Modeling Rating Order Effects Under Item Response Theory Models for Rater-Mediated Assessments

Abstract

Rater effects are commonly observed in rater-mediated assessments. By using item response theory (IRT) modeling, raters can be treated as independent factors that function as instruments for measuring ratees. Most rater effects are static and can be addressed appropriately within an IRT framework, and a few models have been developed for dynamic rater effects. Operational rating projects often require human raters to continuously and repeatedly score ratees over a certain period, imposing a burden on the cognitive processing abilities and attention spans of raters that stems from judgment fatigue and thus affects the rating quality observed during the rating period. As a result, ratees’ scores may be influenced by the order in which they are graded by raters in a rating sequence, and the rating order effect should be considered in new IRT models. In this study, two types of many-faceted (MF)-IRT models are developed to account for such dynamic rater effects, which assume that rater severity can drift systematically or stochastically. The results obtained from two simulation studies indicate that the parameters of the newly developed models can be estimated satisfactorily using Bayesian estimation and that disregarding the rating order effect produces biased model structure and ratee proficiency parameter estimations. A creativity assessment is outlined to demonstrate the application of the new models and to investigate the consequences of failing to detect the possible rating order effect in a real rater-mediated evaluation.