On a Hierarchical Rater Model with Ordered Perceptions

This study advances hierarchical rater modeling by relaxing the common assumption of equal discrimination across latent classes in constructed-response (CR) scoring. Building on univariate signal detection approaches, we propose a hierarchical model with ordered perceptual distributions, allowing rater discrimination to vary across score categories within an item. Through simulation studies, we evaluate parameter recovery, compare performance with equal-discrimination models, and assess how fit indices can identify the correct model. An empirical application to a financial assessment dataset illustrates practical utility. Diagnostic outputs, including tables and visualizations, demonstrate how the model can inform rubric refinement and support targeted rater training in multi-item CR assessments.