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. *Utilizes open-source R software programs available on the 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.

David Kaplan, PhD, is Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin-Madison, and holds an affiliate appointment in the Department of Population Health Sciences. His research focuses on the development of Bayesian methods applied to experimental, quasi-experimental, and observational education research. In the experimental setting, Dr. Kaplan is particularly interested in the development of Bayesian causal mediation models for randomized experiments. In the quasi-experimental setting, his focus is the development and application of Bayesian propensity score analysis. In the observational setting, he is interested in the development of Bayesian posterior predictive causal inference with applications to international large-scale assessments. His collaborative research involves applications of advanced quantitative methodologies to problems in educational psychology, human development, and international comparative education. Dr. Kaplan is actively involved in the Program for International Student Assessment (PISA) of the Organization for Economic Cooperation and Development; he has served on PISA's technical advisory group and currently serves on its questionnaire expert group.