Comparison of two bias reduction techniques for the Rasch model

Abstract

This study examines the effect of two different techniques of bias reduction in the case of the fixed persons-fixed items formulation of the Rasch model. A first approach can be considered “corrective”, because it consists simply in correcting ex-post the joint maximum likelihood estimates by a factor (m-1)/m, were m represents the number of items and/or persons. A second approach, which is an application of a quite general formula for reducing the maximum likelihood estimation bias, can be considered “preventive”, because it arises from a modification of the score function. A comparative study of these two techniques was done using simulated data.