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Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics.
Useful features for teaching or self-study:
*Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth).
*Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs.
*Shows readers how to carefully warrant priors on the basis of empirical data.
*Companion website features data and code for the book's examples, plus other resources.
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- Sales Rank: #628836 in Books
- Published on: 2014-07-23
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.00" w x 6.10" l, .0 pounds
- Binding: Hardcover
- 318 pages
Review
"Bayesian analysis has arrived--and Kaplan has written exactly the book that social science faculty members and graduate students need in order to learn Bayesian statistics. It is sophisticated yet accessible, complete yet an easy read. This book will ride the crest of the Bayesian wave for years to come."--William R. Shadish, PhD, Department of Psychological Sciences, University of California, Merced
"I like that this book is concise but very comprehensive, with topics ranging from the basic regression model to the advanced mixture model. Well-organized sections move from foundations; to model building, basic regression, and generalized linear models; to advanced topics. The author's explanations of concepts and examples are clear and straightforward. He has chosen his examples well; they address very commonly studied research questions in the educational sciences. The ability to access the code and data online will benefit researchers and students tremendously."--Feifei Ye, PhD, Department of Psychology in Education, University of Pittsburgh
"We are all Bayesians at heart--in that we all have prior knowledge--so why use a frequentist approach to statistics? This book can help you understand and implement a Bayesian approach."--John J. McArdle, PhD, Department of Psychology, University of Southern California
"This much-needed book bridges the gap between Bayesian statistics and social sciences. It provides the reader with basic knowledge and practical skills for applying Bayesian methodologies to data-analysis problems. The focus on Bayesian psychometric modeling is noteworthy and unique."--Jay Myung, PhD, Department of Psychology, Ohio State University
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About the Author
David Kaplan, PhD, is Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin–Madison and holds affiliate appointments in the Department of Population Health Sciences and the Center for Demography and Ecology. Dr. Kaplan’s program of research focuses on the development of Bayesian statistical methods for education research. His work on these topics is directed toward application to quasi-experimental and large-scale cross-sectional and longitudinal survey designs. He is most actively involved in the Program for International Student Assessment, sponsored by the Organisation for Economic Co-operation and Development—he served on its Technical Advisory Group from 2005 to 2009 and currently serves as Chair of its Questionnaire Expert Group. Dr. Kaplan also is a member of the Questionnaire Standing Committee of the U.S. National Assessment of Educational Progress, is a Fellow of the American Psychological Association (Division 5), and was a Jeanne Griffith Fellow at the National Center for Education Statistics.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Good conceptual exegesis with off-putting code sections
By Sitting in Seattle
The conceptual coverage here is very appropriate for social scientists -- the advantages and principles of Bayesian methods -- yet when it gets to real applications, it too often dumps R code on the reader in enormous chunks that are unsuitable for a book.
Here's an example: Bayesian factor analysis is an area of high interest and need among psychologists and other social scientists -- but the chapter on it ends with a 50 page section of R code! I'm not kidding; you can check this in the table of contents. The 50 pages are unbroken code printed in landscape format. This has several problems, of which perhaps the three most salient are (1) such massive sections of code belong in a manual or technical document, not in a book; (2) hardly anyone would realistically want to work through 50 pages of code just to implement factor analysis; and (3) it is thus more of a disincentive to using Bayesian methods than an advertisement for them!
On the other hand, the examples are focused on the kinds of data and models that social scientists care about, unlike most texts on Bayesian methods. For use in a graduate course where students are expected to implement models in R code, I suspect this book would be very useful. For readers who want more practical, immediately useful instruction, It will help with the concepts but overall might be more of a dissuasion from Bayesian methods than the author intends.
2 of 2 people found the following review helpful.
Subpar as introduction, but much better in intermediate territory
By Dimitri Shvorob
In my opinion, "Doing Bayesian data analysis" by John Kruschke is easily the best available introduction to Bayesian statistics, and I would confidently recommend DBDA as replacement of this book's first half. It's in the second half of "Bayesian statistics for the social sciences", where discussion moves to more specialized topics, that the book comes into its own and adds value. It's not a five-star book - looking at the un-commented code excerpts, I cannot say that the author gave his all to the manuscript; speaking of code, too bad the book does not use BUGS - but one that makes a positive impression.
0 of 2 people found the following review helpful.
Five Stars
By Rafael R. Ramirez Padilla
excellent
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