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关于在数据文章中使用偏最小二乘结构方程模型的观点。

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

作者信息

Ringle Christian M, Sarstedt Marko, Sinkovics Noemi, Sinkovics Rudolf R

机构信息

Hamburg University of Technology, Department of Management Sciences and Technology, Hamburg, Germany.

Ludwig-Maximilians-University Munich, Germany.

出版信息

Data Brief. 2023 Mar 21;48:109074. doi: 10.1016/j.dib.2023.109074. eCollection 2023 Jun.

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.

摘要

这篇关于使用偏最小二乘结构方程模型(PLS-SEM)的观点文章旨在为希望将可通过此方法分析的数据集作为独立数据文章发表的作者提供指导。独立数据文章与辅助数据文章不同,因为它们不与在其他期刊上发表的完整研究文章相关联。然而,独立数据文章的作者将被要求清楚地证明并说明其数据集的有用性。这篇观点文章提供了关于概念化阶段、适用于PLS-SEM的数据类型以及报告质量标准的可行建议,这些建议通常适用于使用PLS-SEM的研究。我们还提出了用于区分效度检验的HTMT指标的调整版本,拓宽了其适用性。此外,我们强调了将数据文章与已发表的采用PLS-SEM方法的研究论文相联系的好处。

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