Growth mixture modeling outperform simpler clustering algorithms when detecting 
longitudinal heterogeneity, even with small sample sizes

Martin, Daniel P.; von Oertzen, Timo

doi:10.1080/10705511.2014.936340

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Growth mixture modeling outperform simpler clustering algorithms when detecting longitudinal heterogeneity, even with small sample sizes

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von Oertzen, Timo
Center for Lifespan Psychology, Max Planck Institute for Human Development, Max Planck Society;

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http://dx.doi.org/10.1080/10705511.2014.936340
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Martin, D. P., & von Oertzen, T. (2015). Growth mixture modeling outperform simpler clustering algorithms when detecting longitudinal heterogeneity, even with small sample sizes. Structural Equation Modeling, 22(2), 264-275. doi:10.1080/10705511.2014.936340.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0029-B18D-5

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