A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression, very little has transferred to models with latent variables. By adding penalties to specific parameters in a structural equation model, researchers have a high level of flexibility in reducing model complexity, overcoming poor fitting models, and the creation of models that are more likely to generalize to new samples. The proposed method was evaluated through a simulation study, two illustrative examples involving a measurement model, and one empirical example involving the structural part of the model to demonstrate RegSEM’s utility.

中文翻译：

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正则化结构方程建模

提出了一种新方法，将正则化在套索和岭回归中的使用扩展到结构方程模型。该方法称为正则化结构方程建模（RegSEM）。RegSEM 惩罚结构方程模型中的特定参数，目的是创建更容易理解和更简单的模型。尽管正则化在回归中得到了广泛采用，但很少转移到具有潜在变量的模型中。通过对结构方程模型中的特定参数添加惩罚，研究人员在降低模型复杂性、克服拟合不佳的模型以及创建更可能泛化到新样本的模型方面具有高度的灵活性。通过模拟研究评估了所提出的方法，包括测量模型的两个说明性示例，