Diagnostic Measurement: Theory, Methods, and Applicationsby Andre A. Rupp, Jonathan Templin, Robert A. Henson
This book provides a comprehensive introduction to the theory and practice of diagnostic classification models (DCMs), which are useful for statistically driven diagnostic decision making. DCMs can be employed in a wide range of disciplines, including educational assessment and clinical psychology. For the first time in a single volume, the authors present the key
This book provides a comprehensive introduction to the theory and practice of diagnostic classification models (DCMs), which are useful for statistically driven diagnostic decision making. DCMs can be employed in a wide range of disciplines, including educational assessment and clinical psychology. For the first time in a single volume, the authors present the key conceptual underpinnings and methodological foundations for applying these models in practice. Specifically, they discuss a unified approach to DCMs, the mathematical structure of DCMs and their relationship to other latent variable models, and the implementation and estimation of DCMs using Mplus. The book's highly accessible language, real-world applications, numerous examples, and clearly annotated equations will encourage professionals and students to explore the utility and statistical properties of DCMs in their own projects. The companion website (www.guilford.com/rupp-materials) features chapter exercises with answers, data sets, Mplus syntax code, and output.
WinnerAward for Significant Contribution to Educational Measurement and Research Methodology, AERA Division D
"The readability of the book is excellent. It offers a good, basic-level exposition of the underpinnings of cognitive diagnostic assessment. It is particularly important for a book like this to be accessible to a broader audience beyond experts in cognitive diagnosis. The book covers cognitive foundations, makes connections with the most salient aspects of assessment validity, and includes detailed derivations and discussion of core cognitive diagnostic models. The examples are especially effective and clear, as is the tabulation of model parameters in the discussion of six core models and their relationship to the log-linear modeling framework. I would recommend this text for use in graduate seminars and expect to quote from it and cite it in professional presentations and papers. There is no other book available with comparable breadth."Lou DiBello, PhD, Associate Director, Learning Sciences Research Institute, University of Illinois-Chicago
"The coverage in this book is rich and clear. It addresses all of the current topics in diagnostic assessment. Each topic is explained in detail, along with examples and diagrams. The sequence of the materials is easy to follow. I love the Applications section, which illustrates how DCMs can be estimated with the software program Mplus."Yi-hsin Chen, PhD, Department of Educational Measurement and Research, University of South Florida
"The most authoritative, comprehensive source to date on every important aspect of diagnostic measurement, including theory, methods, and applications. The book includes recent advances in the unification of cognitive theory and psychometric methodology. It covers technical issues, such as model specification and parameter estimation, and extends the treatment to a variety of disciplines, from education to clinical and business settings. I was impressed by the reader-friendly presentation. The writing is clear and smooth, making this complex subject matter much more accessible and less intimidating than one might expect. The authors effectively use many examples, tables, and figures to explain difficult concepts. I would definitely consider this book for my professional use and my class use with doctoral psychometric students."Lihshing Leigh Wang, PhD, School of Education, University of Cincinnati
"I would strongly recommend this book to colleagues and students. It provides an interdisciplinary perspective on diagnostic testing; has a strong instructional focus, where concepts and statistical models are explained in a concise and clear way; and covers the entire range of testing, from item development to test score reporting."Mark J. Gierl, PhD, Director, Centre for Research in Applied Measurement and Evaluation, Department of Educational Psychology, University of Alberta, Canada
Meet the Author
André A. Rupp is Assistant Professor in the Department of Measurement, Statistics, and Evaluation at the University of Maryland. Dr. Rupp’s current research centers on developing methodological frameworks and modeling approaches that support rigorous interdisciplinary exchange between measurement specialists, substantive researchers, and applied practitioners. Two promising research areas in which he is intimately involved are diagnostic classification models (DCMs), which are the focus of this book, and modern measurement approaches for innovative digital learning environments.
Jonathan Templin is Assistant Professor in the Department of Educational Psychology and Instructional Technology and the Georgia Center for Assessment at the University of Georgia. His research focus is on the development and application of latent-variable models for educational and psychological measurement, with much of his work being in the field of DCMs. He has sought to expand the practicality of DCMs by developing models for specific purposes and presenting his research in didactic ways to increase the potential for DCM use by scientists and practitioners in multiple research areas.
Robert A. Henson is Assistant Professor in the Department of Educational Research Methodology at the University of North Carolina at Greensboro. His major research interests have focused on DCMs, with a primary focus on developing concepts and procedures for DCMs that are analogous to common concepts and procedures of more traditional measurement techniques. Dr. Henson’s hope is that, by expanding concepts that are already familiar in other settings, DCMs will be developed using a common methodology for assessments that will provide fine-grained information. Additional interests include latent-variable models in general and mixed linear models.
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