The Theory and Practice of Item Response Theory (Methodology in the Social Sciences)

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9781593858698: The Theory and Practice of Item Response Theory (Methodology in the Social Sciences)

Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications.

Designed for researchers, psychometric professionals, and advanced students, this book clearly presents both the "how-to" and the "why" of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. Chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another.

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About the Author:

R. J. de Ayala is Professor of Educational Psychology at the University of Nebraska-Lincoln. His research interests include psychometrics, item response theory, computerized adaptive testing, applied statistics, and multilevel models. His work has appeared in Applied Psychological Measurement, Applied Measurement in Education, the British Journal of Mathematical and Statistical Psychology, Educational and Psychological Measurement, the Journal of Applied Measurement, and the Journal of Educational Measurement. He is a Fellow of the American Psychological Association’s Division 5: Evaluation, Measurement, and Statistics, as well as of the American Educational Research Association.

Review:

"This book is jam-packed with useful information. It includes basic, practical programming examples, with clear explanations of WinSTEPS and BILOG scripts, and step-by-step interpretations of goodness of fit in IRT problems. The author also covers more advanced forms of IRT, including multicategory items, multidimensional latent influences, and advanced multiple-group problems of linking and equating. A tour de force!"--John J. McArdle, PhD, Head, Quantitative Methods Area, Department of Psychology, University of Southern California "A very well-organized and useful introduction to IRT. The book has an excellent structure that covers widely used IRT models and most of their major applications. The author has done an outstanding job of balancing the mathematical with the conceptual, and each chapter contains examples of applications to real data using commercially available software. The book is liberally supplemented by the kinds of graphic displays that can help neophytes understand the complexities of IRT. An especially useful feature is the up-front glossary of notation and acronyms. This is an excellent text for a one-semester graduate-level course in IRT, and should provide students with the knowledge they require to delve deeper into IRT models and their applications. It is also a useful reference for psychological and educational researchers who apply IRT in their work."--David J. Weiss, PhD, Department of Psychology, University of Minnesota; Editor Emeritus, Applied Psychological Measurement "This book provides a thorough overview of item response theory methodology, with a nice blend of theoretical psychometrics and practical applications. The coverage is quite complete, including the standard dichotomous and polytomous unidimensional models as well as multidimensional models. The examples are very useful. This book will serve very well as a technical reference and as a text for upper-level psychometric theory courses."--Mark D. Reckase, PhD, Department of Counseling, Educational Psychology, and Special Education, Michigan State University "De Ayala does a masterful job of describing the fundamental theory and the many applications of IRT. I am impressed by the breadth of models he covers and the detail he presents on various estimation methods. Coverage includes the standard Rasch; one-, two-, and three-parameter models; polytomous and multidimensional models; and applications to linking/equating and differential item functioning. This is a well-written book that will be useful for graduate students, researchers, and practicing measurement specialists in education, health, and psychology. The greatest strength of this book is de Ayala's ability to present IRT in an engaging, accessible manner."--Bruno D. Zumbo, PhD, Paragon–UBC Professor of Psychometrics and Measurement, University of British Columbia   "Offers a good roadmap to the complex array of IRT model parameters, estimation methods, and readily available IRT programs. By juxtaposing algebraic development of IRT models (and model estimation) alongside annotated results and software output from applied examples, this book provides an excellent resource for both intermediate and advanced IRT practitioners. The applied researcher will find this book to be an excellent practical resource with numerous examples that use multiple software packages to analyze the same datasets."--Scott M. Hofer, PhD, Department of Human Development and Family Sciences, Oregon State University "The book has an excellent balance among the technical, conceptual, and practical aspects of item response theory. It is comprehensive; provides example scripts and output from a variety of popular item response programs; and uses selected data sets throughout the book, making model and program comparisons possible. I also liked the coverage of commonly asked questions related to model fit, item fit, and appropriate sample sizes, which are often missing in item response theory texts."--Kevin J. Grimm, PhD, Department of Psychology, Arizona State University
"The main strength of the text is in the descriptions and elaborations of the common IRT models....De Ayala also covers fundamental relationships that exist between models, such as the relationships between the parameters of the nominal response model and the partial credit model. In addition, the chapters contain practical advice for sample sizes commonly used with each model and how to interpret the parameters. De Ayala also presents results as statistical indices and graphics for various examples across different contexts, which allows readers the ability to see how the models work from several different perspectives....Does a good job of introducing common estimation strategies employed in IRT software packages. Especially helpful are the illustrations de Ayala includes with the code from IRT software packages." (Psychometrika 2009-01-01)

"A must read for practitioners who use item response theory to calibrate test data. It also would serve as a tremendous resource for measurement researchers who daily navigate the circuitous paths of various IRT estimation software programs to analyze and understand their assessment data....Each of the 12 chapters is packed with annotated examples of how to use IRT estimation software and the subsequent output....The author does an excellent job of supplementing explanations of various models with calibration examples and output of multiple data sets using several different IRT calibration software programs including BILOG, MULTILOG, BIGSTEPS, and NOHARM....The book is more practitioner-oriented and applied than previous classic books that provide foundational understanding of IRT models and applications....Would be an excellent text for a graduate level IRT class in which the goal of the course would be to review dichotomous, polytomous, and multidimensional IRT models an how to estimate parameters in the various models using a variety of commercially available software....I would encourage all testing practitioners who work with various IRT models, as well as graduate students who plan to go into the measurement field, to seriously consider this book. It is an excellent resource book and one that provides the reader with insight into the rationale and application of the different IRT models. I applaud Dr. de Ayala for all the time and effort he has put into this book. He has clearly done the measurement field a great service." (Journal of Educational Measurement 2009-01-01)

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