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.