Bayesian Item Response Modeling: Theory and Applications / Edition 1

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This book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for many response modeling problems. Free from computational constraints, realistic and state-of-the-art latent variable response models are considered for complex assessment and survey data to solve real-world problems. The Bayesian framework described provides a unified approach for modeling and inference, dealing with (nondata) prior information and information across multiple data sources. The book discusses methods for analyzing item response data and the complex relationships commonly associated with human response behavior and features:
• Self-contained introduction to Bayesian item response modeling and a coverage of extending standard models to handle complex assessment data
• A thorough overview of Bayesian estimation and testing methods for item response models, where MCMC methods are emphasized
• Numerous examples that cover a wide range of application areas, including education, medicine, psychology, and sociology
• Datasets and software (S+, R, and WinBUGS code) of the models and methods presented in the book are available on Bayesian Item Response Modeling is an excellent book for research professionals, including applied statisticians, psychometricians, and social scientists who analyze item response data from a Bayesian perspective. It is a guide to the growing area of Bayesian response modeling for researchers and graduate students, and will also serve them as a good reference.

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Editorial Reviews

From the Publisher
From the reviews:

“Item response theory is a general paradigm for the design and analysis of questionnaires measuring abilities and attitudes of individuals. … the book is written in a concise style and the technical level of the book is relatively high. … I believe this book makes an important contribution in summarizing much of the important literature in Bayesian IRT and I think it will lead to future books focusing on the use and interpretation of these models from a practitioner’s perspective.” (Jim Albert, Journal of the American Statistical Association, Vol. 106 (495), September, 2011)

“This book covers the parameter estimation of standard and extended IRT models using the Bayesian simulation based MCMC method. There are many Bayesian data analysis books, but this is the first book purely devoted to the Bayesian estimation of IRT models. … Overall, it is a good book for advanced learners to grasp the theoretical and technical detail of Bayesian MCMC estimation of extended IRT models adapted to a specific measurement setting.” (Hong Jiao, Psychometrika, Vol. 76 (2), April, 2011)

“This book develops a comprehensive treatment of Bayesian item response modelling … . The book is mostly self-contained. … Each chapter ends with a section of carefully thought-out exercises covering both the mathematical aspects of the models and their application to the analysis of interesting real-life data. … This book will equally cater for those users who just want to apply the models to analyze their data, and more technical users willing to get a deeper understanding of the models … .” (Eduardo Gutiérrez-Peña, International Statistical Review, Vol. 79 (3), 2011)

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Product Details

Meet the Author

Jean-Paul Fox is Associate Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His main research activities are in several areas of Bayesian response modeling. Dr. Fox has published numerous articles in the areas of Bayesian item response analysis, statistical methods for analyzing multivariate categorical response data, and nonlinear mixed effects models.

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Table of Contents

Introduction to Bayesian response modeling.- Bayesian hierarchical response modeling.- Basic elements of Bayesian statistics.- Estimation of Bayesian item response models.- Assessment of Bayesian item response models.- Multilevel item response theory models.- Random item effects models.- Response time item response models.- Randomized item response models.

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