Cluster Analysis for Cognitive Diagnosis: Theory and Applications

Autor/inn/en	Chiu, Chia-Yi; Douglas, Jeffrey A.; Li, Xiaodong
Titel	Cluster Analysis for Cognitive Diagnosis: Theory and Applications
Quelle	In: Psychometrika, 74 (2009) 4, S.633-665 (33 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-009-9125-0
Schlagwörter	Test Length; Identification; Multivariate Analysis; Item Response Theory; Models; Theories; Matrices + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; Multivariate Analyse; Item-Response-Theorie; Analogiemodell; Theory; Theorie; Matrizenrechnung
Abstract	Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative approach that does not require specifying an item response model, but does require an item-by-attribute matrix. After summarizing the data with a particular vector of sum-scores, "K"-means cluster analysis or hierarchical agglomerative cluster analysis can be applied with the purpose of clustering subjects who possess the same skills. Asymptotic classification accuracy results are given, along with simulations comparing effects of test length and method of clustering. An application to a language examination is provided to illustrate how the methods can be implemented in practice. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10