Amanda E. Legate, Joe F. Hair Jr, Janice Lambert Chretien, Jeffrey J. Risher
{"title":"PLS-SEM: Prediction-oriented solutions for HRD researchers","authors":"Amanda E. Legate, Joe F. Hair Jr, Janice Lambert Chretien, Jeffrey J. Risher","doi":"10.1002/hrdq.21466","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47803,"journal":{"name":"Human Resource Development Quarterly","volume":"34 1","pages":"91-109"},"PeriodicalIF":4.0000,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Resource Development Quarterly","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hrdq.21466","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INDUSTRIAL RELATIONS & LABOR","Score":null,"Total":0}
引用次数: 35
Abstract
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.
期刊介绍:
Human Resource Development Quarterly (HRDQ) is the first scholarly journal focused directly on the evolving field of human resource development (HRD). It provides a central focus for research on human resource development issues as well as the means for disseminating such research. HRDQ recognizes the interdisciplinary nature of the HRD field and brings together relevant research from the related fields, such as economics, education, management, sociology, and psychology. It provides an important link in the application of theory and research to HRD practice. HRDQ publishes scholarly work that addresses the theoretical foundations of HRD, HRD research, and evaluation of HRD interventions and contexts.