Stability of Nonparametric Methods for Cognitive Diagnostic Assessment

Cognitive diagnostic models (CDMs) are psychometric models developed to categorize individuals based on their latent attributes. These attributes, often discrete, possess scientific interpretations, such as skill mastery in educational assessments, mental disorders in psychiatric diagnoses, or the presence of disease pathogens in biological samples. Nonparametric CDMs directly classify subjects into latent profiles by minimizing the distance between observed item responses and the centroids of the latent profiles. Two widely used nonparametric methods are nonparametric classification (NPC) and general nonparametric classification (GNPC). However, existing nonparametric algorithms do not offer information regarding the variability of estimates of latent profile membership. This chapter introduces a resampling scheme employing out-of-bag observations to quantify uncertainty in estimates of attributes, latent profiles, and individuals. The proposed approach is illustrated using simulated data.