For Instructors Only:
An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields
Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors.
- Introductory Statistics for undergraduates and beginning graduate students (Masters, PhD) in the fields of data science, political science, public policy, sociology, statistics, economics, education, psychology, and health care policy
- Provides hands-on instruction using R programming, not paper-and-pencil statistics
- Includes more than forty data sets from actual research for students to test their skills on
- Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
- Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises
- Offers a solid foundation for further study
- Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides
What’s New on the Site
7 New Exercises
New R Package (available on Student and Instructor pages)
New Japanese Translation https://www.iwanami.co.jp/book/b352348.html
Errata Updated (10 September 2018)
Various bug fixes
1. POL 345 / SOC 305: Introduction to Quantitative Social Science
Margaret Frye (Sociology), Kosuke Imai (Politics)
Would universal health insurance improve the health of the poor? Do patterns of arrests in US cities show evidence of racial profiling? What accounts for who votes and their choice of candidates? This course will teach students how to address these and other social science questions by analyzing quantitative data. The course introduces basic principles of statistical inference and programming skills for data analysis. The goal is to provide students with the foundation necessary to analyze data in their own research and to become critical consumers of statistical claims made in the news media, in policy reports, and in academic research.
2. POL 245: Visualizing Data
James Lo, Will Lo (Instructors) Winston Chou, Elisha Cohen (Preceptors) Alex Tarr (QuantLab Coordinator) Kosuke Imai (Course Head)
Department of Politics, Princeton University
In this course, we consider ways to illustrate compelling stories hidden in a blizzard of data. Equal parts art, programming, and statistical reasoning, data visualization is a critical tool for anyone doing analysis. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, medicine, and social science. This course introduces students to the powerful R programming language and the basics of creating data-analytic graphics in R. From there, we use real datasets to explore topics ranging from network data (like social interactions on Facebook or trade between counties) to geographical data (like county-level election returns in the US or the spatial distribution of insurgent attacks in Afghanistan). No prior background in statistics or programming is required or expected.
3. Statistical Programming Camp
Munji Choi, Asya Magazinnik, (Instructors) Kosuke Imai (Faculty Advisor)
Department of Politics, Princeton University
This camp will prepare students for POL 572 and other quantitative analysis courses offered in the Politics department and elsewhere. Although participation in this camp is completely voluntary, the materials covered in this camp are a pre-requisite for POL 572. Students will learn the basics of statistical programming using R, an open-source computing environment. Using data from published journal articles, students will learn how to manipulate data, create graphs and tables, and conduct basic statistical analysis. This camp assumes knowledge of probability and statistics as covered in POL 571.
Kosuke Imai (pronounced Ksk) is a professor in the Department of Politics and founding director of the Center for Statistics and Machine Learning at Princeton University. He is also an executive committee member of the Committee for Statistical Studies and the Program for Quantitative and Analytical Political Science (Q-APS). Imai is 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 Princeton, 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 http://imai.princeton.edu/ for more information about Kosuke Imai.
“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