Reliably Assessing Growth with Longitudinal Diagnostic Classification Models. Issue 2 (25th January 2019)

• Record Type: Journal Article
• Authors: Madison, Matthew J.
• Is Part Of: Educational measurement. Volume 38:Issue 2(2019)
• Page Start: 68
• Page End: 78
• Publication Date: 2019-01-25
• Subjects: attribute mastery transition -- cognitive diagnosis model -- consistency -- diagnostic classification model -- growth -- longitudinal item response theory -- multidimensional -- reliability; Educational tests and measurements -- Periodicals; Educational tests and measurements -- United States -- Periodicals; 371.262
• DOI: 10.1111/emip.12243
• Languages: English
• ISSNs: 0731-1745

Abstract

Abstract: Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This 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. A discussion describes how longitudinal DCMs can be used as practical and reliable psychometric models when categorical and criterion‐referenced interpretations of growth are desired.