Regularized Structural Equation Modeling

Ross Jacobucci, Kevin Grimm, John J. McArdle

Research output: Contribution to journalArticlepeer-review

121 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)555-566
Number of pages12
JournalStructural Equation Modeling
Issue number4
StatePublished - Jul 3 2016


  • factor analysis
  • lasso
  • penalization
  • regularization
  • ridge
  • shrinkage
  • structural equation modeling

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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