Testing Latent Mean Differences Between Observed and Unobserved Groups Using Multilevel Factor Mixture Models

When assessing latent mean differences, researchers frequently do not explore possible heterogeneity within their data sets. Sources of differences may be functions of a nested data structure or heterogeneity in the form of unobserved classes of observations defined by a difference in factor means. In this study, the use of multilevel structural equation models in combination with factor mixture models (FMMs) for assessing latent mean differences is discussed. Interpretation of single- and multilevel model parameter estimates when comparing latent means for observed and unobserved groups is demonstrated using a large-scale data set in which students are clustered within schools. Methodological dilemmas are discussed, and directions for future research with respect to multilevel FMMs are suggested.

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