Quantitative social science an introduction

Includes bibliographical references and indexes.

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.

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 economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty 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 the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. --