PLS-SEM: A method demonstration in the R statistical environment
Corresponding Author
Amanda E. Legate
Department of Human Resource Development, University of Texas at Tyler, Tyler, Texas, USA
Correspondence
Amanda E. Legate, Department of Human Resource Development, University of Texas at Tyler, Tyler, TX, USA.
Email: [email protected]
Search for more papers by this authorChristian M. Ringle
Institute of Management and Decision Sciences, Hamburg University of Technology, Hamburg, Germany
Search for more papers by this authorJoseph F. Hair Jr.
Department of Marketing & Quantitative Methods, University of South Alabama, Mobile, Alabama, USA
Search for more papers by this authorCorresponding Author
Amanda E. Legate
Department of Human Resource Development, University of Texas at Tyler, Tyler, Texas, USA
Correspondence
Amanda E. Legate, Department of Human Resource Development, University of Texas at Tyler, Tyler, TX, USA.
Email: [email protected]
Search for more papers by this authorChristian M. Ringle
Institute of Management and Decision Sciences, Hamburg University of Technology, Hamburg, Germany
Search for more papers by this authorJoseph F. Hair Jr.
Department of Marketing & Quantitative Methods, University of South Alabama, Mobile, Alabama, USA
Search for more papers by this authorAbstract
In line with calls to stimulate methodological diversity and support evidence-based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open-source tools and lesser-known quantitative research methods to support the HRD research community and applied HRD in the workplace. In this paper, we provide an informative introduction to partial least squares structural equation modeling (PLS-SEM). We discuss PLS-SEM application trends in the field of HRD, present key characteristics of the method, and demonstrate up-to-date metrics and evaluation guidelines using an illustrative model. Our PLS-SEM demonstration and explanations can serve as a valuable resource for practitioners concerned with substantiating results for organizational stakeholders and support researchers in methodological decision-making while avoiding common pitfalls associated with less familiar methods. Our step-by-step demonstration is conducted in open-source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented.
Open Research
DATA AVAILABILITY STATEMENT
All underlying data, software code, and supplementary materials supporting this manuscript are available in the Open Science Framework (OSF) repository: PLS-SEM CPM in R https://osf.io/jhcx4/.
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