Assessing Change in Latent Skills Across Time With Longitudinal Cognitive Diagnosis Modeling: An Evaluation of Model Performance. (June 2017)

• Record Type: Journal Article
• Authors: Kaya, Yasemin; Leite, Walter L.
• Is Part Of: Educational and psychological measurement. Volume 77:Number 3(2017:Jun.)
• Page Start: 369
• Page End: 388
• Publication Date: 2017-06
• Subjects: cognitive diagnosis models -- latent transition analysis -- diagnostic classification models -- latent class analysis -- longitudinal assessment; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/0013164416659314
• Languages: English
• ISSNs: 0013-1644

Abstract

Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor attribute stability of individuals and to account for respondent dependence. Models based on combining latent transition analysis modeling and the DINA and DINO cognitive diagnosis models were developed and then evaluated through a Monte Carlo simulation study. The study results indicate that the proposed models provide adequate convergence and correct classification rates.