Comparison of Parametric and Nonparametric Bootstrap Methods for Estimating Random Error in Equipercentile Equating

Autor/inn/en	Cui, Zhongmin; Kolen, Michael J.
Titel	Comparison of Parametric and Nonparametric Bootstrap Methods for Estimating Random Error in Equipercentile Equating
Quelle	In: Applied Psychological Measurement, 32 (2008) 4, S.334-347 (14 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621607300854
Schlagwörter	Test Length; Test Content; Simulation; Computation; Nonparametric Statistics; Test Items; Error of Measurement; Comparative Analysis; Scores + Suchen Sie Ihr Suchwort? Testaufgabe; Simulation program; Simulationsprogramm; Test content; Messfehler
Abstract	This article considers two methods of estimating standard errors of equipercentile equating: the parametric bootstrap method and the nonparametric bootstrap method. Using a simulation study, these two methods are compared under three sample sizes (300, 1,000, and 3,000), for two test content areas (the Iowa Tests of Basic Skills Maps and Diagrams and the ACT English), for two test lengths (24 items and 75 items), and for different parametric models (polynomial log-linear models with fitted degrees of C =2 through 10). One thousand bootstrap samples were used to estimate standard errors of equating. The parametric bootstrap method was found to estimate standard errors of equating more accurately than the nonparametric bootstrap method in most of the situations examined. (Contains 5 tables and 6 figures.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10