Abstract

Numerous statistical methods are available for social researchers. Therefore, knowing the appropriate technique can be a challenge. For example, when considering structural equation modelling (SEM), selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be challenging. This paper applies the same theoretical measurement and structural models and dataset to conduct a direct comparison. The findings reveal that when using CB-SEM, many indicators are removed to achieve acceptable goodness-of-fit, when compared to PLS-SEM. Also, composite reliability and convergent validity were typically higher using PLS-SEM, but other metrics such as discriminant validity and beta coefficients are comparable. Finally, when comparing variance explained in the dependent variable indicators, PLS-SEM was substantially better than CB-SEM. Updated guidelines assist researchers in determining whether CB-SEM or PLS-SEM is the most appropriate method to use.

Keywords

Structural equation modelingPartial least squares regressionGoodness of fitCovarianceStatisticsVariance (accounting)MathematicsLatent variableReliability (semiconductor)EconometricsAccountingPhysicsEconomics

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Publication Info

Year
2017
Type
article
Volume
1
Issue
2
Pages
107-107
Citations
1437
Access
Closed

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Joe F. Hair, Lucy M. Matthews, Ryan Matthews et al. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis , 1 (2) , 107-107. https://doi.org/10.1504/ijmda.2017.10008574

Identifiers

DOI
10.1504/ijmda.2017.10008574