Data Analysis for Social Science: A Friendly and Practical Introduction | Elena Llaudet - Kosuke Imai

An ideal textbook for an introductory course on quantitative methods for social scientists―assumes no prior knowledge of statistics or coding

Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.

• Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to use
• Teaches how to measure, predict, and explain quantities of interest based on data
• Shows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experiments
• Assumes no prior knowledge of statistics or coding
• Specifically designed to accommodate students with a variety of math backgrounds
• Provides cheatsheets of statistical concepts and R code
• Supporting materials available online, including real-world datasets and the code to analyze them, plus―for instructor use―sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions

Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.

Editorial Reviews 

Review

“I have been teaching statistics for twenty-five years and I have never seen a book this well done. Data Analysis for Social Science is such a perfect combination of what students need to know. The authors’ descriptions of the basic logic of causality, along with the many practical examples and visuals, are amazing features. Also, I have been resisting teaching intro students R because I am very watchful of overloading their bandwidth and I worry about killing their spirit with buggy code; I want them to love data analysis as much as I do! This book made me a convert. I am going to spend the time to learn R so that I can assign this book.”―Vanessa Baird, University of Colorado, Boulder

“This is without doubt the best book to get started with data analysis in the social sciences. Readers learn best practices in research design, measurement, data analysis, and data visualization, all in an approachable and engaging way. My students―all of them complete novices―were easily able to conduct their own analyses after working through this book.”―Simon Weschle, Syracuse University

“I love this book. More importantly, my students love this book. Data Analysis for Social Science is the perfect introduction to causal inference, probability and statistics, and the open-source programming language R, for students without prior experience. With multiple exercises using R Markdown and a variety of datasets drawn from the research literature, Data Analysis for Social Science gives students a hands-on path to build their skills and confidence.”―Anna Harvey, New York University

“Data science from zero to sixty―gently, expertly, quickly.”―Gary King, Weatherhead University Professor, Harvard University

“This book will transform the way we teach data science in the social sciences. Assuming zero background knowledge, it takes readers step-by-step through the most important concepts of data analysis and coding without sacrificing rigor. With clear explanations, beautiful visuals, and engaging examples, Data Analysis for Social Science is the obvious choice for any student looking to build their data science tool kit.”―Molly Roberts, University of California, San Diego

“I have used Data Analysis for Social Science to teach required undergraduate courses with great success. Students liked the clear explanations and relevant real-world examples, and they even found coding in R fun! By the end, they walked away excited about how these skills opened up new career opportunities and helped them understand the research discussed in other classes.”―Alicia Cooperman, George Washington University

“People have been basically writing the same introductory statistics book for the past thirty-five years. It’s good to see something that’s fundamentally modern. Yes, there have been other modern books that have been both rigorous and accessible, but this is the first I’ve seen that can function as an introductory textbook.”―Andrew Gelman, coauthor of Regression and Other Stories

“Looking to get started with data science, but scared it’d be too complicated? This book has you covered. Data Analysis for Social Science truly delivers what the title claims: friendly and practical. The focus is on experimental data and causal inference much more than on multiple regression analysis, reflecting recent developments in the social sciences. I don’t think I’ve seen a more accessible introduction to R and RStudio―cheat sheets included!”―Didier Ruedin, University of Neuchâtel

“Following the step-by-step guidance provided in this book, I built my skills in R rather than another expensive proprietary software, allowing me to share my growing knowledge with my working-class, first-generation students. I am confident I can continue to independently develop these skills in ways that support both my teaching and research.”―Jamie D. Gravell, California State University, Stanislaus

“At last, we have a truly modern introduction to social science statistics. The authors do not shy away from topics like causal inference, and they gently and seamlessly integrate instructions on how to use R. This textbook is a generous gift to both students and teachers.”―Valerio Baćak, School of Criminal Justice, Rutgers University, Newark

“A very sensible and intuitive introduction to data science. Llaudet and Imai do an excellent job of explaining the why of data analysis along with the how. I would recommend this book to anyone looking for a nice primer on data science coupled with a good set of tools using the R software.”―Craig Depken, University of North Carolina, Charlotte

About the Author

Elena Llaudet is Associate Professor of Political Science at Suffolk University in Boston. Kosuke Imai is Professor of Government and of Statistics at Harvard University.