Invariant Measurement with Explanatory Rasch Models in the Human Sciences
Description
This textbook introduces invariant measurement in the human sciences based on the principles of Rasch measurement theory and presents explanatory Rasch models that integrate item and person covariates that can deepen the understanding of item response processes. The basic family of Rasch models reflects descriptive measurement models. This book describes explanatory item response models that can be used to explore and predict item response probabilities using item and person variables (covariates) that can be explicitly included in measurement models for the purpose of constructing scales to represent latent variables.
The analyses described in this book offer methodological tools that can be used to delve more deeply into person responses to a set of items designed to represent a latent variable or construct. Item and person covariates provide insight into aspects of measurement procedures that may influence or explain measurement properties, such as item difficulty or level of person achievement. Rasch measurement theory provides the opportunity to include both person and item covariates that allows for more nuanced substantive analyses of latent variables that go beyond description. Across chapters, the book progresses from foundational principles and generalized linear mixed models (GLMMs) for estimating Rasch models, to applications in measurement quality evaluation, explanatory modeling, rater-mediated assessments, instrument development, and rating scale analysis.
Invariant Measurement with Explanatory Rasch Models in the Human Sciences will be a valuable resource for advanced undergraduate and graduate students in the human sciences attending advanced undergraduate courses on test theory and measurement, as well as graduate-level courses and seminars on rating scales and invariant measurement. This book can also be used as a supplementary textbook in a variety of courses on research methodology in the social, behavioral and health sciences including psychology, public health, anthropology, sociology education and economics.
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Professor George Engelhard, Jr. , Ph.D., is at The University of Georgia (2013 to present). He is professor emeritus at Emory University (1985 to 2013). Professor Engelhard received his Ph.D. in 1985 from The University of Chicago in the MESA Program--measurement, evaluation, and statistical analysis. While he was at The University of Chicago, he worked closely with Professors Ben Bloom and Ben Wright. Professor Engelhard is the author of three books and the co-editor of five edited volumes. He has over 200 journal articles, book chapters, and monographs. He is a past president of the Pacific Rim Objective Measurement Society. He provides technical advice on measurement and policy issues both nationally and internationally. He is a fellow of the American Educational Research Association.
Stefanie A. Wind , Ph.D. is a Professor of Educational Measurement at the University of Alabama. She received her PhD in Educational Measurement from Emory University. Her primary research interests include the exploration of methodological issues in the field of educational measurement, with emphases on methods related to rater-mediated assessments, rating scales, latent trait models, and nonparametric item response theory. Her research appears in peer-reviewed journals and edited volumes in the fields of educational measurement and various substantive areas, including music, language, and counseling. She has authored or co-authored four books related to measurement, and she has received awards for her research, including the Alicia Cascallar early career scholar award from the National Council on Measurement in Education and the Georg William Rasch Early Career Scholar award from the American Educational Research Association.
Content
Chapter 1. Introduction.- Chapter 2. Measurement, Explanation, and Invariance.- Chapter 3. Rasch Measurement Theory and Generalized Linear Mixed Models.- Chapter 4. Evaluating Measurement Quality for Dichotomous Rasch Models.- Chapter 5. Explanatory Rasch Models: Linear Logistic Rasch Models and Latent Regression Rasch Models.- Chapter 6. Rater-Mediated Assessments with Explanatory Rasch Models.- Chapter 7. Instrument Development with Explanatory Rasch Models.- Chapter 8. Rating Scale Analysis for Polytomous Explanatory Rasch Models.- Chapter 9. Conclusion.
