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The Impact of Ignoring a Level of Nesting Structure in Multilevel Mixture Model

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  • Qi Chen

Abstract

Mixture modeling has gained more attention among practitioners and statisticians in recent years. However, when researchers analyze their data using finite mixture model (FMM), some may assume that the units are independent of each other even though it may not always be the case. This article used simulation studies to examine the impact of ignoring a higher nesting structure in multilevel mixture models. Results indicate that the misspecification results in lower classification accuracy of individuals, less accurate fixed effect estimates, inflation of lower level variance estimates, and less accurate standard error estimates in each subpopulation, the latter result of which in turn affects the accuracy of tests of significance for the fixed effects. The magnitude of the intraclass correlation (ICC) coefficient has a substantial impact. The implication for applied researchers is that it is important to model the multilevel data structure in mixture modeling.

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  • Qi Chen, 2012. "The Impact of Ignoring a Level of Nesting Structure in Multilevel Mixture Model," SAGE Open, , vol. 2(1), pages 21582440124, January.
    • Handle: RePEc:sae:sagope:v:2:y:2012:i:1:p:2158244012442518
    DOI: 10.1177/2158244012442518
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    References listed on IDEAS

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    1. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
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    2. Kara Misto, 2019. "Family Perceptions of Family Nursing in a Magnet Institution During Acute Hospitalizations of Older Adult Patients," Clinical Nursing Research, , vol. 28(5), pages 548-566, June.

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