Marcoulides Katerina M, Trinchera Laura
Department of Psychology, University of Minnesota, Minneapolis, MN, United States.
NEOMA Business School, Mont-Saint-Aignan, France.
Front Psychol. 2021 Feb 26;12:618647. doi: 10.3389/fpsyg.2021.618647. eCollection 2021.
Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for determining the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered.
增长混合模型经常应用于行为科学和社会科学领域，以识别遵循不同发展轨迹的未知异质子群体。马尔库利德斯和特里切拉（2019年）最近提出了一种混合建模方法，该方法通过基于个体案例残差评估，对遵循相同估计增长轨迹的个体进行算法分组或聚类，来检验多个潜在类别的存在情况。本文的目的是进行一项模拟研究，考察这种新方法在确定增长混合模型中类别数量时的性能。在各种纵向数据设计条件下，检验该方法正确识别类别数量的性能。研究结果表明，在所考虑的所有设计条件下，新方法都是非常可靠的类别指标。
