Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models

Autor/inn/en	Templin, Jonathan L.; Henson, Robert A.; Templin, Sara E.; Roussos, Louis
Titel	Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models
Quelle	In: Applied Psychological Measurement, 32 (2008) 7, S.559-574 (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621607300286
Schlagwörter	Factor Structure; Identification; Correlation; Computation; Models; Simulation; Cognitive Processes; Cognitive Measurement; Psychology + Suchen Sie Ihr Suchwort? Faktorenstruktur; Identifikation; Identifizierung; Korrelation; Analogiemodell; Simulation program; Simulationsprogramm; Cognitive process; Kognitiver Prozess; Psychologie
Abstract	Several types of parameterizations of attribute correlations in cognitive diagnosis models use the reduced reparameterized unified model. The general approach presumes an unconstrained correlation matrix with K(K - 1)/2 parameters, whereas the higher order approach postulates K parameters, imposing a unidimensional structure on the correlation matrix between the latent skills. This article investigates the differences in performance between the correlational structure parameterizations (a general structure, a higher order single-factor structure, and a baseline uniform distributional approach constraining the attributes to be independent) across a wide variety of simulated multidimensional attribute spaces. Results suggest that the correlational approaches perform equally well with respect to classification and item parameter estimation accuracy, regardless of the violations of the assumptions of the higher order model. Findings suggest the general robustness of the higher order model and the associated estimation procedure. The three approaches are also used to analyze a real-world test; results suggest that such tests can be analyzed effectively by the higher order algorithm. (Contains 3 tables and 1 note.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10