Structural Equation Modeling by Extended Redundancy Analysis

Heungsun Hwang and Yoshio Takane
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Heungsun Hwang: McGill University, Department of Psychology
Yoshio Takane: McGill University, Department of Psychology

A chapter in Measurement and Multivariate Analysis, 2002, pp 115-124 from Springer

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.

Keywords: Gross Domestic Product; Structural Equation Modeling; Infant Mortality Rate; Data Transformation; Maternal Mortality Ratio (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-65955-6_12

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DOI: 10.1007/978-4-431-65955-6_12

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