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.

翻译：为将认知诊断模型（CDMs）扩展至纵向研究场景，研究者广泛采用将CDM与潜变量转换模型及协变量相结合的分步法，因其具有灵活性。已有研究表明分步估计可能产生有偏结果，由此提出采用分类误差校正方法以改进未经校正的分步推断程序。本研究评估了一种统一贝叶斯动态认知诊断模型，该模型能在包含协变量的纵向场景中联合估计测量成分（项目参数、潜在属性轮廓）与转换成分（转换参数）。通过蒙特卡洛研究，我们将此联合方法与偏差校正后的分步潜变量转换CDM进行比较。结果表明，联合建模能更准确地恢复转换参数，尤其在测试长度和样本量有限的情况下优势显著，这凸显了其在纵向诊断分析中的价值，并为应用研究者提供了实践指导。