About the Book
An ideal textbook for complete beginners--teaches from scratch R, statistics, and the fundamentals of quantitative social science
Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses' strengths and limitations.
Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.
Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.
Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book's website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.
Assumes no prior knowledge of statistics or coding.
Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.
Provides cheatsheets of statistical concepts and R code.
Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed.
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.
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.
Review :
"Data Analysis for Social Science is a great textbook for any undergraduate research methods course. I especially like that it teaches point estimates and uncertainty separately. In the past, when I taught these concepts together, I found students were overwhelmed. Breaking them up makes the statistics easier to understand. It's a genius idea! I truly can't recommend this book enough!"--Christopher Ojeda, University of California, Merced
"My favorite feature of Data Analysis for Social Science is that it puts causal inference first, before probability and statistical inference. I have found that this unconventional order is gentler and more engaging for complete beginners than the approach used in many other books. It also allows students with some prior knowledge of statistics to learn something new from the start."--Max Goplerud, University of Pittsburgh
"This is the book that I plan to teach from next time I teach introductory statistics. As it is, I recommend it as a reference for students in more advanced classes such as Applied Regression and Causal Inference, if they want a clean refresher from first principles."--Andrew Gelman, coauthor of Regression and Other Stories
"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
"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
"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
"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
"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
"Data science from zero to sixty--gently, expertly, quickly."--Gary King, Weatherhead University Professor, Harvard University
"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
"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 Bacak, School of Criminal Justice, Rutgers University, Newark
"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
