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Data analysis for social science : a friendly and practical introduction
Llaudet E., Imai K., PRINCETON UNIVERSITY PRESS, Princeton, New Jersey, 2023. 256 pp. Type: Book (0691199434)
Date Reviewed: May 31 2023

Earl Babbie’s classic textbook The practice of social research, which went to its 15th edition in 2021 [1], is a standard reference for social science students and researchers. Its comprehensive review of research methods is still of great value even to modern data analysts in other scientific domains. However, it lacks the recently popular programming approach to such topics. Babbie does augment his textbook with guides to using IBM’s SPSS statistical software, yet today’s students and practitioners often look for more easily accessible open-source software like R and Python.

Llaudet and Imai’s new textbook, Data analysis for social science, specifically applies the fundamentals of data analytics to the social sciences. Llaudet is a political science researcher at Boston’s Suffolk University, and Imai is a professor and statistician at Harvard. Together they have written an introductory textbook for beginning social scientists that assumes no prior training in statistics or programming, yet thoroughly covers many of the same concepts, methods, and advice found in Babbie’s books.

The authors begin with a chapter on the goals of social science data exploration and analysis, and a detailed introduction to the tools used to extract useful knowledge from such data, specifically the R statistical language and its libraries and RStudio for program development and execution, focusing on the exploration and discovery of “causal effects” in the data. Subsequent chapters use published research examples to develop understanding of the data analysis processes of measuring, predicting, and explaining.

Chapters 2 and 3 introduce the vocabulary of social science research, such as treatments, outcomes and controls, randomization, sampling, and statistical measures and graphs. Chapter 4 focuses on prediction using linear regression. There is a supplementary chapter on basic probability covering the essential ideas of populations and sampling, significance, and hypothesis testing. The authors’ explanations of these concepts are quite clear and understandable for readers encountering them for the first time, particularly their discussion of p-values. The book concludes with helpful separate indexes for concepts, mathematical notations, and R functions.

The text is attractively formatted in two columns per page, adding commentary and explanations next to the main text. Each chapter is accompanied by a “cheatsheet” that summarizes relevant concepts and R functions. There is a companion website for the book that includes the example datasets, R source code, exercises, errata, and instructor resources.

Like Babbie’s book, Llaudet and Imai’s text can serve as a valuable introduction to data analytics in any domain, not just the social sciences, and perhaps even as an introduction to statistics in general.

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Reviewer:  Harry J. Foxwell Review #: CR147595 (2307-0083)
1) Babbie, E. The practice of social research (15th ed.). Cengage, Boston, MA, 2021.
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