Latent Transition Cognitive Diagnosis Model with Covariates
Overview
Implementation of the three-step approach of (latent transition) cognitive diagnosis model (CDM) with covariates. This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) doi:10.3102/10769986231163320 and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) doi:10.17632/kpjp3gnwbt.1.
New Features
- Added a new method for single time-point analyses (cross-sectional data), which enables researchers to investigate how the covariates are associated with attribute mastery. The steps involved in the three-step approach are: (1) fitting a CDM, (2) assigning examinees to latent classes and computing the CEPs, and (3) estimating the logistic regression model with the CEPs. This method was proposed in Iaconangelo (2017) and was adopted in a cross-sectional analysis of digital literacy assessment data in Liang et al (2021) <doi: 10.1016/j.chb.2021.106850>.
References:
Iaconangelo, C. (2017). Uses of classification error probabilities in the three-step approach to estimating cognitive diagnosis models (Doctoral dissertation). https://rucore.libraries.rutgers.edu/rutgers-lib/55495/PDF/1/play/
Liang, Q., de la Torre, J., & Law, N. (2021). Do background characteristics matter in children’s mastery of digital literacy? A cognitive diagnosis model analysis. Computers in Human Behavior, 122, 106850. https://doi.org/10.1016/j.chb.2021.106850
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- 2024-05-15
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