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Vol.10 No.5
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A Study on Factor Analytical Methods and Procedures for PLS-SEM (Partial Least Squares Structural Equation Modeling)

The Journal of Industrial Distribution & Business / The Journal of Industrial Distribution & Business, (E)2233-5382
2019, v.10 no.5, pp.7-20
https://doi.org/https://doi.org/10.13106/ijidb.2019.vol10.no5.7.
YIM, Myung-Seong
YIM,, M. (2019). A Study on Factor Analytical Methods and Procedures for PLS-SEM (Partial Least Squares Structural Equation Modeling). The Journal of Industrial Distribution & Business, 10(5), 7-20, https://doi.org/https://doi.org/10.13106/ijidb.2019.vol10.no5.7.
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Abstract

Purpose - This study provides appropriate procedures for EFA to help researchers conduct empirical studies by using PLS-SEM. Research design, data, and methodology - This study addresses the absolute and relative sample size criteria, sampling adequacy, factor extraction models, factor rotation methods, the criterion for the number of factors to retain, interpretation of results, and reporting information. Results - The factor analysis procedure for PLS-SEM consists of the following five stages. First, it is important to look at whether both the Bartlett test of sphericity and the KMO MSA meet the qualitative criteria. Second, PAF is a better choice of methodology. Third, an oblique technique is a suitable method for PLS-SEM. Fourth, a combined approach is strongly recommended to factor retention. PA should be used at the onset. Next, it is recommended using the K1 criterion. In addition, it is necessary to extract factors that increase the total variance explanatory power through the PVA-FS. Finally, it is appropriate to select an item with a factor loading into 0.5 or higher and a communality of 0.5. Conclusions - It is expected that the accurate factor analysis processed for PLS-SEM as previously presented will help us extract more precise factors of the structural model.

keywords
Factor Analysis, Exploratory Factor Analysis, Principal Component Analysis, Partial Least Squares, Structural Equation Modeling, Research Methodology

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