Overview
- Introduces Bayesian item response modeling with examples in a wide array of contexts
- Gives a unified treatment of extending traditional item response models to handle more complex assessment data
- Computer code and examples facilitate the Bayesian approach to item response modeling
- Includes supplementary material: sn.pub/extras
Part of the book series: Statistics for Social and Behavioral Sciences (SSBS)
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About this book
The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models.
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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Reviews
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 ofextended 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)
Authors and Affiliations
About 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.
Bibliographic Information
Book Title: Bayesian Item Response Modeling
Book Subtitle: Theory and Applications
Authors: Jean-Paul Fox
Series Title: Statistics for Social and Behavioral Sciences
DOI: https://doi.org/10.1007/978-1-4419-0742-4
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC 2010
Hardcover ISBN: 978-1-4419-0741-7Published: 08 June 2010
Softcover ISBN: 978-1-4614-2606-6Published: 05 September 2012
eBook ISBN: 978-1-4419-0742-4Published: 19 May 2010
Series ISSN: 2199-7357
Series E-ISSN: 2199-7365
Edition Number: 1
Number of Pages: XIV, 313
Topics: Methodology of the Social Sciences, Probability Theory and Stochastic Processes, Statistics for Social Sciences, Humanities, Law, Psychometrics, Assessment, Testing and Evaluation, Marketing