List of Figures List of Tables 1. Learn about R and Write First Toy Programs 2. Get Data Ready: Import, Inspect, and Prepare Data 3. One-Sample and Difference of Means Tests 4. Covariance and Correlation 5. Regression Analysis 6. Regression Diagnostics and Sensitivity Analysis 7. Replicate Findings in Published Analyses 8. Appendix: A Brief Introduction to Analyzing Discrete Data

"In this useful new book, Quan Li is your expert guide to the R Project for Statistical Computing and the Replication Movement-two massive, ongoing revolutions in quantitative social analysis. If you missed some of the revolutions, do not miss this book."-Gary King, Weatherhead University Professor and director of the Institute for Quantitative Social Science, Harvard University "Using R for Data Analysis in Social Sciences is a tremendous resource for students encountering R and quantitative methods for the first time. Professor Li teaches students nuts and bolts R skills while laying the foundation for statistical inference in the context of motivating questions about politics. Students learn how to describe data, apply the logic of statistical inference, and estimate, diagnose, and interpret linear regression models using data from published research. Along the way, Professor Li instills good programming habits and reinforces the importance of replication in the social sciences. This practical approach to data analysis means Using R for Data Analysis in Social Sciences will become students' go-to resource and a standard text assigned in undergraduate methods courses in the social sciences." -Suzanna Linn, Professor of Political Science, Penn State University "For any student who is interested in computational social science, this book provides useful guidance into the process of managing, visualizing, and analyzing data. It is unique and clever and contains practical insights into using an open-source R programming environment. The chapter with replication exercises will guide you through applying various statistical methods to conduct independent research. "-In Song Kim, Assistant Professor of Political Science, MIT

Selling point: Step-by-step guide on data preparation, visualization, analysis, and reporting Selling point: Examines the logic and procedures of statistical analysis, using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics Selling point: Illustrates how to reproduce and replicate the findings in published journal articles and diagnose model assumption violations Selling point: Test-used in undergraduate classes and student self-study, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research. Selling point: Offers enormous pedagogical value in terms of how to teach computer programming, data analysis, and empirical social scientific research

Dr. Quan Li is Professor of Political Science at Texas A&M University. His research has appeared in over thirty articles in numerous journals and two coauthored books, Democracy and Economic Openness in an Interconnected System: Complex Transformations and Politics and Foreign Direct Investment. He has served on the editorial boards of American Journal of Political Science, Journal of Politics, International Studies Quarterly, and International Interactions.

Selling point: Step-by-step guide on data preparation, visualization, analysis, and reporting Selling point: Examines the logic and procedures of statistical analysis, using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics Selling point: Illustrates how to reproduce and replicate the findings in published journal articles and diagnose model assumption violations Selling point: Test-used in undergraduate classes and student self-study, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research. Selling point: Offers enormous pedagogical value in terms of how to teach computer programming, data analysis, and empirical social scientific research