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ERIC Number: EJ916026
Record Type: Journal
Publication Date: 2011-Mar
Pages: 19
Abstractor: As Provided
Reference Count: 31
ISSN: ISSN-0146-6216
A Test-Length Correction to the Estimation of Extreme Proficiency Levels
Magis, David; Beland, Sebastien; Raiche, Gilles
Applied Psychological Measurement, v35 n2 p91-109 Mar 2011
In this study, the estimation of extremely large or extremely small proficiency levels, given the item parameters of a logistic item response model, is investigated. On one hand, the estimation of proficiency levels by maximum likelihood (ML), despite being asymptotically unbiased, may yield infinite estimates. On the other hand, with an appropriate prior distribution, the Bayesian approach of maximum a posteriori (MAP) yields finite estimates, but it suffers from severe estimation bias at the extremes of the proficiency scale. As a first step, a simple correction to the MAP estimator is proposed to reduce this estimation bias. The correction factor is determined through a simulation study and depends only on the length of the test. In a second step, some additional simulations emphasize that the corrected estimator behaves like the ML estimator and outperforms the standard MAP method for extremely small or extremely large abilities. Although based on the Rasch model, the method could be adapted to other logistic item response models. (Contains 2 tables and 7 figures.)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A