Detection of Outliers in Anchor Items Using Modified Rasch Fit Statistics. Issue 4 (2nd October 2021) - Liu, Chunyan; Jurich, Daniel; Morrison, Carol; Grabovsky, Irina
ABSTRACT: The existence of outliers in the anchor items can be detrimental to the estimation of examinee ability and undermine the validity of score interpretation across forms. However, in practice, anchor item performance can become distorted due to various reasons. This study compares the performance of modified INFIT and OUTFIT Rasch statistics with the Logit Difference approach with 0.3 and 0.5 as the predetermined cutoff values, and the Robust z statistic with 1.645 and 2.7 as the cutoff values through a simulation study by varying the sample size, proportion of outliers, item difficulty drift direction, and group difference magnitude. The results suggest that both modified INFIT and OUTFIT statistics perform very similarly and outperform the other methods in all aspects, including sensitivity of flagging outliers, specificity of flagging non-outliers, recovery of translation constant, and recovery of examinee ability in all simulated conditions.