Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Psychol Methods. 2013 Mar;18(1):71-86. doi: 10.1037/a0030001. Epub 2012 Sep 17.
In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model.
在行为和社会科学领域，结构方程模型（SEM）已被广泛接受为潜在变量和观测变量之间关系的建模工具。SEM 可以被视为几种多元分析技术的统一。SEM 树通过构建递归地将数据集分离成具有显著不同 SEM 参数估计的子集的树结构，将 SEM 的优势与决策树范例结合在一起。SEM 树提供了一种方法，可以找到预测观测和潜在空间中结构参数差异的协变量和协变量交互作用，并促进基于理论的实证数据探索。我们描述了该方法，讨论了理论和实际意义，并展示了对因子模型和线性增长曲线模型的应用。
