Printer Friendly

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

To read the full text of this article, click here:

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.)

COPYRIGHT 2010 U.S. Department of Education
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2010 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Guo, Hongwen; Sinharay, Sandip
Publication:ERIC: Reports
Article Type:Abstract
Geographic Code:1USA
Date:Apr 3, 2010
Previous Article:Content and Language Integrated Learning in Higher Technical Education Using the "InGenio" Online Multimedia Authoring Tool.
Next Article:Practical Use of ICT in Science and Mathematics Teachers' Training at Dar es Salaam University College of Education: An Analysis of Prospective...

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters