Literaturnachweis - Detailanzeige
Autor/inn/en | Zhan, Peida; Jiao, Hong; Liao, Dandan; Li, Feiming |
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Titel | A Longitudinal Higher-Order Diagnostic Classification Model |
Quelle | In: Journal of Educational and Behavioral Statistics, 44 (2019) 3, S.251-281 (31 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 1076-9986 |
DOI | 10.3102/1076998619827593 |
Schlagwörter | Classification; Growth Models; Educational Diagnosis; Item Response Theory; Achievement Tests; Physics; Science Tests; Foreign Countries; Grade 8; China Classification system; Klassifikation; Klassifikationssystem; Pedagogical diagnostics; Pädagogische Diagnostik; Item-Response-Theorie; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Physik; Ausland; School year 08; 8. Schuljahr; Schuljahr 08 |
Abstract | Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach. (As Provided). |
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Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |