Estimating Consistency and Accuracy Indices for Multiple Classifications

Autor/inn/en	Lee, Won-Chan; Hanson, Bradley A.; Brennan, Robert L.
Titel	Estimating Consistency and Accuracy Indices for Multiple Classifications
Quelle	In: Applied Psychological Measurement, 26 (2002) 4, S.412-432 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/014662102237797
Schlagwörter	Classification; True Scores; Psychometrics; Item Response Theory; Comparative Analysis; Models; Testing + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Psychometry; Psychometrie; Item-Response-Theorie; Analogiemodell; Testdurchführung; Testen
Abstract	This article describes procedures for estimating various indices of classification consistency and accuracy for multiple category classifications using data from a single test administration. The estimates of the classification consistency and accuracy indices are compared under three different psychometric models: the two-parameter beta binomial, four-parameter beta binomial, and three-parameter logistic IRT (item response theory) models. Using real data sets, the estimation procedures are illustrated, and the characteristics of the estimated classification indices are examined. This article also examines the behavior of the estimated classification indices as a function of the latent variable. All three components of the models (i.e., the estimated true score distributions, fitted observed score distributions, and estimated conditional error variances) appear to have considerable influence on the magnitudes of the estimated classification indices. Choosing a model in practice should be based on various considerations including the degree of model fit to the data, suitability of the model assumptions, and the computational feasibility. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10