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Multilevel regression mixture analysis

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  • Bengt Muthén
  • Tihomir Asparouhov

Abstract

Summary. A two‐level regression mixture model is discussed and contrasted with the conventional two‐level regression model. Simulated and real data shed light on the modelling alternatives. The real data analyses investigate gender differences in mathematics achievement from the US National Education Longitudinal Survey. The two‐level regression mixture analyses show that unobserved heterogeneity should not be presupposed to exist only at level 2 at the expense of level 1. Both the simulated and the real data analyses show that level 1 heterogeneity in the form of latent classes can be mistaken for level 2 heterogeneity in the form of the random effects that are used in conventional two‐level regression analysis. Because of this, mixture models have an important role to play in multilevel regression analyses. Mixture models allow heterogeneity to be investigated more fully, more correctly attributing different portions of the heterogeneity to the different levels.

Suggested Citation

  • Bengt Muthén & Tihomir Asparouhov, 2009. "Multilevel regression mixture analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 639-657, June.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:3:p:639-657
    DOI: 10.1111/j.1467-985X.2009.00589.x
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    1. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
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