A Comparison of Joint and Stepwise Dynamic Cognitive Diagnostic Models

TL;DR

This paper introduces a unified Bayesian model for longitudinal cognitive diagnosis, demonstrating its superior accuracy over traditional stepwise methods through simulation studies.

Contribution

It proposes a joint Bayesian approach for dynamic CDMs that improves transition parameter estimation compared to bias-corrected stepwise methods.

Findings

Joint modeling yields more accurate transition estimates.

The approach performs better with limited test length and sample size.

Simulation results support the advantages of the joint model.

Abstract

To extend cognitive diagnostic models (CDMs) to longitudinal settings, stepwise approaches that integrate a CDM model with a latent transition model and covariates are widely used due to their flexibility. Previous research has shown that stepwise estimation can yield biased results, motivating classification-error correction as a means of improving inference over uncorrected stepwise procedures. In this study, we evaluate a unified Bayesian dynamic cognitive diagnostic model that jointly estimates measurement (item parameters, latent attribute profiles) and transition components (transition parameters) in longitudinal settings with covariates. We compare this joint approach with the bias-corrected stepwise latent transition CDM through a Monte Carlo study. Results demonstrate that joint modeling provides more accurate recovery of transition parameters, particularly under limited test length and sample size, underscoring its advantages for longitudinal diagnostic analysis and offering practical guidance for applied researchers.