A review of using partial least square structural equation modeling in e‐learning research. (4th December 2019)

• Record Type: Journal Article
• Authors: Lin, Hung‐Ming; Lee, Min‐Hsien; Liang, Jyh‐Chong; Chang, Hsin‐Yi; Huang, Pinchi; Tsai, Chin‐Chung
• Is Part Of: British journal of educational technology. Volume 51:Number 4(2020:Jul.)
• Page Start: 1354
• Page End: 1372
• Publication Date: 2019-12-04
• Subjects: e‐learning -- Mobile learning -- Quantitative Analysis; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.12890
• Languages: English
• ISSNs: 0007-1013

Abstract

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.