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Measurement Error in Nonparametric Item Response Curve Estimation. Research Report. ETS RR-11-28.

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Nonparametric, or kernel, estimation of item response curve (IRC) is a concern theoretically and operationally. Accuracy of this estimation, often used in item analysis in testing programs, is biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. In this study, we investigate the deconvolution kernel estimation of IRC, correcting for the measurement error in the regressor variable. Using item response theory (IRT) simulated data and some real data, we compared the traditional kernel estimation and the deconvolution estimation of IRC. Results show that in capturing important features of the IRC, the traditional kernel estimation is comparable to the deconvolution kernel estimation in item analysis. Measurement Error is appended. (Contains 3 tables and 10 figures.)

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Author:Guo, Hongwen; Sinharay, Sandip
Publication:ERIC: Reports
Article Type:Abstract
Geographic Code:1USA
Date:Apr 3, 2010
Words:193
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