 8, 10) | Put the same concepts to work on your own data, one deliverable at a time | The book |
| This site | Setup guides, technique primers, tool lists, templates, and datasets that can be updated as software changes | Here |
The book explains research tasks independently of a programming language. This site mostly uses Python to show how a task can be implemented. You can adapt the approach to your own tools, checking that they support the operations the exercise requires.
The For researchers page introduces the book’s arguments and frameworks; course fit and a possible fifteen-week schedule are on For instructors.
Start where you are
New to computational work?
Start with a small table in a browser notebook, then work through the primers you need: text data, supervised learning, and prompting.
Teaching with the book?
Plan a course with the chapter decks, a suggested schedule, and assessment guidance for students using AI. The setup guide and primers support students who need more preparation.
Engaging it as research?
Consider the book’s eight claims, find the chapters that develop them, and explore frameworks you can adapt to your own research.
The library
- CSS Empirical Studies Database: 122 published computational studies, annotated by design stage, searchable and downloadable.
- Worked notebooks and data: three notebooks with saved outputs, plus sample network data for Chapter 8.
- Tool guides: data management, automated coding, analysis, and communication, revised as software changes.
- Resource list: where to go beyond this book.
When a model codes text, drafts code, or proposes a data structure, work through the checks alongside the technique. Inspect disagreements and errors to assess whether the result fits your research task.