Within-Level Group Factorial Invariance With Multilevel Data: Multilevel Factor Mixture and Multilevel MIMIC Models

Abstract

This study suggests 2 approaches to factorial invariance testing with multilevel data when the groups are at the within level: multilevel factor mixture model for known classes (ML FMM) and multilevel multiple indicators multiple causes model (ML MIMIC). The adequacy of the proposed approaches was investigated using Monte Carlo simulations. Additionally, the performance of different types of model selection criteria for determining factorial invariance or detecting item noninvariance was examined. Generally, both ML FMM and ML MIMIC demonstrated acceptable performance with high true positive and low false positive rates, but the performance depended on the fit statistics used for model selection under different simulation conditions.

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