### Abstract

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 language | English (US) |
---|---|

Pages (from-to) | 1-12 |

Number of pages | 12 |

Journal | Structural Equation Modeling |

DOIs | |

State | Accepted/In press - Apr 13 2016 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

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

### Cite this

*Structural Equation Modeling*, 1-12. https://doi.org/10.1080/10705511.2016.1154793

**Regularized Structural Equation Modeling.** / Jacobucci, Ross; Grimm, Kevin; McArdle, John J.

Research output: Contribution to journal › Article

*Structural Equation Modeling*, pp. 1-12. https://doi.org/10.1080/10705511.2016.1154793

}

TY - JOUR

T1 - Regularized Structural Equation Modeling

AU - Jacobucci, Ross

AU - Grimm, Kevin

AU - McArdle, John J.

PY - 2016/4/13

Y1 - 2016/4/13

N2 - 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.

AB - 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.

KW - factor analysis

KW - lasso

KW - penalization

KW - regularization

KW - ridge

KW - shrinkage

KW - structural equation modeling

UR - http://www.scopus.com/inward/record.url?scp=84963576551&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963576551&partnerID=8YFLogxK

U2 - 10.1080/10705511.2016.1154793

DO - 10.1080/10705511.2016.1154793

M3 - Article

AN - SCOPUS:84963576551

SP - 1

EP - 12

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

ER -