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Model comparison of nonlinear structural equation models with fixed covariates

Abstract

Recently, it has been recognized that the commonly used linear structural equation model is inadequate to deal with some complicated substantive theory. A new nonlinear structural equation model with fixed covariates is proposed in this article. A procedure, which utilizes the powerful path sampling for computing the Bayes factor, is developed for model comparison. In the implementation, the required random observations are simulated via a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm. It is shown that the proposed procedure is efficient and flexible; and it produces Bayesian estimates of the parameters, latent variables, and their highest posterior density intervals as by-products. Empirical performances of the proposed procedure such as sensitivity to prior inputs are illustrated by a simulation study and a real example.

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Correspondence to Sik-Yum Lee.

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This research is fully supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 4346/01H). The authors are thankful to the Editor, the Associate Editor, and anonymous reviewers for valuable comments which improve the paper significantly, and grateful to ICPSR and the relevant funding agency for allowing use of the data in the example. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.

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Lee, SY., Song, XY. Model comparison of nonlinear structural equation models with fixed covariates. Psychometrika 68, 27–47 (2003). https://doi.org/10.1007/BF02296651

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Key words

  • nonlinear structural equation model
  • model comparison
  • Bayes factor
  • path sampling
  • Gibbs sampler
  • Metropolis-Hastings algorithm
  • sensitivity