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Sinopsis
Written especially for psychometricians, scale developers, and practitioners interested in applications of Bayesian estimation and model checking of item response theory (IRT) models, this book teaches you how to accomplish all of this with the SAS MCMC Procedure. Because of its tutorial structure, Bayesian Analysis of Item Response Theory Models Using SAS will be of immediate practical use to SAS users with some introductory background in IRT models and the Bayesian paradigm.
Working through this book’s examples, you will learn how to write the PROC MCMC programming code to estimate various simple and more complex IRT models, including the choice and specification of prior distributions, specification of the likelihood model, and interpretation of results. Specifically, you will learn PROC MCMC programming code for estimating particular models and ways to interpret results that illustrate convergence diagnostics and inferences for parameters, as well as results that can be used by scale developers—for example, the plotting of item response functions. In addition, you will learn how to compare competing IRT models for an application, as well as evaluate the fit of models with the use of posterior predictive model checking methods.
Numerous programs for conducting these analyses are provided and annotated so that you can easily modify them for your applications.
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Acerca del autor
Clement A. Stone is a professor in the Research Methodology program at the University of Pittsburgh School of Education. He is an expert in psychometrics, including educational and psychological instrument development and validation, item response theory (IRT) models and applications, and Bayesian analysis of IRT models. Also an expert in SAS software, he has used SAS extensively in research utilizing simulation methods, and he instructs graduate students in the use of SAS. He applies IRT in the development and validation of educational, psychological, and behavioral assessments, including a research focus on educational achievement, critical thinking, psychosocial stress, communication outcomes, risk factors for addiction, and physical disability. He has published numerous articles, and he coauthored a chapter on the development and analysis of performance assessments in Educational Measurement. In addition to publishing in and reviewing for numerous prominent journals, he has served on the editorial boards for the Journal of Educational Measurement, Applied Measurement in Education, Educational and Psychological Measurement, and the American Educational Research Journal. Stone holds a Ph.D. in research methods, measurement, and statistics from the University of Arizona.Xiaowen Zhu is an associate professor in the Department of Sociology and a research fellow in the Institute for Empirical Social Science Research, Xi'an Jiaotong University, China. Formerly, she was a psychometrician for the Data Recognition Corporation in the United States, working on large-scale state educational assessments. She has a strong theoretical background, operational experience, and research experience in psychometrics and applied statistics. She is especially adept at Bayesian analysis of IRT models and has published journal articles on this subject. In addition, she has used SAS throughout her research career for general statistical analysis and simulation studies. Zhu holds a Ph.D. in research methodology from the University of Pittsburgh.
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