*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.
Publisher: Guilford Publications
Number of pages: 318
Weight: 700 g
Dimensions: 234 x 156 x 25 mm
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
-As the name suggests, Bayesian Statistics for the Social Sciences is a valuable read for researchers, practitioners, teachers, and graduate students in the field of social sciences....Extremely accessible and incredibly delightful....The wide breadth of topics covered, along with the author's clear and engaging style of writing and inclusion of numerous examples, should provide an adequate foundation for any psychologist wishing to take a leap into Bayesian thinking. Furthermore, the technical details and analytic aspects provided in all chapters should equip readers with enough knowledge to embark on Bayesian analysis with their own research data.--Psychometrika, 3/1/2017
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