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ERIC Number: ED523692
Record Type: Non-Journal
Publication Date: 2011-Jun
Pages: 28
Abstractor: As Provided
Reference Count: 19
Measurement Error in Nonparametric Item Response Curve Estimation. Research Report. ETS RR-11-28
Guo, Hongwen; Sinharay, Sandip
Educational Testing Service, Paper presented at the Annual Meeting of the National Council on Measurement in Education (NCME) (Denver, CO, Apr 29-May 3, 2010)
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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Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: Educational Testing Service