Comparing the Recovery of Latent Class Membership Under Two Constrints in the Mixture Rasch Modeling

Abstract

Mixture IRT modeling allows the detection of latent classes and different item parameter profile patterns across latent classes. In Rasch mixture model estimation, latent classes are assumed to follow a normal distribution with means constrained to be equal across latent classes for the model identification purpose. In the literature, this conventional constraint was shown to be problematic in establishing a common scale and comparing item profile patterns across different latent classes. In this study, a simulation study was conducted to explore the degree of recovery of class membership. Also, the class membership recovery of the conventional constraint approach was compared to the class-invariant item constraint approach. The results show that the recovery of class membership has the similar recovery for both approaches. In addition, the consistency of class membership for two approaches is consistent with each other.