TDCM

An R Package for Estimating Longitudinal Diagnostic Classification Models

Bibliographic Data

ID19290715
AuthorsMatthew J Madison (0000-0002-2944-7442, University of Georgia, corresponding author), Moongee Jeon (0000-0002-5880-4146, University of California, Los Angeles), Michael Cotterell (University of Georgia), Michael E Cotterell (0000-0002-4302-0891, University of Georgia), Sergio Haab (University of Iowa), Selay Zor (National Educational Expert, Directorate General for Measurement, Assessment, and Examination Services, Ministry of National Education)
Year2025
Volume60
Issue3
Pages518-527
Publication date2025-05-04
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2025.2453454
PMID39935411
OpenAlexW4407417702
LanguageEN
Citations received1
References cited33

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent attributes. Longitudinal DCMs have recently been developed as psychometric models for modeling changes in examinee proficiency statuses over time. Currently, software programs for estimating longitudinal DCMs are limited in functionality and generality, expensive, or cumbersome for applied researchers. This manuscript describes and demonstrates a newly developed R package for estimating a general longitudinal DCM, the transition diagnostic classification model

Data mining · Machine learning · Programming language · R package · Statistics · Artificial Intelligence · Computer Science · Machine Learning in Healthcare · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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