Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes

Abstract

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.