A Review of Using Partial Least Square Structural Equation Modeling in E-Learning Research

Autor/inn/en	Lin, Hung-Ming; Lee, Min-Hsien; Liang, Jyh-Chong; Chang, Hsin-Yi; Huang, Pinchi; Tsai, Chin-Chung
Titel	A Review of Using Partial Least Square Structural Equation Modeling in E-Learning Research
Quelle	In: British Journal of Educational Technology, 51 (2020) 4, S.1354-1372 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lin, Hung-Ming) ORCID (Tsai, Chin-Chung)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12890
Schlagwörter	Least Squares Statistics; Structural Equation Models; Electronic Learning; Educational Research; Multivariate Analysis; Sample Size + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Multivariate Analyse
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01