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Regularized Structural Equation Modeling.

Ross Jacobucci1, Kevin J Grimm2, John J McArdle1

  • 1University of Southern California.

Structural Equation Modeling : a Multidisciplinary Journal
|July 12, 2016
PubMed
Summary
This summary is machine-generated.

Regularized structural equation modeling (RegSEM) introduces penalties to simplify complex models. This new method enhances model interpretability and generalizability in structural equation modeling research.

Keywords:
factor analysislassopenalizationregularizationridgeshrinkagestructural equation modeling

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Regularization techniques like LASSO and Ridge regression are widely used in standard regression models.
  • Their application to structural equation models (SEMs), especially those involving latent variables, remains limited.
  • Model complexity and poor model fit are common challenges in SEM.

Purpose of the Study:

  • To introduce a novel method, regularized structural equation modeling (RegSEM), extending regularization to SEMs.
  • To enhance the interpretability, simplicity, and generalizability of SEMs.
  • To provide researchers with greater flexibility in model complexity reduction.

Main Methods:

  • Proposed RegSEM, which applies penalties to specific parameters within SEMs.
  • Evaluated RegSEM through a simulation study.
  • Demonstrated utility with two measurement model examples and one empirical structural model example.

Main Results:

  • RegSEM effectively penalizes specific parameters, leading to simpler and more understandable models.
  • The method shows promise in overcoming issues with poor-fitting models.
  • Demonstrated potential for creating models that generalize better to new samples.

Conclusions:

  • RegSEM offers a flexible and powerful approach to regularizing SEMs.
  • The method facilitates the development of more parsimonious and robust structural equation models.
  • RegSEM is a valuable tool for researchers working with complex statistical models involving latent variables.