Mastery Assessment with Latent Class and Quasi-Independence Models Representing Homogeneous Item Domains

Abstract

This article discusses mastery classification involving the use of latent class and quasi-independence models. Extensions of mastery classification techniques developed by Macready and Dayton are presented. These extensions provide decision rules for assigning individuals to latent classes in complex models involving more than two latent categories. Procedures for identifying the minimally acceptable proportion of misclassified individuals in complex latent class models are also detailed.