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Regularized Generalized Structured Component Analysis

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Abstract

Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge type of regularization into GSCA in a unified framework, thereby enabling to handle multi-collinearity problems effectively. An alternating regularized least squares algorithm is developed for parameter estimation. A Monte Carlo simulation study is conducted to investigate the performance of the proposed method as compared to its non-regularized counterpart. An application is also presented to demonstrate the empirical usefulness of the proposed method.

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Authors and Affiliations

  1. Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada

    Heungsun Hwang

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  1. Heungsun Hwang
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Correspondence to Heungsun Hwang.

Additional information

The author wishes to thank Yoshio Takane for his valuable comments on an earlier version of this paper. The author is also grateful for Claes Fornell who generously provided the ACSI data. Finally, the author is indebted to the Editor, Associate Editor, and three anonymous reviewers for their constructive comments that improved the quality and readability of the paper.

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Hwang, H. Regularized Generalized Structured Component Analysis. Psychometrika 74, 517–530 (2009). https://doi.org/10.1007/s11336-009-9119-y

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  • DOI: https://doi.org/10.1007/s11336-009-9119-y

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