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Measurement Error in Nonparametric Item Response Curve Estimation IRC IRT CTT

Guo, Hongwen; Sinharay, Sandip
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
Item Response Curve (IRC), Item Response Theory (IRT), Classical Test Theory (CTT), Error of Measurement


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.

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