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New Developments in Psychometrics pp 133–140Cite as

On Analysis of Nonlinear Structural Equation Models

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Summary

The existing theory and its computer software in structural equation modeling are mainly established based on linear relationships among manifest variables and latent variables. However, models with nonlinear relationships are often encountered in social and behavioral sciences. In this article, an EM type algorithm is developed for maximum likelihood estimation of a general nonlinear structural equation model. To avoid computation of the complicated multiple integrals involved, the E-step is completed by a Metropolis-Hastings algorithm. The M-step can be completed efficiently by simple conditional maximization. Convergence is monitored by bridging sample and standard errors estimates are obtained via Louis’s formula. The methodology is illustrated with a real example.

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Authors and Affiliations

  1. Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong

    Sik Yum Lee

  2. Medical School, Yale University, USA

    Hong Tu Zhu

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  1. Sik Yum Lee
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  2. Hong Tu Zhu
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Editor information

H. Yanai A. Okada K. Shigemasu Y. Kano J. J. Meulman

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© 2003 Springer Japan

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Lee, S.Y., Zhu, H.T. (2003). On Analysis of Nonlinear Structural Equation Models. In: Yanai, H., Okada, A., Shigemasu, K., Kano, Y., Meulman, J.J. (eds) New Developments in Psychometrics. Springer, Tokyo. https://doi.org/10.1007/978-4-431-66996-8_13

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  • DOI: https://doi.org/10.1007/978-4-431-66996-8_13

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-66998-2

  • Online ISBN: 978-4-431-66996-8

  • eBook Packages: Springer Book Archive

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