Quantitative Social Science: An Introduction in Stata

by Kosuke Imai, Lori D. Bougher, Raymond P. Hicks (Contribution by)

Overview

The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fields

Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as business, economics, education, political science, psychology, sociology, public policy, and data science.

Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the Stata statistical software and interpret the results—it emphasizes hands-on learning, not paper-and-pencil statistics. More than fifty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.

Proven in classrooms around the world, this one-of-a-kind textbook features numerous additional data analysis exercises, and also comes with supplementary teaching materials for instructors.

• Written especially for students in the social sciences and allied fields, including business, economics, education, psychology, political science, sociology, public policy, and data science
• Provides hands-on instruction using Stata, not paper-and-pencil statistics
• Includes more than fifty 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

Product Details

ISBN-13:	9780691270852
Publisher:	Princeton University Press
Publication date:	09/10/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	472
File size:	23 MB
Note:	This product may take a few minutes to download.

About the Author

Kosuke Imai is Professor of Government and of Statistics at Harvard University. Lori D. Bougher is a senior research specialist at the Data-Driven Social Science Initiative at Princeton University.

Table of Contents

List of Tables xiii

List of Figures xv

Preface xvii

Preface to the Original Book xix

1 Introduction 1

1.1 Overview of the Book 3

1.2 How to Use this Book 7

1.3 Introduction to Stata 8

1.3.1 Arithmetic Operations 9

1.3.2 Variables 10

1.3.3 Labels 16

1.3.4 Describing the Data 18

1.3.5 Data Files 20

1.3.6 Merging Data Sets in Stata 21

1.3.7 Packages 23

1.3.8 Programming and Learning Tips 25

1.4 Summary 26

1.5 Exercises 27

1.5.1 Bias in Self-Reported Turnout 27

1.5.2 Understanding World Population Dynamics 28

2 Causality 32

2.1 Racial Discrimination in the Labor Market 32

2.2 Subsetting the Data in Stata 38

2.2.1 Relational Operators 38

2.2.2 Logical Operators 39

2.2.3 Simple Conditional Statements and Variable Creation 40

2.2.4 Subsetting Using Conditions 43

2.2.5 Preserving and Transforming Data Sets 44

2.3 Causal Effects and the Counterfactual 47

2.4 Randomized Controlled Trials 49

2.4.1 The Role of Randomization 50

2.4.2 Social Pressure and Voter Turnout 51

2.5 Observational Studies 56

2.5.1 Minimum Wage and Unemployment 56

2.5.2 Confounding Bias 60

2.5.3 Before-and-After and Difference-in-Differences Designs 63

2.6 Descriptive Statistics for a Single Variable 67

2.6.1 Quantiles 67

2.6.2 Standard Deviation 72

2.7 Summary 75

2.8 Exercises 75

2.8.1 Efficacy of Small Class Size in Early Education 75

2.8.2 Changing Minds on Gay Marriage 77

2.8.3 Success of Leader Assassination as a Natural Experiment 79

3 Measurement 81

3.1 Measuring Civilian Victimization during Wartime 81

3.2 Handling Missing Data in Stata 84

3.2.1 Missings Package 85

3.3 Visualizing the Univariate Distribution 86

3.3.1 Bar Plot 87

3.3.2 Histogram 89

3.3.3 Box Plot 92

3.3.4 Printing and Saving Graphs 94

3.4 Survey Sampling 96

3.4.1 The Role of Randomization 96

3.4.2 Nonresponse and Other Sources of Bias 101

3.5 Measuring Political Polarization 105

3.6 Summarizing Bivariate Relationships 106

3.6.1 Scatterplot 106

3.6.2 Correlation 109

3.6.3 Quantile-Quantile Plot 114

3.7 Clustering 117

3.7.1 The k-Means Algorithm 117

3.8 Summary 121

3.9 Exercises 122

3.9.1 Changing Minds on Gay Marriage: Revisited 122

3.9.2 Political Efficacy in China and Mexico 123

3.9.3 Voting in the United Nations General Assembly 125

4 Prediction 128

4.1 Predicting Election Outcomes 128

4.1.1 Macros 129

4.1.2 Loops 131

4.1.3 Poll Predictions 133

4.2 Linear Regression 144

4.2.1 Facial Appearance and Election Outcomes 144

4.2.2 Correlation and Scatterplots 146

4.2.3 Least Squares 148

4.2.4 Regression toward the Mean 154

4.2.5 Model Fit 160

4.3 Regression and Causation 167

4.3.1 Randomized Experiments 167

4.3.2 Regression with Multiple Predictors 171

4.3.3 Heterogeneous Treatment Effects 177

4.3.4 Regression Discontinuity Design 184

4.4 Summary 190

4.5 Exercises 190

4.5.1 Prediction Based on Betting Markets 190

4.5.2 Election and Conditional Cash Transfer Program in Mexico 193

4.5.3 Government Transfer and Poverty Reduction in Brazil 195

5 Probability 197

5.1 Probability 197

5.1.1 Frequentist versus Bayesian 197

5.1.2 Definition and Axioms 199

5.1.3 Permutations 202

5.1.4 Sampling with and without Replacement 205

5.1.5 Combinations 208

5.2 Conditional Probability 210

5.2.1 Conditional, Marginal, and Joint Probabilities 210

5.2.2 Independence 218

5.2.3 Bayes' Rule 226

5.2.4 Predicting Race Using Surname and Residence Location 228

5.3 Random Variables and Probability Distributions 241

5.3.1 Random Variables 241

5.3.2 Bernoulli and Uniform Distributions 241

5.3.3 Binomial Distribution 246

5.3.4 Normal Distribution 249

5.3.5 Expectation and Variance 256

5.3.6 Predicting Election Outcomes with Uncertainty 260

5.4 Large Sample Theorems 264

5.4.1 The Law of Large Numbers 264

5.4.2 The Central Limit Theorem 266

5.5 Summary 271

5.6 Exercises 272

5.6.1 The Mathematics of Enigma 272

5.6.2 A Probability Model for Betting Market Election Prediction 274

6 Uncertainty 276

6.1 Estimation 276

6.1.1 Unbiasedness and Consistency 277

6.1.2 Standard Error 285

6.1.3 Confidence Intervals 290

6.1.4 Margin of Error and Sample Size Calculation in Polls 296

6.1.5 Analysis of Randomized Controlled Trials 301

6.1.6 Analysis Based on Student's t-Distribution 304

6.2 Hypothesis Testing 307

6.2.1 Tea-Tasting Experiment 307

6.2.2 The General Framework 313

6.2.3 One-Sample Tests 316

6.2.4 Two-Sample Tests 322

6.2.5 Pitfalls of Hypothesis Testing 327

6.2.6 Power Analysis 329

6.3 Linear Regression Model with Uncertainty 336

6.3.1 Linear Regression as a Generative Model 337

6.3.2 Unbiasedness of Estimated Coefficients 343

6.3.3 Standard Errors of Estimated Coefficients 345

6.3.4 Inference about Coefficients 348

6.3.5 Inference about Predictions 350

6.4 Summary 356

6.5 Exercises 357

6.5.1 Sex Ratio and the Price of Agricultural Crops in China 357

6.5.2 Filedrawer and Publication Bias in Academic Research 359

6.5.3 The 1932 German Election in the Weimar Republic 361

7 Discovery 364

7.1 Network Data 364

7.1.1 Marriage Network in Renaissance Florence 365

7.1.2 Undirected Graph and Centrality Measures 367

7.1.3 Twitter Following Network 375

7.1.4 Directed Graph and Centrality 376

7.2 Spatial Data 386

7.2.1 The 1854 Cholera Outbreak in London 386

7.2.2 Spatial Data in State 389

7.2.3 United States Presidential Elections 393

7.2.4 Expansion of Walmart 395

7.2.5 Animation in Stata 397

7.3 Textual Data 400

7.3.1 The Disputed Authorship of The Federalist Papers 400

7.3.2 Topic Discovery 404

7.3.3 Document-Term Matrix and Clusters 411

7.3.4 Authorship Prediction 413

7.3.5 Cross Validation 417

7.4 Summary 420

7.5 Exercises

7.5.1 International Trade Network 420

7.5.2 Mapping US Presidential Election Results over Time 422

75.3 Analyzing the Preambles of Constitutions 424

8 Next 429

General Index 433

Stata Index 439

Stata Command Abbreviation List 443