Data Analysis for Social Science: A Friendly and Practical Introduction

Review
“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
“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
“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
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.