A review of using partial least square structural equation modeling in e-learning research

Hung Ming Lin, Min Hsien Lee, Jyh Chong Liang, Hsin Yi Chang, Pinchi Huang, Chin Chung Tsai

Research output: Contribution to journalReview article

Abstract

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate statistical modeling technique that educational researchers frequently use. This paper reviews the uses of PLS-SEM in 16 major e-learning journals, and provides guidelines for improving the use of PLS-SEM as well as recommendations for future applications in e-learning research. A total of 53 articles using PLS-SEM published in January 2009–August 2019 are reviewed. We assess these published applications in terms of the following key criteria: reasons for using PLS-SEM, model characteristics, sample characteristics, model evaluations and reporting. Our results reveal that small sample size and nonnormal data are the first two major reasons for using PLS-SEM. Moreover, we have identified how to extend the applications of PLS-SEM in the e-learning research field.

Original languageEnglish
JournalBritish Journal of Educational Technology
DOIs
Publication statusAccepted/In press - 2019 Jan 1
Externally publishedYes

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ASJC Scopus subject areas

  • Education

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