A Penalized Likelihood Method for Structural Equation Modeling

Po Hsien Huang, Hung Chen, Li Jen Weng

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as a methodology for exploring the underlying relations among both observed and latent variables. Compared to the usual likelihood method, PL includes a penalty term to control the complexity of the hypothesized model. When the penalty level is appropriately chosen, the PL can yield an SEM model that balances the model goodness-of-fit and model complexity. In addition, the PL results in a sparse estimate that enhances the interpretability of the final model. The proposed method is especially useful when limited substantive knowledge is available for model specifications. The PL method can be also understood as a methodology that links the traditional SEM to the exploratory SEM (Asparouhov & Muthén in Struct Equ Model Multidiscipl J 16:397–438, 2009). An expectation-conditional maximization algorithm was developed to maximize the PL criterion. The asymptotic properties of the proposed PL were also derived. The performance of PL was evaluated through a numerical experiment, and two real data illustrations were presented to demonstrate its utility in psychological research.

Original languageEnglish
Pages (from-to)329-354
Number of pages26
JournalPsychometrika
Volume82
Issue number2
DOIs
Publication statusPublished - 2017 Jun 1

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All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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