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.
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Hwang, H., Takane, Y. (2002). Structural Equation Modeling by Extended Redundancy Analysis. In: Nishisato, S., Baba, Y., Bozdogan, H., Kanefuji, K. (eds) Measurement and Multivariate Analysis. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65955-6_12
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DOI: https://doi.org/10.1007/978-4-431-65955-6_12
Publisher Name: Springer, Tokyo
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