Reliably Assessing Growth with Longitudinal Diagnostic Classification Models
Reliably Assessing Growth with Longitudinal Diagnostic Classification Models is a scholarly work, published in 2019 in ''Educational Measurement''. The main subjects of the publication include secure communication, semiconductor device reliability, longitudinal study, psychology, computer science, measurement invariance, categorical variable, and panel data. The study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes: (1) to define and evaluate two reliability measures to be used in the application of longitudinal DCMs; (2) through simulation, demonstrate that longitudinal DCM growth estimates have increased reliability compared to longitudinal item response theory models; and (3) through an empirical analysis, illustrate the practical and interpretive benefits of longitudinal DCMs.