A Longitudinal Higher-Order Diagnostic Classification Model

Autor/inn/en	Zhan, Peida; Jiao, Hong; Liao, Dandan; Li, Feiming
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 Verfügbarkeit
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 + Suchen Sie Ihr Suchwort? 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