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. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 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.
• 2 Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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