> For the complete documentation index, see [llms.txt](https://laurence-wilse-samson.gitbook.io/textbooks/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://laurence-wilse-samson.gitbook.io/textbooks/empirical-research-methods/readme.md).

# About This Book

*Laurence Wilse-Samson* *NYU Wagner Graduate School of Public Service*

> **Full draft, last updated November 2026.** One of 22 textbooks freely readable online ([all books](https://lwilsesamson.com/textbooks/)), all written with AI assistance. Corrections are welcome at <laurence@lwilsesamson.com>. See the [status of this edition](/textbooks/empirical-research-methods/edition_status.md).

***

## About This Book

This book provides a comprehensive introduction to empirical research methods for graduate students and working researchers in economics and the social sciences. It takes a question-first approach: rather than organizing around techniques, it asks what different research questions require and how various methods can address them.

The book is distinctive in several ways:

* **Methodologically pluralist**: Quantitative and qualitative methods are treated as complements, not competitors. Each approach has strengths for particular questions.
* **Integration-focused**: Methods don't exist in isolation. The book emphasizes how to combine approaches—triangulating evidence, understanding mechanisms, and assessing generalizability.
* **Practically oriented**: Every chapter includes concrete guidance on implementation, common pitfalls, and when methods apply. Programming companions provide R and Python code.
* **Current**: Coverage includes modern developments—heterogeneity-robust difference-in-differences, causal forests, double machine learning—alongside foundational methods.

## Structure

The book is organized in five parts:

| Part                            | Focus                                            | Key Questions                        |
| ------------------------------- | ------------------------------------------------ | ------------------------------------ |
| **I. Foundations**              | Philosophy, data, statistics                     | What can empirical research tell us? |
| **II. Description**             | Surveys, patterns, time series                   | What is happening?                   |
| **III. Causation**              | Experiments, IV, DiD, RD, and more               | What causes what?                    |
| **IV. Beyond Average Effects**  | Mechanisms, heterogeneity, ML, structural models | For whom and why?                    |
| **V. Integration and Practice** | Triangulation, synthesis, practice               | How do we build evidence?            |

## Formats

* **Read online:** [GitBook edition](https://laurence-wilse-samson.gitbook.io/textbooks/empirical-research-methods)
* Source and figure-generation code may be provided on request

## Running Examples

Five examples recur throughout, showing how different methods address different aspects of the same substantive questions:

1. **Returns to Education** — The canonical question in labor economics
2. **Minimum Wage and Employment** — Policy evaluation across methods
3. **Microfinance and Poverty Reduction** — Development economics and RCTs
4. **Monetary Policy Effects** — Time series and macroeconomic identification
5. **China's Post-1978 Growth** — Methodological pluralism for big questions

## How to Read

* **For a methods course**: Follow the parts sequentially, selecting chapters based on course focus
* **For causal inference**: Begin with Chapter 1, skim Chapter 3, then dive into Part III
* **For specific methods**: Use the table of contents and cross-reference index to find relevant chapters
* **For implementation**: Each part ends with a programming companion chapter

## Citation

> Wilse-Samson, Laurence. *Empirical Research Methods*. Online edition, 2026.

## License and Access

This is a draft manuscript. All rights reserved. Feedback and corrections are welcome at <laurence@lwilsesamson.com>.

***

[*Read the Preface*](/textbooks/empirical-research-methods/preface.md) *for more on the book's goals and approach.*

## About the author

I am an economist in New York. I am Associate Director at Oxford Economics, I teach as an adjunct at NYU Wagner and Columbia SIPA, and I am a Senior Research Fellow at the University of Johannesburg's Centre for Competition, Regulation and Economic Development. I took my PhD in economics at Columbia. More at [lwilsesamson.com](https://lwilsesamson.com/).

## How this book was made

I wrote this book with substantial help from AI language models, principally Claude (Anthropic), which I used for drafting, editing, converting technical notes, programming-companion code, and cross-references, and consistency checking. The outline, the argument, the selection of material and the judgments throughout are mine, as is the responsibility for what remains wrong. Figures were generated from code using the sources identified in their captions; illustrative figures are labelled.

In October 2026 I revised the whole text in two further passes, also with AI help: one to remove the stock phrasing that AI drafts produce, and one to bring the prose closer to my own academic writing.

In November 2026 I had the key checkable claims in each chapter (395 in all, across the 22 chapters that are not programming walkthroughs) compared with a primary source, using AI tools for the lookups and reviewing each change myself. Each claim was confirmed, corrected, softened or cut, except 94 for which no primary source could be reached; those were left as written and flagged. This was a check of selected claims, not a line-by-line proofread, so please check numbers and quotations against the primary sources before citing them.

Please report errors to <laurence@lwilsesamson.com>. Corrections usually go live the same day.

*Last updated: November 2026*