PLS vs. SEM: Choosing the Right Path for Your Research

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PLS vs. SEM 

Dr. Engr. Md. Abdur Rashid

Director (Research & Publication)

NAPD, Bangladesh

In the research world and data analysis, researchers often face the challenge of choosing the right structural equation modeling technique. Two popular approaches

a)     Partial Least Squares (PLS-SEM)

b)     Covariance-Based SEM (CB-SEM)

Both above may look similar at first glance, but they serve very different purposes. Understanding their distinctions is crucial for anyone working with complex models in social sciences, management, or governance.

PLS-SEM is often described as a prediction-oriented method. It thrives in situations where the researcher’s goal is to explain variance, explore new theories, or deal with formative constructs. Because it does not rely on strict assumptions of normality, PLS-SEM is flexible and can handle smaller sample sizes and highly complex models. This makes it particularly useful in early-stage theory development, exploratory studies, and contexts where practical insights are more important than perfect model fit.

On the other hand, CB-SEM is the tool of choice when the objective is theory testing and confirmation. It is rooted in covariance-based estimation, requiring larger samples and assuming multivariate normality. CB-SEM provides global fit indices such as chi-square, RMSEA (Root Mean Square Error of Approximation) , and CFI (Comparative Fit Index) that allow researchers to rigorously test whether their theoretical model holds true. This makes it ideal for mature theories, confirmatory factor analysis, and studies where precision and model validation is paramount.

Application Difference

In essence, PLS-SEM is about prediction and exploration, while CB-SEM is about theory testing and confirmation. The choice between them depends on the researcher’s goals, the nature of the constructs, and the data available. For policy innovation, procurement reform, or digital governance studies, PLS-SEM often offers the flexibility needed to uncover drivers and patterns. But when the task is to validate established frameworks or compare rival theories, CB-SEM provides the rigor required.

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