"I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods.” -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology
"Kruschke's text covers a much broader range of traditional experimental designs.has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology
"John Kruschke has written a book on Statistics. It's better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it's truly amazin'!”-James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University
"In a December article in The New Yorker, Jonah Lehrer pointed out that some phenomena in the psychology literature are not always repeatable. One reason for this failure to replicate results comes from the kinds of statistics often used in Psychology. We use a procedure called Null Hypothesis Testing that was developed over 100 years ago. More recently, statisticians and psychologists have been working to create a new form of statistical testing based on Bayesian statistics. These methods may help us to avoid publishing studies that are not likely to replicate. John Kruschke published a nice tutorial on how to use these methods." -2010's top ten advances in psychology on Psychology Today's blog
"The intended audience for this book is a first-year graduate student or advanced undergraduate in the social or biological sciences, but one whose mathematical background is sufficient for them to not be put off by occasional references to calculus. Kruschke also provides a comprehensive solution manual for the exercises in each chapter. He says he has worked on his book for six years and it shows, not least because it has few typographical errors and is well-presented. In summary, this book has several features that could make it preferable to its competitors.it is impressive that Kruschke is able to quickly bring readers up to speed on techniques such as robust regression and repeated-measures regression that would be considered ''advanced'' in the conventional NHST curriculum. His extensions from linear regression to logistic, ordinal probit and Poisson regression are very clearly articulated and will outfit students with a very adaptable statistical toolbox. This is the best introductory textbook on Bayesian MCMC techniques I have read, and the most suitable for psychology students. It fills a gap I described in my recent review of six other introductory Bayesian method texts (Smithson, 2010). I look forward to using it in my own teaching, and I recommend it to anyone wishing to introduce graduate or advanced undergraduate students to the emerging Bayesian revolution."--Journal of Mathematical Psychology
"In sum, this is a new kind of textbook to teach a kind of statistical analysis that will be new to its audience. It uses a tutorial approach and instills in its students the tools of the trade: coding, debugging, simulating, and plotting. Though some will surely look down on its folksy tone, its extended analogies and cautious commenting, these measures will probably do much more good than harm. The text has the potential to change the methodological toolbox of a new generation of social scientists, bringing them up to a level of computation, modeling, and analysis that they might not have thought to be within their grasp. Where past approaches to teaching statistics to those in psychology and economics have not lead to widespread insight, this tutorial approach might."--Journal of Economic Psychology
"I would describe this book as revolutionary, at least in the context of psychology. It is, to my knowledge, the first book of its kind in this field to provide a general introduction to exclusively Bayesian statistical methods. In addition, it does so almost entirely by way of Monte Carlo simulation methods. While reasonable minds may disagree, it is arguable that both the general Bayesian framework advocated here, and the heavy use of Monte Carlo simulations, are destined to be the future of all data-analysis, whether in psychology or elsewhere.the ideas and methods presented here will eventually be seen as the foundations for new approaches to statistics that will become commonplace in the near future."--British Journal of Mathematical and Statistical Psychology
"There are quite a few books on Bayesian statistics, but what makes Doing Bayesian Data Analysis: A Tutorial With R and BUGS stand out for me is the author's focus of the book-writing for real people with real data. From the very first chapter, the engaging writing style will get readers excited about this topic, a comment one can rarely make about statistical books. Clearly a master teacher, the author, John Kruschke, uses plain language to explain complex ideas and concepts. A comprehensive website is associated with the book and provides program codes, examples, data, and solutions to the exercises. If the book is used to teach a statistics course, this set of materials will be necessary and helpful for students as they go through the materials in the book step by step."--PsycCritiques
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).
This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.
