Fitting structural equation model trees and latent growth curve mixture models in longitudinal designs: The influence of model misspecification in estimating the number of classes
Fitting structural equation model trees and latent growth curve mixture models in longitudinal designs: The influence of model misspecification in estimating the number of classes
复制标题
在纵向设计中拟合结构方程模型树和潜在增长曲线混合模型：模型错误指定对估计类数的影响
DOI：
10.1080/10705511.2016.1266267
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J.J.
中科院分区：
文献类型：
--
作者：
Usami;S.;Hayes;T.;McArdle;J.J.
在进行纵向研究时，对个体内变化模式的个体间差异的调查可以提供重要的见解。在这篇文章中，我们使用模拟方法来研究基于模型的探索性数据挖掘技术的性能-结构方程模型树（SEM树; Brandmaier，Oertzen，McArdle，& Lindenberger，2013）-作为检测群体异质性的工具。我们使用一个潜在的变化得分模型作为数据生成模型，并操纵由协变量提供的关于真实潜在的配置文件以及其他因素，包括样本量，在模型错误指定的可能影响下的信息的精度。仿真结果表明，与潜在增长曲线混合模型相比，SEM树在估计类数时可能对模型误设定非常敏感。这可以归因于在识别类别时的较低统计功效，这是由于类别之间的模板模型所规定的参数差异较小。
When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed by the template model between classes.