Instructor Resources

Quantitative Social Science Quantitative Social Science: An Introduction
Kosuke Imai

If you have assigned this textbook in your course, you may gain access to the instructor resources described on this page by clicking the link below. All requests for instructor access are verified by Princeton University Press. Once you are granted access, you will be able to download all materials listed below, including data sets and both R and tidyverse scripts.

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Instructor Data Files - R and tidyverse

The downloads below contain the data sets and tidyverse scripts with solutions, needed to complete exercises for all of the chapters in Quantitative Social Science: An Introduction (and Quantitative Social Science: An Introduction in tidyverse.) Each zip file contains a “Read Me” that provides detail about the other files included.

All Instructor Files [ZIP]

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Chapter 1 Introduction [ZIP] (with solutions)

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Chapter 2 Causality [ZIP] (with solutions)

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Chapter 3 Measurement [ZIP] (with solutions)

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Chapter 4 Prediction [ZIP] (with solutions)

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Chapter 5 Discovery [ZIP] (with solutions)

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Chapter 6 Probability [ZIP] (with solutions)

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Chapter 7 Uncertainty [ZIP] (with solutions)

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Illustration Package [ZIP]

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Instructor Lecture slides [ZIP]

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Student Data Files - R and tidyverse

Click link to access the student data sets and tidyverse scripts needed to complete exercises for all of the chapters in Quantitative Social Science: An Introduction. No solutions are included at this link.

Errata [ZIP]

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Inside the Book


1. POL 345 / SOC 305: Introduction to Quantitative Social Science

Fall 2016
Margaret Frye (Sociology), Kosuke Imai (Politics)
Princeton University

Introduction to Quantitative Social Science Syllabus [PDF] 

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

Summer 2015
James Lo, Will Lo (Instructors) Winston Chou, Elisha Cohen (Preceptors) Alex Tarr (QuantLab Coordinator) Kosuke Imai (Course Head)
Department of Politics, Princeton University

Visualizing Data Syllabus [PDF] 

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

Spring 2017
Munji Choi, Asya Magazinnik, (Instructors) Kosuke Imai (Faculty Advisor)
Department of Politics, Princeton University

Visualizing Data Syllabus [PDF] 
Visualizing Data Lecture Video

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.