TYPES OF STRUCTURAL EQUATION MODELING ANALYSIS
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Structural Equation Modeling (SEM) is an advanced statistical analysis technique playing a crucial role in contemporary quantitative research due to its capability to examine complex causal relationships between latent and observed variables, test and develop theories, manage measurement error, and analyze models with intricate structures. This academic article presents an overview of the two primary types of SEM analysis: Covariance-Based Structural Equation Modeling (CB-SEM), which emphasizes theory testing and confirmation by assessing the congruence between the model and empirical data, and Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM offers greater flexibility, focuses on prediction and explaining variance, and is suitable for exploratory research, new model development, or when data do not adhere to the stringent prerequisite assumptions of CB-SEM. The paper elaborates on the fundamental concepts, characteristics, limitations, selection criteria, and research application contexts for each type, serving as a comparative reference and guide for academics to effectively select the SEM technique that appropriately aligns with their research objectives, data characteristics, and overall research goals.
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