<p>This book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for ma
Bayesian Analysis of Item Response Theory Models Using SAS
β Scribed by Clement A. Stone, Xiaowen Zhu
- Publisher
- SAS Institute
- Year
- 2015
- Tongue
- English
- Leaves
- 280
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 bookβs 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 developersβfor 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.
β¦ Subjects
ΠΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°;ΠΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ°;SAS / JMP;
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<span>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 m
Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. <i>Using R for Item Response Theory Model Applications</i> is a practical guide for students, instructors, practitioners, and
<p>Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. <i>Using R for Item Response Theory Model Applications</i> is a practical guide for students, instructors, practitioners,
<p>Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. <i>Using R for Item Response Theory Model Applications</i> is a practical guide for students, instructors, practitioners,