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

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  • Heungsun Hwang

    (McGill University)

  • Gyeongcheol Cho

    (McGill University)

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.

Suggested Citation

  • Heungsun Hwang & Gyeongcheol Cho, 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 947-972, December.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:4:d:10.1007_s11336-020-09733-2
    DOI: 10.1007/s11336-020-09733-2
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    References listed on IDEAS

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    13. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
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    2. Joon-ho Kim & Kwang-jin Kim & Bum-tae Park & Hyun-ju Choi, 2022. "The Phenomenon and Development of K-Pop: The Relationship between Success Factors of K-Pop and the National Image, Social Network Service Citizenship Behavior, and Tourist Behavioral Intention," Sustainability, MDPI, vol. 14(6), pages 1-30, March.
    3. Gyeongcheol Cho & Heungsun Hwang & Marko Sarstedt & Christian M. Ringle, 2020. "Cutoff criteria for overall model fit indexes in generalized structured component analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 189-202, December.
    4. Basco, Rodrigo & Hair, Joseph F. & Ringle, Christian M. & Sarstedt, Marko, 2022. "Advancing family business research through modeling nonlinear relationships: Comparing PLS-SEM and multiple regression," Journal of Family Business Strategy, Elsevier, vol. 13(3).
    5. Nguyen, Nguyen-Hong & Nguyen, Luan-Thanh, 2023. "The impact of online shopping motivation on customer loyalty in Mobile Applications," MPRA Paper 119657, University Library of Munich, Germany, revised 02 Jan 2024.
    6. Gyeongcheol Cho & Christopher Schlaegel & Heungsun Hwang & Younyoung Choi & Marko Sarstedt & Christian M. Ringle, 2022. "Integrated Generalized Structured Component Analysis: On the Use of Model Fit Criteria in International Management Research," Management International Review, Springer, vol. 62(4), pages 569-609, August.

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