Social Science


Quantitative Social Science

Princeton University Press is pleased to present you with two introductory textbooks on data analysis and statistics, available in both R and Stata editions, written primarily for students in the social sciences and allied fields. Each book comes with unique supporting documentation regularly updated on this website to help students and instructors navigate through an introductory course on quantitative social science.

QSS An Introduction Cover

Quantitative Social Science:
An Introduction

Paperback Edition
Published: Feb. 2018
432 pp. | 7 x 10 | 14 color illus.
9 halftones. 77 line illus.
ISBN: 9780691175461
$49.50 | £42.00
eBook | eISBN: 9781400885251

QSS: An Introduction in Stata book cover

Quantitative Social Science:
An Introduction in Stata

Paperback Edition
Published: March 2021
472 pp. | 7 x 10 | 79 color illus.
11 b/w illus. 49 tables
ISBN: 9780691191096
$49.50 | £42.00
eBook | eISBN: 9780691191294


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For Instructors Only:

Kosuke ImaiKosuke Imai

Kosuke Imai (pronounced K$ bar{text{o}}^primecdot$sk$ bar{text{a}}$) is a professor in the Department of Government and in the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science. Previously he was a professor at Princeton University for 15 years, and an executive committee member of the Committee for Statistical Studies and the Program for Quantitative and Analytical Political Science (Q-APS) at Princeton University. In addition, Imai was the founding director of the undergraduate certificate program in Statistics and Machine Learning. He specializes in the development of statistical methods and their applications to social science research. Outside of Harvard, Imai is currently serving as the Vice President and President-elect of the Society for Political Methodology. He is also Professor of Visiting Status in the Graduate Schools of Law and Politics at The University of Tokyo. Visit https://imai.fas.harvard.edu/ for more information about Kosuke Imai.


Lori D. BougherLori D. Bougher

Lori D. Bougher, co-author of Quantitative Social Science: An Introduction in Stata, is a senior research specialist at the Data-Driven Social Science Initiative at Princeton University. She was previously the data and statistical analyst at the Niehaus Center for Globalization and Governance at Princeton, where she also held the positions of associate research scholar and postdoctoral research associate with the Center for the Study of Democratic Politics. Bougher holds a PhD in social and developmental psychology from the University of Cambridge and a dual Master’s of Public Administration from the London School of Economics and Columbia University, specializing in applied economics and advance policy analysis.

“Kosuke Imai has produced a superb hands-on introduction to modern quantitative methods in the social sciences. Placing practical data analysis front and center, this book is bound to become a standard reference in the field of quantitative social science and an indispensable resource for students and practitioners alike.”–Alberto Abadie, Massachusetts Institute of Technology
“Kosuke Imai’s book takes a very novel and interesting approach to a first quantitative methods course for the social sciences. Focusing on interesting questions from the beginning, he starts by introducing the potential outcome approach to causality, and proceeds to present the reader with a wide range of methods for an admirably broad range of settings, including textual, network, and spatial data. Integrated with the methodological discussions are examples with detailed R code. Readers who work through this book will be well equipped to use modern methods for data analysis in the social sciences. I highly recommend this book!”–Guido W. Imbens, coauthor of Causal Inference for Statistics, Social, and Biomedical Sciences
“The search for a good undergraduate social science textbook is eternal, but with Imai’s book, the search may well be over. It covers a host of cutting-edge issues in quantitative analysis, from causality and inference to its use of R so that students can advance in both their research and work lives. Imai plots a new way for us to think about how to teach undergraduate methods.”–Nathaniel Beck, New York University

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