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 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 scienceProvides hands-on instruction using Stata, not paper-and-pencil statisticsIncludes more than fifty data sets from actual research for students to test their skills onCovers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical toolsFeatures a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercisesOffers a solid foundation for further studyComes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

Produktinformation

Utgivningsdatum:2021-03-16
Mått:178 x 254 x 35 mm
Vikt:1 157 g
Format:Inbunden
Språk:Engelska
Antal sidor:472
Förlag:Princeton University Press
ISBN:9780691191089

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Mer om författaren

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.

Innehållsförteckning

List of TablesList of FiguresPrefacePreface to the Original Book1 INTRODUCTION1.1 Overview of the Book1.2 How to Use this Book1.3 Introduction to Stata1.3.1 Arithmetic Operations1.3.2 Variables1.3.3 Labels1.3.4 Describing the Data1.3.5 Data Files1.3.6 Merging Data Sets in Stata1.3.7 Packages1.3.8 Programming and Learning Tips1.4 Summary1.5 Exercises1.5.1 Bias in Self-Reported Turnout1.5.2 Understanding World Population Dynamics2 CAUSALITY2.1 Racial Discrimination in the Labor Market2.2 Subsetting the Data in Stata2.2.1 Relational Operators2.2.2 Logical Operators2.2.3 Simple Conditional Statements and Variable Creation2.2.4 Subsetting Using Conditions2.2.5 Preserving and Transforming Data Sets2.3 Causal Effects and the Counterfactual2.4 Randomized Controlled Trials2.4.1 The Role of Randomization2.4.2 Social Pressure and Voter Turnout2.5 Observational Studies2.5.1 Minimum Wage and Unemployment2.5.2 Confounding Bias2.5.3 Before-and-After and Difference-in-Differences Designs2.6 Descriptive Statistics for a Single Variable2.6.1 Quantiles2.6.2 Standard Deviation2.7 Summary2.8 Exercises2.8.1 Efficacy of Small Class Size in Early Education2.8.2 Changing Minds on Gay Marriage2.8.3 Success of Leader Assassination as a Natural Experiment3 MEASUREMENT3.1 Measuring Civilian Victimization during Wartime3.2 Handling Missing Data in Stata3.2.1 Missings Package3.3 Visualizing the Univariate Distribution3.3.1 Bar Plot3.3.2 Histogram3.3.3 Box Plot3.3.4 Printing and Saving Graphs3.4 Survey Sampling3.4.1 The Role of Randomization3.4.2 Nonresponse and Other Sources of Bias3.5 Measuring Political Polarization3.6 Summarizing Bivariate Relationships3.6.1 Scatterplot3.6.2 Correlation3.6.3 Quantile–Quantile Plot3.7 Clustering3.7.1 The k-Means Algorithm3.8 Summary3.9 Exercises3.9.1 Changing Minds on Gay Marriage: Revisited3.9.2 Political Efficacy in China and Mexico3.9.3 Voting in the United Nations General Assembly4 PREDICTION4.1 Predicting Election Outcomes4.1.1 Macros4.1.2 Loops4.1.3 Poll Predictions4.2 Linear Regression4.2.1 Facial Appearance and Election Outcomes4.2.2 Correlation and Scatterplots4.2.3 Least Squares4.2.4 Regression toward the Mean4.2.5 Model Fit4.3 Regression and Causation4.3.1 Randomized Experiments4.3.2 Regression with Multiple Predictors4.3.3 Heterogeneous Treatment Effects4.3.4 Regression Discontinuity Design4.4 Summary4.5 Exercises4.5.1 Prediction Based on Betting Markets4.5.2 Election and Conditional Cash Transfer Program in Mexico4.5.3 Government Transfer and Poverty Reduction in Brazil5 PROBABILITY5.1 Probability5.1.1 Frequentist versus Bayesian5.1.2 Definition and Axioms5.1.3 Permutations5.1.4 Sampling with and without Replacement5.1.5 Combinations5.2 Conditional Probability5.2.1 Conditional, Marginal, and Joint Probabilities5.2.2 Independence5.2.3 Bayes’ Rule5.2.4 Predicting Race Using Surname and Residence Location5.3 Random Variables and Probability Distributions5.3.1 Random Variables5.3.2 Bernoulli and Uniform Distributions5.3.3 Binomial Distribution5.3.4 Normal Distribution5.3.5 Expectation and Variance5.3.6 Predicting Election Outcomes with Uncertainty5.4 Large Sample Theorems5.4.1 The Law of Large Numbers5.4.2 The Central Limit Theorem5.5 Summary5.6 Exercises5.6.1 The Mathematics of Enigma5.6.2 A Probability Model for Betting Market Election Prediction6 UNCERTAINTY6.1 Estimation6.1.1 Unbiasedness and Consistency6.1.2 Standard Error6.1.3 Confidence Intervals6.1.4 Margin of Error and Sample Size Calculation in Polls6.1.5 Analysis of Randomized Controlled Trials6.1.6 Analysis Based on Student’s t-Distribution6.2 Hypothesis Testing6.2.1 Tea-Tasting Experiment6.2.2 The General Framework6.2.3 One-Sample Tests6.2.4 Two-Sample Tests6.2.5 Pitfalls of Hypothesis Testing6.2.6 Power Analysis6.3 Linear Regression Model with Uncertainty6.3.1 Linear Regression as a Generative Model6.3.2 Unbiasedness of Estimated Coefficients6.3.3 Standard Errors of Estimated Coefficients6.3.4 Inference about Coefficients6.3.5 Inference about Predictions6.4 Summary6.5 Exercises6.5.1 Sex Ratio and the Price of Agricultural Crops in China6.5.2 Filedrawer and Publication Bias in Academic Research6.5.3 The 1932 German Election in the Weimar Republic7 DISCOVERY7.1 Network Data7.1.1 Marriage Network in Renaissance Florence7.1.2 Undirected Graph and Centrality Measures7.1.3 Twitter Following Network7.1.4 Directed Graph and Centrality7.2 Spatial Data7.2.1 The 1854 Cholera Outbreak in London7.2.2 Spatial Data in Stata7.2.3 United States Presidential Elections7.2.4 Expansion of Walmart7.2.5 Animation in Stata7.3 Textual Data7.3.1 The Disputed Authorship of The Federalist Papers7.3.2 Topic Discovery7.3.3 Document–Term Matrix and Clusters7.3.4 Authorship Prediction7.3.5 Cross Validation7.4 Summary7.5 Exercises7.5.1 International Trade Network7.5.2 Mapping US Presidential Election Results over Time7.5.3 Analyzing the Preambles of Constitutions8 NEXTGeneral IndexStata IndexStata Command Abbreviation List