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measurement invariance, factorial invariance, multilevel factor mixture model, multilevel MIMIC

Digital Object Identifier (DOI)


This study suggests two 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 in 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.

Citation / Publisher Attribution

Structural Equation Modeling, v. 22, issue 4, p. 603-616.