Document Type
Article
Publication Date
2015
Keywords
measurement invariance, factorial invariance, multilevel factor mixture model, multilevel MIMIC
Digital Object Identifier (DOI)
http://dx.doi.org/10.1080/10705511.2014.938217
Abstract
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.
Rights Information
Citation / Publisher Attribution
Structural Equation Modeling, v. 22, issue 4, p. 603-616.
Scholar Commons Citation
Kim, Eun Sook; Yoon, Myeongsun; Wen, Yao; Luo, Wen; and Kwok, Oi-man, "Within-level Group Factorial Invariance with Multilevel Data: Multilevel Factor Mixture and Multilevel MIMIC Models" (2015). Educational Measurement and Research Faculty Publications. 3.
https://digitalcommons.usf.edu/edq_facpub/3
