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A perspective on using partial least squares structural equation modelling in data articles

Lookup NU author(s): Professor Noemi SinkovicsORCiD

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Abstract

This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.

Publication metadata

Author(s): Ringle CM, Sarstedt M, Sinkovics N, Sinkovics RR

Publication type: Article

Publication status: Published

Journal: Data in Brief

Year: 2023

Volume: 48

Print publication date: 01/06/2023

Online publication date: 21/03/2023

Acceptance date: 10/03/2023

Date deposited: 06/09/2024

ISSN (electronic): 2352-3409

Publisher: Elsevier

URL: https://doi.org/10.1016/j.dib.2023.109074

DOI: 10.1016/j.dib.2023.109074

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