The Impact of Ignoring a Level of Nesting Structure in Multilevel Growth Mixture Models: A Monte Carlo Study
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Growth mixture modeling (GMM) is a relatively new technique for analyzing longitudinal data. However, when applying GMM, researchers might assume that the higher level (nonrepeated measure) units (e.g., students) are independent from each other even though it might not always be true. This article reports the results of a simulation study examining the impact of ignoring a higher level nesting structure in multilevel GMM. Three-level longitudinal clustered data were generated and then analyzed with the correct 3-level model and the incorrect model that ignored the highest level of nesting structure separately. The simulation results showed that although over 90% of the replications resulted in the correct class solution under both true and misspecified models, ignoring a higher level nesting structure could still result in lower classification accuracy, overestimated lower level variance components, and biased standard errors. The biased standard errors in turn affect the tests of significance for the fixed effects. Taylor & Francis Group, LLC.
Structural Equation Modeling: A Multidisciplinary Journal
author list (cited authors)
Chen, Q. i., Kwok, O., Luo, W., & Willson, V. L.
complete list of authors
Chen, Qi||Kwok, Oi-Man||Luo, Wen||Willson, Victor L