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