• Quantitative Social Science
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Quantitative Social Science

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The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fieldsQuantitative analysis is an increasingly essential skill for social science...
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  • Format:
  • Publication Date: 16 March 2021
  • ISBN: 9780691191096
  • Pages: 472
  • Imprint: Princeton University Press
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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
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Price: $68.00
Pages: 472
Publisher: Princeton University Press
Imprint: Princeton University Press
Publication Date: 16 March 2021
ISBN: 9780691191096
Format: Paperback
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.
  • List of Tables
  • List of Figures
  • Preface
  • Preface to the Original Book
  • 1 INTRODUCTION
    • 1.1 Overview of the Book
    • 1.2 How to Use this Book
    • 1.3 Introduction to Stata
      • 1.3.1 Arithmetic Operations
      • 1.3.2 Variables
      • 1.3.3 Labels
      • 1.3.4 Describing the Data
      • 1.3.5 Data Files
      • 1.3.6 Merging Data Sets in Stata
      • 1.3.7 Packages
      • 1.3.8 Programming and Learning Tips
    • 1.4 Summary
    • 1.5 Exercises
      • 1.5.1 Bias in Self-Reported Turnout
      • 1.5.2 Understanding World Population Dynamics
  • 2 CAUSALITY
    • 2.1 Racial Discrimination in the Labor Market
    • 2.2 Subsetting the Data in Stata
      • 2.2.1 Relational Operators
      • 2.2.2 Logical Operators
      • 2.2.3 Simple Conditional Statements and Variable Creation
      • 2.2.4 Subsetting Using Conditions
      • 2.2.5 Preserving and Transforming Data Sets
    • 2.3 Causal Effects and the Counterfactual
    • 2.4 Randomized Controlled Trials
      • 2.4.1 The Role of Randomization
      • 2.4.2 Social Pressure and Voter Turnout
    • 2.5 Observational Studies
      • 2.5.1 Minimum Wage and Unemployment
      • 2.5.2 Confounding Bias
      • 2.5.3 Before-and-After and Difference-in-Differences Designs
    • 2.6 Descriptive Statistics for a Single Variable
      • 2.6.1 Quantiles
      • 2.6.2 Standard Deviation
    • 2.7 Summary
    • 2.8 Exercises
      • 2.8.1 Efficacy of Small Class Size in Early Education
      • 2.8.2 Changing Minds on Gay Marriage
      • 2.8.3 Success of Leader Assassination as a Natural Experiment
  • 3 MEASUREMENT
    • 3.1 Measuring Civilian Victimization during Wartime
    • 3.2 Handling Missing Data in Stata
      • 3.2.1 Missings Package
    • 3.3 Visualizing the Univariate Distribution
      • 3.3.1 Bar Plot
      • 3.3.2 Histogram
      • 3.3.3 Box Plot
      • 3.3.4 Printing and Saving Graphs
    • 3.4 Survey Sampling
      • 3.4.1 The Role of Randomization
      • 3.4.2 Nonresponse and Other Sources of Bias
    • 3.5 Measuring Political Polarization
    • 3.6 Summarizing Bivariate Relationships
      • 3.6.1 Scatterplot
      • 3.6.2 Correlation
      • 3.6.3 Quantile–Quantile Plot
    • 3.7 Clustering
      • 3.7.1 The k-Means Algorithm
    • 3.8 Summary
    • 3.9 Exercises
      • 3.9.1 Changing Minds on Gay Marriage: Revisited
      • 3.9.2 Political Efficacy in China and Mexico
      • 3.9.3 Voting in the United Nations General Assembly
  • 4 PREDICTION
    • 4.1 Predicting Election Outcomes
      • 4.1.1 Macros
      • 4.1.2 Loops
      • 4.1.3 Poll Predictions
    • 4.2 Linear Regression
      • 4.2.1 Facial Appearance and Election Outcomes
      • 4.2.2 Correlation and Scatterplots
      • 4.2.3 Least Squares
      • 4.2.4 Regression toward the Mean
      • 4.2.5 Model Fit
    • 4.3 Regression and Causation
      • 4.3.1 Randomized Experiments
      • 4.3.2 Regression with Multiple Predictors
      • 4.3.3 Heterogeneous Treatment Effects
      • 4.3.4 Regression Discontinuity Design
    • 4.4 Summary
    • 4.5 Exercises
      • 4.5.1 Prediction Based on Betting Markets
      • 4.5.2 Election and Conditional Cash Transfer Program in Mexico
      • 4.5.3 Government Transfer and Poverty Reduction in Brazil
  • 5 PROBABILITY
    • 5.1 Probability
      • 5.1.1 Frequentist versus Bayesian
      • 5.1.2 Definition and Axioms
      • 5.1.3 Permutations
      • 5.1.4 Sampling with and without Replacement
      • 5.1.5 Combinations
    • 5.2 Conditional Probability
      • 5.2.1 Conditional, Marginal, and Joint Probabilities
      • 5.2.2 Independence
      • 5.2.3 Bayes’ Rule
      • 5.2.4 Predicting Race Using Surname and Residence Location
    • 5.3 Random Variables and Probability Distributions
      • 5.3.1 Random Variables
      • 5.3.2 Bernoulli and Uniform Distributions
      • 5.3.3 Binomial Distribution
      • 5.3.4 Normal Distribution
      • 5.3.5 Expectation and Variance
      • 5.3.6 Predicting Election Outcomes with Uncertainty
    • 5.4 Large Sample Theorems
      • 5.4.1 The Law of Large Numbers
      • 5.4.2 The Central Limit Theorem
    • 5.5 Summary
    • 5.6 Exercises
      • 5.6.1 The Mathematics of Enigma
      • 5.6.2 A Probability Model for Betting Market Election Prediction
  • 6 UNCERTAINTY
    • 6.1 Estimation
      • 6.1.1 Unbiasedness and Consistency
      • 6.1.2 Standard Error
      • 6.1.3 Confidence Intervals
      • 6.1.4 Margin of Error and Sample Size Calculation in Polls
      • 6.1.5 Analysis of Randomized Controlled Trials
      • 6.1.6 Analysis Based on Student’s t-Distribution
    • 6.2 Hypothesis Testing
      • 6.2.1 Tea-Tasting Experiment
      • 6.2.2 The General Framework
      • 6.2.3 One-Sample Tests
      • 6.2.4 Two-Sample Tests
      • 6.2.5 Pitfalls of Hypothesis Testing
      • 6.2.6 Power Analysis
    • 6.3 Linear Regression Model with Uncertainty
      • 6.3.1 Linear Regression as a Generative Model
      • 6.3.2 Unbiasedness of Estimated Coefficients
      • 6.3.3 Standard Errors of Estimated Coefficients
      • 6.3.4 Inference about Coefficients
      • 6.3.5 Inference about Predictions
    • 6.4 Summary
    • 6.5 Exercises
      • 6.5.1 Sex Ratio and the Price of Agricultural Crops in China
      • 6.5.2 Filedrawer and Publication Bias in Academic Research
      • 6.5.3 The 1932 German Election in the Weimar Republic
  • 7 DISCOVERY
    • 7.1 Network Data
      • 7.1.1 Marriage Network in Renaissance Florence
      • 7.1.2 Undirected Graph and Centrality Measures
      • 7.1.3 Twitter Following Network
      • 7.1.4 Directed Graph and Centrality
    • 7.2 Spatial Data
      • 7.2.1 The 1854 Cholera Outbreak in London
      • 7.2.2 Spatial Data in Stata
      • 7.2.3 United States Presidential Elections
      • 7.2.4 Expansion of Walmart
      • 7.2.5 Animation in Stata
    • 7.3 Textual Data
      • 7.3.1 The Disputed Authorship of The Federalist Papers
      • 7.3.2 Topic Discovery
      • 7.3.3 Document–Term Matrix and Clusters
      • 7.3.4 Authorship Prediction
      • 7.3.5 Cross Validation
    • 7.4 Summary
    • 7.5 Exercises
      • 7.5.1 International Trade Network
      • 7.5.2 Mapping US Presidential Election Results over Time
      • 7.5.3 Analyzing the Preambles of Constitutions
  • 8 NEXT
  • General Index
  • Stata Index
  • Stata Command Abbreviation List