Abstract

In this article an item response model is introduced for repeated ratings of student work, which we have called the Rater Bundle Model (RBM). Development of this model was motivated by the observation that when repeated ratings occur, the assumption of conditional independence is violated, and hence current state-of-the-art item response models, such as the rater facets model, that ignore this violation, underestimate measurement error, and overestimate reliability. In the rater bundle model these dependencies are explicitly parameterized. The model is applied to both real and simulated data to illustrate the approach.