Digital Module 11: Bayesian Psychometric Modeling

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1 Scopus citations


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 a 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 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.

Original languageEnglish (US)
Pages (from-to)94-95
Number of pages2
JournalEducational Measurement: Issues and Practice
Issue number1
StatePublished - Mar 1 2020


  • Bayes theorem
  • Bayesian psychometrics
  • JAGS
  • Markov chain Monte Carlo estimation
  • R
  • dichotomous data
  • item response theory
  • normal distribution
  • unidimensional models

ASJC Scopus subject areas

  • Education


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