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ERIC Number: EJ901434
Record Type: Journal
Publication Date: 2010
Pages: 20
Abstractor: As Provided
Reference Count: 31
ISSN: ISSN-1070-5511
The Impact of Ignoring a Level of Nesting Structure in Multilevel Growth Mixture Models: A Monte Carlo Study
Chen, Qi; Kwok, Oi-Man; Luo, Wen; Willson, Victor L.
Structural Equation Modeling: A Multidisciplinary Journal, v17 n4 p570-589 2010
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. (Contains 4 tables and 1 figure.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A