Theory-Guided Exploration With Structural Equation Model Forests
Theory-Guided Exploration With Structural Equation Model Forests
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DOI：
10.1037/met0000090
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发表时间：
2016-12-01
影响因子：
7
通讯作者：
Lindenberger, Ulman
中科院分区：
文献类型：
--
作者：
Brandmaier, Andreas M.;Prindle, John J.;Lindenberger, Ulman
结构方程模型（SEM）树是结构方程模型和决策树的结合，被提出作为一种数据分析工具，用于理论指导下的经验数据探索。对于多元结果的假设模型，这样的树递归地找到具有相似观察数据模式的子组。扫描电镜树允许在特定的理论模型中自动选择预测个体差异的变量，例如，潜在因素概况或发展轨迹的差异。然而，当数据的微小变化可能导致不同的树时，SEM树是不稳定的。作为补救措施，SEM森林，即基于原始数据集的重新采样的SEM树的集合，提供了更高的稳定性。由于大型森林不太适合目视检查和解释，综合措施为研究人员提供了如何改进其模型的提示：(a)变量重要性基于单个树木的袋外样本的随机排列，并对每个变量量化模型预测分布的不确定性的平均减少；(b)案例接近性使研究人员能够进行聚类和异常值检测。我们提供了SEM森林的概述，并说明了他们在智力和情景记忆的横截面因素模型的背景下的效用。我们讨论了益处和局限性，并就如何以及何时在未来的研究中使用SEM树和森林提供了建议。
Structural equation model (SEM) trees, a combination of SEMs and decision trees, have been proposed as a data-analytic tool for theory-guided exploration of empirical data. With respect to a hypothesized model of multivariate outcomes, such trees recursively find subgroups with similar patterns of observed data. SEM trees allow for the automatic selection of variables that predict differences across individuals in specific theoretical models, for instance, differences in latent factor profiles or developmental trajectories. However, SEM trees are unstable when small variations in the data can result in different trees. As a remedy, SEM forests, which are ensembles of SEM trees based on resamplings of the original dataset, provide increased stability. Because large forests are less suitable for visual inspection and interpretation, aggregate measures provide researchers with hints on how to improve their models: (a) variable importance is based on random permutations of the out-of-bag (OOB) samples of the individual trees and quantifies, for each variable, the average reduction of uncertainty about the model-predicted distribution; and (b) case proximity enables researchers to perform clustering and outlier detection. We provide an overview of SEM forests and illustrate their utility in the context of cross-sectional factor models of intelligence and episodic memory. We discuss benefits and limitations, and provide advice on how and when to use SEM trees and forests in future research.