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

• Autores: Hung-Ming Lin, Min-Hsien Lee, Jyh‐Chong Liang, Hsin-Yi Chang, Pinchi Huang, Chin-Chung Tsai
• Localización: British journal of educational technology, ISSN 0007-1013, Vol. 51, Nº. 4, 2020, págs. 1354-1372
• Idioma: inglés
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• Dialnet Métricas: 1 Cita
• Resumen

  • 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.