Nonparametric Item Response Curve Estimation with Correction for Measurement Error

Autor/inn/en	Guo, Hongwen; Sinharay, Sandip
Titel	Nonparametric Item Response Curve Estimation with Correction for Measurement Error
Quelle	In: Journal of Educational and Behavioral Statistics, 36 (2011) 6, S.755-778 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998610396891
Schlagwörter	Testing Programs; Measurement; Item Analysis; Error of Measurement; Nonparametric Statistics; Item Response Theory; Regression (Statistics); Scores; Comparative Analysis + Suchen Sie Ihr Suchwort? Messverfahren; Itemanalyse; Messfehler; Item-Response-Theorie; Regression; Regressionsanalyse
Abstract	Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally. This study investigates the deconvolution kernel estimation of IRCs, which corrects for the measurement error in the regressor variable. A comparison of the traditional kernel estimation and the deconvolution estimation of IRCs is carried out using both simulated and operational data. It is found that, in item analysis, the traditional kernel estimation is comparable to the deconvolution kernel estimation in capturing important features of the IRC. (Contains 1 note, 3 tables and 11 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10