Abstract

The G-DINA model is a versatile cognitive diagnostic model used for individuals' classification. In this paper, we propose a Bayesian formulation of the G-DINA model and an estimation method using an MCMC Gibbs Sampling algorithm, implemented using the software JAGS. A simulation study was designed to evaluate the parameters recovery and the estimation accuracy of the Bayesian implementation of G-DINA and the results were compared with the standard frequentist approach in four scenarios. The results show that the proposed Bayesian implementation recovers all parameters and has good accuracy in the estimation, with performance similar to or better than the frequentist approach in all simulated scenarios. As an application, we propose a new methodology of classification of depression for respondents to the Beck Depression Inventory (BDI) based on an existing one, by replacing the original DINA model for the G-DINA. A comparative analysis of the application of these two methods in a data set from 1111 respondents of the BDI test was conducted. The results indicate that the methodology with the G-DINA model is more suitable for this application.