Literaturnachweis - Detailanzeige
| Autor/in | Madison, Matthew J. |
|---|---|
| Titel | Reliably Assessing Growth with Longitudinal Diagnostic Classification Models |
| Quelle | In: Educational Measurement: Issues and Practice, 38 (2019) 2, S.68-78 (11 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Zusatzinformation | ORCID (Madison, Matthew J.) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 0731-1745 |
| DOI | 10.1111/emip.12243 |
| Schlagwörter | Longitudinal Studies; Item Response Theory; Psychometrics; Criterion Referenced Tests; Test Reliability; Classification; Mastery Learning; Simulation; Comparative Analysis; Models |
| 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. (As Provided). |
| Anmerkungen | Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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