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

Heungsun Hwang () and Gyeongcheol Cho
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Heungsun Hwang: McGill University
Gyeongcheol Cho: McGill University

Psychometrika, 2020, vol. 85, issue 4, No 7, 947-972

Abstract: 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 modeling; full-information; single optimization criterion; alternating least squares; block-wise generalized structured component analysis; component-based structural equation modeling; regularized generalized canonical correlation analysis; Lohmöller’s algorithm; Wold’s algorithm (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11336-020-09733-2

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