A review of using partial least square structural equation modeling in e-learning research |
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| Authors: | Hung-Ming Lin Min-Hsien Lee Jyh-Chong Liang Hsin-Yi Chang Pinchi Huang Chin-Chung Tsai |
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| Institution: | Address for correspondence: Chin-Chung Tsai, Program of Learning Sciences and Institute for Research Excellence in Learning Sciences, National Taiwan Normal Unversity, #162, Section 1, Heping E. Rd., Taipei City 106, Taiwan. Email: tsaicc@ntnu.edu.tw |
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| 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. |
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| Keywords: | e-learning Mobile learning Quantitative Analysis |
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