On the Estimation of Standard Errors in Cognitive Diagnosis Models

Autor/inn/en	Philipp, Michel; Strobl, Carolin; de la Torre, Jimmy; Zeileis, Achim
Titel	On the Estimation of Standard Errors in Cognitive Diagnosis Models
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 1, S.88-115 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998617719728
Schlagwörter	Computation; Error of Measurement; Models; Cognitive Measurement; Educational Assessment; Maximum Likelihood Statistics; Statistical Analysis + Suchen Sie Ihr Suchwort? Messfehler; Analogiemodell; Education; assessment; Bewertungssystem; Statistische Analyse
Abstract	Cognitive diagnosis models (CDMs) are an increasingly popular method to assess mastery or nonmastery of a set of fine-grained abilities in educational or psychological assessments. Several inference techniques are available to quantify the uncertainty of model parameter estimates, to compare different versions of CDMs, or to check model assumptions. However, they require a precise estimation of the standard errors (or the entire covariance matrix) of the model parameter estimates. In this article, it is shown analytically that the currently widely used form of calculation leads to underestimated standard errors because it only includes the item parameters but omits the parameters for the ability distribution. In a simulation study, we demonstrate that including those parameters in the computation of the covariance matrix consistently improves the quality of the standard errors. The practical importance of this finding is discussed and illustrated using a real data example. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01