An Explicit Form with Continuous Attribute Profile of the Partial Mastery DINA Model

Autor/inn/en	Shu, Tian; Luo, Guanzhong; Luo, Zhaosheng; Yu, Xiaofeng; Guo, Xiaojun; Li, Yujun
Titel	An Explicit Form with Continuous Attribute Profile of the Partial Mastery DINA Model
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 5, S.573-602 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shu, Tian)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986231159436
Schlagwörter	Cognitive Measurement; Models; Diagnostic Tests; Accuracy; Bayesian Statistics; Markov Processes; Monte Carlo Methods; Algorithms; Error of Measurement; Mathematical Formulas + Suchen Sie Ihr Suchwort? Analogiemodell; Diagnostic test; Diagnostischer Test; Markowscher Prozess; Monte-Carlo-Methode; Algorithm; Algorithmus; Messfehler; Mathematische Formel
Abstract	Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a continuous attribute mastery format in recent literature. To obtain subjects' finer-grained attribute mastery for more precise diagnosis and guidance, an equivalent but more explicit form of the partial-mastery-deterministic inputs, noisy "and" gate (DINA) model (termed continuous attribute profile [CAP]-DINA form) is proposed in this article. Its parameters estimation algorithm based on this form using Bayesian techniques with Markov chain Monte Carlo algorithm is also presented. Two simulation studies are conducted then to explore its parameter recovery and model misspecification, and the results demonstrate that the CAP-DINA form performs robustly with satisfactory efficiency in these two aspects. A real data study of the English test also indicates it has a better model fit than DINA. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01