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Tutorial on the Use of the regsem Package in R

Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA
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Academic Editor: Alexander Robitzsch
Psych 2021, 3(4), 579-592; https://doi.org/10.3390/psych3040038
Received: 1 June 2021 / Revised: 29 September 2021 / Accepted: 30 September 2021 / Published: 5 October 2021
Sparse estimation through regularization is gaining popularity in psychological research. Such techniques penalize the complexity of the model and could perform variable/path selection in an automatic way, and thus are particularly useful in models that have small parameter-to-sample-size ratios. This paper gives a detailed tutorial of the R package regsem, which implements regularization for structural equation models. Example R code is also provided to highlight the key arguments of implementing regularized structural equation models in this package. The tutorial ends by discussing remedies of some known drawbacks of a popular type of regularization, computational methods supported by the package that can improve the selection result, and some other practical issues such as dealing with missing data and categorical variables. View Full-Text
Keywords: regularization; structural equation modeling; latent variables; statistical learning; R regularization; structural equation modeling; latent variables; statistical learning; R
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MDPI and ACS Style

Li, X.; Jacobucci, R.; Ammerman, B.A. Tutorial on the Use of the regsem Package in R. Psych 2021, 3, 579-592. https://doi.org/10.3390/psych3040038

AMA Style

Li X, Jacobucci R, Ammerman BA. Tutorial on the Use of the regsem Package in R. Psych. 2021; 3(4):579-592. https://doi.org/10.3390/psych3040038

Chicago/Turabian Style

Li, Xiaobei, Ross Jacobucci, and Brooke A. Ammerman 2021. "Tutorial on the Use of the regsem Package in R" Psych 3, no. 4: 579-592. https://doi.org/10.3390/psych3040038

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