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Comparing Different Approaches of Bias Correction for Ability Estimation in IRT Models IRT MLE WLE CWLE ERF

Lee, Yi-Hsuan; Zhang, Jinming
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ETS Research Report
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Subject/Key Words:
Item Response Theory (IRT), Maximum Likelihood Estimator (MLE), Expected Response Function, Measurement-Error Modeling, Weighted Likelihood Estimator (WLE), Corrected Weighted Likelihood Estimator (CWLE)


The method of maximum-likelihood is typically applied to item response theory (IRT) models when the ability parameter is estimated while conditioning on the true item parameters. In practice, the item parameters are unknown and need to be estimated first from a calibration sample. Lewis (1985) and Zhang and Lu (2007) proposed the expected response functions (ERFs) and the corrected weighted-likelihood estimator (CWLE), respectively, to take into account the uncertainty regarding item parameters for purposes of ability estimation. In this paper, we investigate the performance of ERFs and of the CWLE in different situations, such as various test lengths and levels of measurement error in item parameter estimation. Our empirical results indicate that ERFs can cause the bias in ability estimation to fall within [-0.2, 0.2] for all conditions, whereas the CWLE can effectively reduce the bias in ability estimation provided that it has a good foundation to start from.

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