Applying a Mixture Rasch Model-Based Approach to Standard Setting

The subjective aspect of standard-setting is often criticized, yet data-driven standard-setting methods are rarely applied. Therefore, we applied a mixture Rasch model approach to setting performance standards across several testing programs of various sizes and compared the results to existing passing standards derived from traditional standard-setting methods. We found that heterogeneity of the sample is clearly necessary for the mixture Rasch model approach to standard setting to be useful. While possibly not sufficient to determine passing standards on their own, there may be value in these data-driven models for providing additional validity evidence to support decision-making bodies entrusted with establishing cut scores. They may also provide a useful tool for evaluating existing cut scores and determining if they continue to be supported or if a new study is warranted.