PLS‐SEM: Prediction‐oriented solutions for HRD researchers

• Published in: Human resource development quarterly Vol. 34; no. 1; pp. 91 - 109
• Main Authors: Legate, Amanda E., Hair, Joe F., Chretien, Janice Lambert, Risher, Jeffrey J.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA Wiley Periodicals, Inc 01.03.2023; Wiley Periodicals Inc
• Subjects: confirmatory composite analysis; Guidelines; human resource development; Human resource management; Least Squares Statistics; partial least squares; PLS‐SEM; Statistical Analysis; Structural equation modeling; Structural Equation Models; variance‐based SEM
• ISSN: 1044-8004; 1532-1096
• DOI: 10.1002/hrdq.21466

Structural equation modeling, often referred to as SEM, is a well‐established, covariance‐based multivariate method used in Human Resource Development (HRD) quantitative research. In some research contexts, however, the rigorous assumptions associated with covariance‐based SEM (CB‐SEM) limit applications of the method. An emergent complementary SEM approach, partial least squares structural equation modeling (PLS‐SEM), is a variance‐based SEM method that provides valid solutions and overcomes several limitations associated with CB‐SEM. Despite PLS‐SEM's increasing popularity in many social sciences disciplines, the method has yet to gain traction in the field of HRD. An accessible overview of the method, including potential advantages for HRD research and extant methodological advancements, is provided in this article with the goal of encouraging productive dialogue in the field of HRD surrounding the PLS‐SEM approach. We present an emergent analytical tool for quantitative HRD research, offer practical guidelines for researchers to consider when selecting a SEM method, and clarify assessment stages and up‐to‐date evaluation criteria through an illustrative example.