Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach

Qianru Liang, Jimmy de la Torre and Nancy Law
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Nancy Law: The University of Hong Kong

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 6, 690-718

Abstract: To expand the use of cognitive diagnosis models (CDMs) to longitudinal assessments, this study proposes a bias-corrected three-step estimation approach for latent transition CDMs with covariates by integrating a general CDM and a latent transition model. The proposed method can be used to assess changes in attribute mastery status and attribute profiles and to evaluate the covariate effects on both the initial state and transition probabilities over time using latent (multinomial) logistic regression. Because stepwise approaches generally yield biased estimates, correction for classification error probabilities is considered in this study. The results of the simulation study showed that the proposed method yielded more accurate parameter estimates than the uncorrected approach. The use of the proposed method is also illustrated using a set of real data.

Keywords: latent transition analysis; cognitive diagnosis models; G-DINA model; bias-correction; three-step approach; covariates (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:6:p:690-718

DOI: 10.3102/10769986231163320

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