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Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling

Published online by Cambridge University Press:  01 January 2025

Heungsun Hwang Open the ORCID record for Heungsun Hwang [Opens in a new window]  and
Gyeongcheol Cho Open the ORCID record for Gyeongcheol Cho [Opens in a new window]
Heungsun Hwang*
Affiliation:
McGill University
Gyeongcheol Cho
Affiliation:
McGill University
*
Correspondence should be made to Heungsun Hwang, Department of Psychology, McGill University, 2001 McGill College Avenue, MontrealQC H3A 1G1, Canada. Email: heungsun.hwang@mcgill.ca
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Abstract

Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach remains a limited-information method that carries out two separate stages sequentially to estimate parameters (component weights, loadings, and path coefficients), indicating that it has no single optimization criterion for estimating the parameters at once. In general, limited-information methods are known to provide less efficient parameter estimates than full-information ones. To address this enduring issue, we propose a full-information method for partial least squares path modeling, termed global least squares path modeling, where a single least squares criterion is consistently minimized via a simple iterative algorithm to estimate all the parameters simultaneously. We evaluate the relative performance of the proposed method through the analyses of simulated and real data. We also show that from algorithmic perspectives, the proposed method can be seen as a block-wise special case of another full-information method for component-based structural equation modeling—generalized structured component analysis.

Keywords

partial least squares path modelingfull-informationsingle optimization criterionalternating least squaresblock-wise generalized structured component analysiscomponent-based structural equation modelingregularized generalized canonical correlation analysisLohmöller’s algorithmWold’s algorithm
Type
Theory and Methods
Information
Psychometrika , Volume 85 , Issue 4 , December 2020 , pp. 947 - 972
Copyright
Copyright © 2020 The Psychometric Society

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Footnotes

Electronic Supplementary material The online version of this article (https://doi.org/10.1007/s11336-020-09733-2) contains supplementary material, which is available to authorized users.

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