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Bayesian Analysis for the Social Sciences Hardcover – December 2, 2009

ISBN-13: 978-0470011546 ISBN-10: 0470011548 Edition: 1st

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Product Details

  • Hardcover: 598 pages
  • Publisher: Wiley; 1 edition (December 2, 2009)
  • Language: English
  • ISBN-10: 0470011548
  • ISBN-13: 978-0470011546
  • Product Dimensions: 6.9 x 1.5 x 9.9 inches
  • Shipping Weight: 2.6 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #561,107 in Books (See Top 100 in Books)

Editorial Reviews


“This is a comprehensive text on applied Bayesian statistics. Though it is primarily aimed at social scientists with strong computational and statistical backgrounds, its scope should appeal to a wider readership.  I recommend it to anybody interested in actually applying Bayesian methods.”   (Significance, 1 June 2010)

"As in many texts, each chapter ends with a collection of exercises which would make this text suitable for teaching a one-semester course in Bayesian methods with applications in the social sciences . . . with this small caveat, I was impressed with the text and believe it would be a worthy candidate for a first Bayesian courses that gives the student a balanced view of the theory and practice of Bayesian thinking." (The American Statistician, 1 February 2011)

From the Back Cover

Bayesian Analysis for the Social Sciences provides a thorough yet accessible treatment of Bayesian statistical inference in social science settings.

The first part of this book presents the foundations of Bayesian inference, via simple inferential problems in the social sciences: proportions, cross-tabulations, counts, means and regression analysis. A review of modern, simulation-based inference is presented with a detailed examination of the suite of computational tools (Markov chain Monte Carlo algorithms) that underlie the “Bayesian revolution” in contemporary statistics. Furthermore, the book introduces the general purpose Bayesian computer programs BUGS and JAGS along with numerous examples, and a detailed consideration of the art of using these programs in real-world settings. 

The second half of the book focuses on intermediate to advanced applications in the social sciences, including hierarchical or “multi-level” models, models for discrete responses (binary, ordinal, and multinomial data), measurement models (factor analysis, item-response models, dynamic linear models), and mixture models, along with models that are interesting hybrids of these models.  Each model is accompanied by worked examples using BUGS/JAGS, using data from political science, sociology, psychology, education, communications, economics and anthropology.

Each chapter is accompanied with exercises to further the students' understanding of Bayesian methods and applications.  Extensive appendices provide important technical background and proofs of key theoretical propositions.

This book presents a forceful argument for the philosophical and practical utility of the Bayesian approach in many social science settings. Graduate and postgraduate students in such fields as political science, sociology, psychology, communications, education, and economics and statisticians will find much value in this book.

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10 of 10 people found the following review helpful By Joel Cadwell on February 9, 2011
Format: Hardcover Verified Purchase
I found this book to be very helpful. Jackman takes his time to explain MCMC, slowly and comprehensively. He divides Monte Carlo and Markov chains into two separate chapters. Then, he combines the techniques and shows how to run JAGS from R to obtain summaries and estimates. It is especially helpful that he includes a number of examples, which he explains in great detail (including line-by-line discussion of the R code). Finally, the book concludes with three chapters of useful applications. Although hierarchical models are covered by other texts (e.g., Gelman and Hill, 2007), Jackman's chapter is well worth reading as he systematically introduces the model and shows how to run the programs in R. This applications section ends with the clearest explanation of Bayesian measurement models that I have seen. (You should also visit Jackman's website where he has code and materials for a course he teaches using this text.)

I would highly recommend this book for anyone with a social science background looking for a comprehensive introduction to Bayesian inference and the R packages needed to run the analysis.
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9 of 13 people found the following review helpful By jde on January 23, 2010
Format: Hardcover Verified Purchase
This 2009 text book on Bayesian Analysis at the graduate school level is the best I have ever seen, and is a welcomed addition to the literature. It is for serious "scholars" of statistics, applied statistics, and comples data analysis. It comes with code and examples ready for use in the R statistical computing environment.
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