Weighted Likelihood Estimation of Ability in Item Response Theory

1989/09/01 by Thomas A. Warm · 12 citations
Decision Sciences · Computer Science · Mathematics · #Psychometric Methodologies and Testing #Advanced Statistical Modeling Techniques #Statistical Methods and Bayesian Inference

paper · doi:10.1007/bf02294627

Abstract

Applications of item response theory, which depend upon its parameter invariance property, require that parameter estimates be unbiased. A new method, weighted likelihood estimation (WLE), is derived, and proved to be less biased than maximum likelihood estimation (MLE) with the same asymptotic variance and normal distribution. WLE removes the first order bias term from MLE. Two Monte Carlo studies compare WLE with MLE and Bayesian modal estimation (BME) of ability in conventional tests and tailored tests, assuming the item parameters are known constants. The Monte Carlo studies favor WLE over MLE and BME on several criteria over a wide range of the ability scale.

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