Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling

Schuberth, Florian; Rosseel, Yves; Rönkkö, Mikko; Trinchera, Laura; Kline, Rex B.; Henseler, Jörg

http://hdl.handle.net/10362/145720

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
Structural_Parameters_under_Partial_Least_Squares_Covariance_Based_Structural_Equation_Modeling_Comment_Yuan_and_Deng_2021.pdf		1.76 MB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Resumo(s)

In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response to their article highlights that the relationship they find between PLS-SEM and CB-SEM structural parameters is not universally valid, and that consequently, tests on individual parameters in CB-SEM and PLS-SEM generally do not fulfill the same purpose.

Descrição

Schuberth, F., Rosseel, Y., Rönkkö, M., Trinchera, L., Kline, R. B., & Henseler, J. (2023). Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling: A Comment on Yuan and Deng (2021). Structural Equation Modeling-A Multidisciplinary Journal, 30(3), 339-345. https://doi.org/10.1080/10705511.2022.21 --- Funding: Jorg Henseler gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through a research grant from the Information Management Research Center—MagIC/NOVA IMS (UIDB/04152/2020).

Palavras-chave

Bias correction Factor score regression Measurement error Partial least squares structural equation modeling (PLS-SEM) General Decision Sciences Modelling and Simulation Sociology and Political Science Economics, Econometrics and Finance(all)