Partial Least Squares Structural Equation Modeling (PLS-SEM) and Covariance-Based Structural Equation Modeling (CB-SEM) are both statistical techniques used for analyzing complex relationships among variables in a structural equation modeling framework. While they share similarities, they have distinct characteristics and are suited for different scenarios.

1. PLS-SEM (Partial Least Squares Structural Equation Modeling):PLS-SEM is a method that prioritizes predictive accuracy and is often used when the focus is on understanding relationships among latent constructs and predicting dependent variables. It's particularly suitable for exploratory research or when theory development is not yet well-established. Key features of PLS-SEM include:

• Focus: PLS-SEM focuses on predicting relationships between latent constructs and observed variables.
• Assumptions: It has fewer distributional assumptions and is more robust when dealing with smaller sample sizes and non-normal data.
• Model Complexity: PLS-SEM can handle complex models, even with small sample sizes, making it useful for small or exploratory studies.
• Use Cases: PLS-SEM is often employed in situations where the primary goal is prediction or when the theoretical model is not fully developed.

2. CB-SEM (Covariance-Based Structural Equation Modeling):CB-SEM is a method that aims to test and validate a well-defined theoretical model. It is more suited for confirmatory research where researchers have a strong theoretical foundation and specific hypotheses to test. Key features of CB-SEM include:

• Focus: CB-SEM focuses on testing the fit of the model to the data and estimating the relationships between latent constructs and observed variables.
• Assumptions: CB-SEM assumes multivariate normality and requires a larger sample size for stable results.
• Model Complexity: While CB-SEM can handle complex models, it may require a larger sample size to achieve accurate parameter estimates.
• Use Cases: CB-SEM is used when researchers have a well-defined theoretical model and specific hypotheses to test. It's suitable for confirmatory studies and hypothesis testing.

When to Use PLS-SEM and CB-SEM:

In summary, PLS-SEM is better suited for predictive and exploratory research, while CB-SEM is more appropriate for confirmatory research with well-defined theoretical models and hypotheses. The choice between the two depends on the research objectives, available data, and the stage of theory development. Prince Dacosta Anaman