Digital Module 11: Bayesian Psychometrics

4.8 (5 votes)

Recorded On: 01/31/2020

In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their capabilities more broadly conceived as well as fitting models to characterize the psychometric properties of tasks. The approach is first developed in the general context of estimating a mean and variance of a normal distribution before turning to the context of unidimensional item response theory (IRT) models for dichotomously scored data. Dr. Levy illustrates the process of fitting Bayesian models using the JAGS software facilitated through the R statistical environment. The module is designed to be relevant for students, researchers, and data scientists in various disciplines such as education, psychology, sociology, political science, business, health and other social sciences. It contains audio-narrated slides, diagnostic quiz questions, and data-based activities with video solutions as well as curated resources and a glossary.

Keywords: Bayesian psychometrics, Bayes theorem, dichotomous data, item response theory (IRT), JAGS, Markov-chain Monte Carlo (MCMC) estimation, normal distribution, R, unidimensional models

Roy Levy

Professor, Arizona State University

Roy is a professor in the T. Denny Sanford School of Social & Family Dynamics at Arizona State University, specializing in Measurement and Statistical Analysis. He received his Ph.D. in Measurement, Statistics & Evaluation from the University of Maryland. His research and teaching interests include methodological investigations and applications in psychometrics and statistical modeling, focusing on item response theory, structural equation modeling, Bayesian networks, and Bayesian approaches to inference and modeling, as well as evidentiary principles and applications in complex assessments. He is the co-author of the book Bayesian Psychometric Modeling, and has published his work in a variety of leading methodological journals. For his work, he has received awards from the National Council on Measurement in Education, the American Educational Research Association, and the President of the United States. He currently serves on the editorial boards for Structural Equation Modeling: A Multidisciplinary Journal, Educational Measurement: Issues and Practice, Measurement: Interdisciplinary Research and Perspectives, and Educational Assessment.

Contact Roy via email at


Digital Module
Recorded 01/31/2020
Recorded 01/31/2020 The full interactive digital module with all resources, quiz questions, videos, and other materials.
DM11 VIDEO (Introduction, Version 1.0)
Open to view video.
Open to view video. Video version of the introductory section without interactive components [4 Minutes]
DM11 VIDEO (Section 1, Version 1.0)
Open to view video.
Open to view video. Video version of first content section without quizzes or interactive activities [13 Minutes]
DM11 VIDEO (Section 2, Version 1.0)
Open to view video.
Open to view video. Video version of the second content section without interactive components [30 Minutes]
DM11 VIDEO (Section 3, Version 1.0)
Open to view video.
Open to view video. Video version of the third content section without interactive components [38 Minutes]
Files for Data Activities
Open to download resource.
Open to download resource. All files for the data activities for normal distribution models and unidimensional IRT models.