Concept to Practice: Guideline for Choosing between Covariance-Based SEM and Variance-Based SEM (Partial Least Square: PLS-SEM) in Structural Equation Modeling
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Abstract
Structural Equation Modeling (SEM) is a quantitative analysis technique that is utilized to examine the relationship of factors to observed and unobserved variables (latent variable). Two major ways of conducting SEM are Covariance-Based SEM (CB-SEM) and Variance-Based SEM (VB-SEM), frequently referred to as Partial Least Squares (PLS). Both the approaches are based on different concepts and utilize different steps in the analysis process. This article conducts CB-SEM, through the AMOS program, and VB-SEM (PLS-SEM), through SmartPLS, using a case study of the Technology Acceptance Model (TAM), with a raw dataset provided from www.smartpls.com. The sample size is 1,190 and randomly 100 for the student version. In this case study, TAM was adapted for technical reasons with the same data and the same model. The findings reveal that CB-SEM is suitable for testing theoretical frameworks with a large dataset. In contrast, VB-SEM (PLS-SEM) is more suitable in cases with low samples or complex models. In conclusion, the article reveals the benefits and drawbacks of each methodology and provides some practical hints to help the researcher make the best choice for their work.
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