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Quantitative social science : an introduction / Kosuke Imai.

by Imai, Kosuke, author.

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Social sciences -- Methodology.

Social sciences -- Research.

xix, 408 pages, 8 unnumbered pages of plates : illustrations (some color), maps (some color) ; 26 cm.

1.1.Overview of the Book -- 1.2.How to Use this Book -- 1.3.Introduction to R -- 1.3.1.Arithmetic Operations -- 1.3.2.Objects -- 1.3.3.Vectors -- 1.3.4.Functions -- 1.3.5.Data Files -- 1.3.6.Saving Objects -- 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 -- 2.1.Racial Discrimination in the Labor Market -- 2.2.Subsetting the Data in R -- 2.2.1.Logical Values and Operators -- 2.2.2.Relational Operators -- 2.2.3.Subsetting -- 2.2.4.Simple Conditional Statements -- 2.2.5.Factor Variables -- 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 -- 3.1.Measuring Civilian Victimization during Wartime -- 3.2.Handling Missing Data in R -- 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.Scatter Plot -- 3.6.2.Correlation -- 3.6.3.Quantile-Quantile Plot -- 3.7.Clustering -- 3.7.1.Matrix in R -- 3.7.2.List in R -- 3.7.3.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 -- 4.1.Predicting Election Outcomes -- 4.1.1.Loops in R -- 4.1.2.General Conditional Statements in R -- 4.1.3.Poll Predictions -- 4.2.Linear Regression -- 4.2.1.Facial Appearance and Election Outcomes -- 4.2.2.Correlation and Scatter Plots -- 4.2.3.Least Squares -- 4.2.4.Regression towards the Mean -- 4.2.5.Merging Data Sets in R -- 4.2.6.Model Fit -- 4.3.Regression and Causation -- 4.3.1.Randomized Experiments -- 4.3.2.Regression with Multiple Predictors -- 4.3.3.Heterogenous 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 -- 5.1.Textual Data -- 5.1.1.The Disputed Authorship of The Federalist Papers -- 5.1.2.Document-Term Matrix -- 5.1.3.Topic Discovery -- 5.1.4.Authorship Prediction -- 5.1.5.Cross Validation -- 5.2.Network Data -- 5.2.1.Marriage Network in Renaissance Florence -- 5.2.2.Undirected Graph and Centrality Measures -- 5.2.3.Twitter-Following Network -- 5.2.4.Directed Graph and Centrality -- 5.3.Spatial Data -- 5.3.1.The 1854 Cholera Outbreak in London -- 5.3.2.Spatial Data in R -- 5.3.3.Colors in R -- 5.3.4.US Presidential Elections -- 5.3.5.Expansion of Walmart -- 5.3.6.Animation in R -- 5.4.Summary -- 5.5.Exercises -- 5.5.1.Analyzing the Preambles of Constitutions -- 5.5.2.International Trade Network -- 5.5.3.Mapping US Presidential Election Results over Time -- 6.1.Probability -- 6.1.1.Frequentist versus Bayesian -- 6.1.2.Definition and Axioms -- 6.1.3.Permutations -- 6.1.4.Sampling with and without Replacement -- 6.1.5.Combinations -- 6.2.Conditional Probability -- 6.2.1.Conditional, Marginal, and Joint Probabilities -- 6.2.2.Independence -- 6.2.3.Bayes' Rule -- 6.2.4.Predicting Race Using Surname and Residence Location -- 6.3.Random Variables and Probability Distributions -- 6.3.1.Random Variables -- 6.3.2.Bernoulli and Uniform Distributions -- 6.3.3.Binomial Distribution -- 6.3.4.Normal Distribution -- 6.3.5.Expectation and Variance -- 6.3.6.Predicting Election Outcomes with Uncertainty -- 6.4.Large Sample Theorems -- 6.4.1.The Law of Large Numbers -- 6.4.2.The Central Limit Theorem -- 6.5.Summary -- 6.6.Exercises -- 6.6.1.The Mathematics of Enigma -- 6.6.2.A Probability Model for Betting Market Election Prediction -- 6.6.3.Election Fraud in Russia -- 7.1.Estimation -- 7.1.1.Unbiasedness and Consistency -- 7.1.2.Standard Error -- 7.1.3.Confidence Intervals -- 7.1.4.Margin of Error and Sample Size Calculation in Polls -- 7.1.5.Analysis of Randomized Controlled Trials -- 7.1.6.Analysis Based on Student's t-Distribution -- 7.2.Hypothesis Testing -- 7.2.1.Tea-Tasting Experiment -- 7.2.2.The General Framework -- 7.2.3.One-Sample Tests -- 7.2.4.Two-Sample Tests -- 7.2.5.Pitfalls of Hypothesis Testing -- 7.2.6.Power Analysis -- 7.3.Linear Regression Model with Uncertainty -- 7.3.1.Linear Regression as a Generative Model -- 7.3.2.Unbiasedness of Estimated Coefficients -- 7.3.3.Standard Errors of Estimated Coefficients -- 7.3.4.Inference about Coefficients -- 7.3.5.Inference about Predictions -- 7.4.Summary -- 7.5.Exercises -- 7.5.1.Sex Ratio and the Price of Agricultural Crops in China -- 7.5.2.File Drawer and Publication Bias in Academic Research -- 7.5.3.The 1932 German Election in the Weimar Republic.

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