Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes
Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes
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DOI：
10.1037/1082-989x.8.3.338
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发表时间：
2003-09-01
影响因子：
7
通讯作者：
Curran, PJ
中科院分区：
文献类型：
--
作者：
Bauer, DJ;Curran, PJ
生长混合模型通常用于确定种群中是否存在遵循不同发展轨迹的亚群。然而，有限正态混合模型的统计理论表明，潜在的轨迹类可以估计，即使在人口异质性的情况下，如果重复测量的分布是非正态的。利用这一理论，本文证明了多个轨迹类可以估计，并出现最佳的非正态数据，即使只有I组存在于人口。此外，从这些模型中获得的类内参数估计值在很大程度上是不可解释的。重要的预测关系可能会被掩盖或识别出虚假的关系。这些结果的应用研究的影响被强调，并建议未来的定量发展方向。
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only I group exists in the population. Further, the within-class parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested.