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

Christian M Ringle1, Marko Sarstedt2,3, Noemi Sinkovics4

  • 1Hamburg University of Technology, Department of Management Sciences and Technology, Hamburg, Germany.

Data in Brief
|April 17, 2023
PubMed
Summary
This summary is machine-generated.

This guide helps authors publish standalone datasets analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). It provides recommendations on data suitability, quality criteria, and validity testing for enhanced data article discoverability.

Keywords:
Discriminant validityHTMTOpen sciencePLS-SEMPartial least squaresStructural equation modelling

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Area of Science:

  • Methodology in Social Sciences
  • Data Science
  • Statistical Modeling

Background:

  • Partial Least Squares Structural Equation Modeling (PLS-SEM) is a widely used statistical technique.
  • There is a growing trend towards publishing standalone data articles.
  • Authors need clear guidelines for preparing datasets suitable for PLS-SEM analysis in data articles.

Purpose of the Study:

  • To provide a comprehensive guide for authors on preparing and publishing datasets for PLS-SEM analysis as standalone data articles.
  • To offer actionable recommendations for the conceptualization, data suitability, and quality reporting phases.
  • To introduce adjusted metrics for discriminant validity testing and highlight the benefits of linking data articles.

Main Methods:

  • The article offers a perspective and practical recommendations based on established PLS-SEM principles.
  • It discusses conceptualization, data types, and quality criteria relevant to PLS-SEM.
  • Adjusted HTMT (Heterotrait-Monotrait) ratio metrics are presented for enhanced discriminant validity assessment.

Main Results:

  • Authors can successfully publish standalone data articles if datasets are well-conceptualized and suitable for PLS-SEM.
  • Specific recommendations are provided for reporting quality criteria and ensuring data usefulness.
  • Modified HTMT metrics improve the reliability of discriminant validity testing.

Conclusions:

  • This perspective provides a roadmap for authors to create valuable standalone data articles using PLS-SEM.
  • Adhering to the recommendations enhances the quality and discoverability of published datasets.
  • Linking data articles to existing PLS-SEM research further increases their impact and utility.