NEWS.md

In LTCDM: Latent Transition Cognitive Diagnosis Model with Covariates

LTCDM Package Updates

Version 1.1.0 - 2025-05-17

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) . 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

LTCDM documentation built on Aug. 21, 2025, 5:26 p.m.