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Structural Equation Modeling by Extended Redundancy Analysis

In: Measurement and Multivariate Analysis

Author

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  • Heungsun Hwang

    (McGill University, Department of Psychology)

  • Yoshio Takane

    (McGill University, Department of Psychology)

Abstract

Summary A new approach to structural equation modeling, so-called extended redundancy analysis (ERA), is proposed. In ERA, latent variables are obtained as linear combinations of observed variables, and model parameters are estimated by minimizing a single least squares criterion. As such, it can avoid limitations of covariance structure analysis (e.g., stringent distributional assumptions, improper solutions, and factor score indeterminacy) in addition to those of partial least squares (e.g., the lack of a global optimization). Moreover, data transformation is readily incorporated in the method for analysis of categorical variables. An example is given for illustration.

Suggested Citation

  • Heungsun Hwang & Yoshio Takane, 2002. "Structural Equation Modeling by Extended Redundancy Analysis," Springer Books, in: Shizuhiko Nishisato & Yasumasa Baba & Hamparsum Bozdogan & Koji Kanefuji (ed.), Measurement and Multivariate Analysis, pages 115-124, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-65955-6_12
    DOI: 10.1007/978-4-431-65955-6_12
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