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Quantitative Social Science: An Introduction

Kosuke Imai
Publisher: 
Princeton University Press
Publication Date: 
2017
Number of Pages: 
408
Format: 
Paperback
Price: 
49.50
ISBN: 
9780691175461
Category: 
Textbook
BLL Rating: 

The Basic Library List Committee suggests that undergraduate mathematics libraries consider this book for acquisition.

[Reviewed by
Jason M. Graham
, on
08/9/2017
]

Today, data science involves an interesting mixture of area-specific content knowledge with quantitative methods and computing. Furthermore, one may find data scientists occupying positions in a variety of academic departments, from computer science to marketing, and also in a variety of different industries. Of course, being well established collectors of data, social scientists ranging from the political to the psychological often require as much use of modern data science tools as any other group of researchers. Yet social scientists do not typically obtain the same level of training in computing, mathematics and statistics as their peers in the natural and computing sciences. Hence the existence of this book.

In Quantitative Social Science the reader will learn about essential techniques from probability and statistics, predictive modeling, network analysis, data wrangling and visualization, and a few other related topics. The book is written with the needs of social scientists in mind, but this does not mean that the reader must come from a social science background to benefit from reading it. In fact, I believe that this book provides an excellent introduction to data science for almost anyone. This is because the author has masterfully balanced careful explanations of the quantitative theory with the practical computer implementation of the methods applied to real world data sets that many readers will find interesting and appealing.

In order to implement methods and provide the reader with hands on experience in analyzing real data, the author has adopted the R statistical computing platform. In Quantitative Social Science the reader will learn the basics of R and see the relevant R code written out in full. Additionally, there is a website where the reader can access and download data sets and R code.

When it comes to the use of R in Quantitative Social Science An Introduction, there is more than meets the eye. This is because Imai has used the R package swirl to create interactive tutorials in R that the reader can use to test both their understanding of the material covered and their R coding skills. In fact, associated with each chapter are swirl tutorials that carefully reinforce the material of the corresponding chapter. This is in addition to end-of-chapter exercises included in the book.

As I was working through the book, I discovered a problem with a couple of the corresponding data sets from the web site. Specifically, for some reason I could not download the twitter-senator data set, and there was a data set for which the column names did not correspond to those used in the book. These are minor problems, but they should be corrected.

The fact that Quantitative Social Science An Introduction is carefully written, detailed, and interactive makes it useful either as a textbook for a lecture course or for self-study. It truly is an excellent book and a pleasure to read and work through. I highly recommend the book to anyone looking for an introduction to data science.


Jason M. Graham is an assistant professor in the department of mathematics at the University of Scranton, Scranton, Pennsylvania. His current professional interests are in teaching applied mathematics and mathematical biology, and collaborating with biologists specializing in the collective behavior of groups of organisms.

List of Tables xiii
List of Figures xv
Preface xvii
1 Introduction 1
1.1 Overview of the Book 3
1.2 How to Use this Book 7
1.3 Introduction to R 10
1.3.1 Arithmetic Operations 10
1.3.2 Objects 12
1.3.3 Vectors 14
1.3.4 Functions 16
1.3.5 Data Files 20
1.3.6 Saving Objects 23
1.3.7 Packages 24
1.3.8 Programming and Learning Tips 25
1.4 Summary 27
1.5 Exercises 28
1.5.1 Bias in Self-Reported Turnout 28
1.5.2 Understanding World Population Dynamics 29
2 Causality 32
2.1 Racial Discrimination in the Labor Market 32
2.2 Subsetting the Data in R 36
2.2.1 Logical Values and Operators 37
2.2.2 Relational Operators 39
2.2.3 Subsetting 40
2.2.4 Simple Conditional Statements 43
2.2.5 Factor Variables 44
2.3 Causal Effects and the Counterfactual 46
2.4 Randomized Controlled Trials 48
2.4.1 The Role of Randomization 49
2.4.2 Social Pressure and Voter Turnout 51
2.5 Observational Studies 54
2.5.1 Minimum Wage and Unemployment 54
2.5.2 Confounding Bias 57
2.5.3 Before-and-After and Difference-in-Differences Designs 60
2.6 Descriptive Statistics for a Single Variable 63
2.6.1 Quantiles 63
2.6.2 Standard Deviation 66
2.7 Summary 68
2.8 Exercises 69
2.8.1 Efficacy of Small Class Size in Early Education 69
2.8.2 Changing Minds on Gay Marriage 71
2.8.3 Success of Leader Assassination as a Natural Experiment 73
3 Measurement 75
3.1 Measuring Civilian Victimization during Wartime 75
3.2 Handling Missing Data in R 78
3.3 Visualizing the Univariate Distribution 80
3.3.1 Bar Plot 80
3.3.2 Histogram 81
3.3.3 Box Plot 85
3.3.4 Printing and Saving Graphs 87
3.4 Survey Sampling 88
3.4.1 The Role of Randomization 89
3.4.2 Nonresponse and Other Sources of Bias 93
3.5 Measuring Political Polarization 96
3.6 Summarizing Bivariate Relationships 97
3.6.1 Scatter Plot 98
3.6.2 Correlation 101
3.6.3 Quantile-Quantile Plot 105
3.7 Clustering 108
3.7.1 Matrix in R 108
3.7.2 List in R 110
3.7.3 The k-Means Algorithm 111
3.8 Summary 115
3.9 Exercises 116
3.9.1 Changing Minds on Gay Marriage: Revisited 116
3.9.2 Political Efficacy in China and Mexico 118
3.9.3 Voting in the United Nations General Assembly 120
4 Prediction 123
4.1 Predicting Election Outcomes 123
4.1.1 Loops in R 124
4.1.2 General Conditional Statements in R 127
4.1.3 Poll Predictions 130
4.2 Linear Regression 139
4.2.1 Facial Appearance and Election Outcomes 139
4.2.2 Correlation and Scatter Plots 141
4.2.3 Least Squares 143
4.2.4 Regression towards the Mean 148
4.2.5 Merging Data Sets in R 149
4.2.6 Model Fit 156
4.3 Regression and Causation 161
4.3.1 Randomized Experiments 162
4.3.2 Regression with Multiple Predictors 165
4.3.3 Heterogenous Treatment Effects 170
4.3.4 Regression Discontinuity Design 176
4.4 Summary 181
4.5 Exercises 182
4.5.1 Prediction Based on Betting Markets 182
4.5.2 Election and Conditional Cash Transfer Program in Mexico 184
4.5.3 Government Transfer and Poverty Reduction in Brazil 187
5 Discovery 189
5.1 Textual Data 189
5.1.1 The Disputed Authorship of The Federalist Papers 189
5.1.2 Document-Term Matrix 194
5.1.3 Topic Discovery 195
5.1.4 Authorship Prediction 200
5.1.5 Cross Validation 202
5.2 Network Data 205
5.2.1 Marriage Network in Renaissance Florence 205
5.2.2 Undirected Graph and Centrality Measures 207
5.2.3 Twitter-Following Network 211
5.2.4 Directed Graph and Centrality 213
5.3 Spatial Data 220
5.3.1 The 1854 Cholera Outbreak in London 220
5.3.2 Spatial Data in R 223
5.3.3 Colors in R 226
5.3.4 US Presidential Elections 228
5.3.5 Expansion of Walmart 231
5.3.6 Animation in R 233
5.4 Summary 235
5.5 Exercises 236
5.5.1 Analyzing the Preambles of Constitutions 236
5.5.2 International Trade Network 238
5.5.3 Mapping US Presidential Election Results over Time 239
6 Probability 242
6.1 Probability 242
6.1.1 Frequentist versus Bayesian 242
6.1.2 Definition and Axioms 244
6.1.3 Permutations 247
6.1.4 Sampling with and without Replacement 250
6.1.5 Combinations 252
6.2 Conditional Probability 254
6.2.1 Conditional, Marginal, and Joint Probabilities 254
6.2.2 Independence 261
6.2.3 Bayes' Rule 266
6.2.4 Predicting Race Using Surname and Residence Location 268
6.3 Random Variables and Probability Distributions 277
6.3.1 Random Variables 278
6.3.2 Bernoulli and Uniform Distributions 278
6.3.3 Binomial Distribution 282
6.3.4 Normal Distribution 286
6.3.5 Expectation and Variance 292
6.3.6 Predicting Election Outcomes with Uncertainty 296
6.4 Large Sample Theorems 300
6.4.1 The Law of Large Numbers 300
6.4.2 The Central Limit Theorem 302
6.5 Summary 306
6.6 Exercises 307
6.6.1 The Mathematics of Enigma 307
6.6.2 A Probability Model for Betting Market Election Prediction 309
6.6.3 Election Fraud in Russia 310
7 Uncertainty 314
7.1 Estimation 314
7.1.1 Unbiasedness and Consistency 315
7.1.2 Standard Error 322
7.1.3 Confidence Intervals 326
7.1.4 Margin of Error and Sample Size Calculation in Polls 332
7.1.5 Analysis of Randomized Controlled Trials 336
7.1.6 Analysis Based on Student's t-Distribution 339
7.2 Hypothesis Testing 342
7.2.1 Tea-Tasting Experiment 342
7.2.2 The General Framework 346
7.2.3 One-Sample Tests 350
7.2.4 Two-Sample Tests 356
7.2.5 Pitfalls of Hypothesis Testing 361
7.2.6 Power Analysis 363
7.3 Linear Regression Model with Uncertainty 370
7.3.1 Linear Regression as a Generative Model 370
7.3.2 Unbiasedness of Estimated Coefficients 375
7.3.3 Standard Errors of Estimated Coefficients 378
7.3.4 Inference about Coefficients 380
7.3.5 Inference about Predictions 384
7.4 Summary 389
7.5 Exercises 390
7.5.1 Sex Ratio and the Price of Agricultural Crops in China 390
7.5.2 File Drawer and Publication Bias in Academic Research 392
7.5.3 The 1932 German Election in the Weimar Republic 394
8 Next 397
General Index 401
R Index 406

Dummy View - NOT TO BE DELETED