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

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...

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Bibliographic Details
Published in:Data in brief 2023-06, Vol.48, p.109074-109074, Article 109074
Main Authors: Ringle, Christian M., Sarstedt, Marko, Sinkovics, Noemi, Sinkovics, Rudolf R.
Format: Article
Language:English
Subjects:
Data
Discriminant validity
HTMT
Open science
Partial least squares
PLS-SEM
Structural equation modelling
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Summary: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.
ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2023.109074