Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial

TL;DR

This tutorial introduces how to use JAGS software for Bayesian cognitive diagnosis models, covering various models, extensions, and an empirical example to guide practitioners in implementation.

Contribution

It systematically demonstrates the application of JAGS for multiple Bayesian CDMs and extensions, providing practical guidance and code examples.

Findings

Effective implementation of Bayesian CDMs using JAGS

Extensions to polytomous attributes and testlet effects

Empirical example illustrating the modeling process

Abstract

In this article, the JAGS software program is systematically introduced to fit common Bayesian cognitive diagnosis models (CDMs), including the deterministic inputs, noisy "and" gate (DINA) model, the deterministic inputs, noisy "or" gate (DINO) model, the linear logistic model, the reduced reparameterized unified model (rRUM), and the log-linear CDM (LCDM). The unstructured latent structural model and the higher-order latent structural model are both introduced. We also show how to extend those models to consider the polytomous attributes, the testlet effect, and the longitudinal diagnosis. Finally, an empirical example is presented as a tutorial to illustrate how to use the JAGS codes in R.