A practical guide for quantitative researchers

One of the most frequent questions in quantitative research—especially in management and social sciences—is:

Should I use PLS-SEM or CB-SEM?

The confusion often arises because the choice is treated as a technical or software decision. In reality, the distinction between PLS-SEM and CB-SEM is conceptual, rooted in the maturity of theory and the objective of the study.

This post explains that logic clearly and simply.

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1. The core principle: theory maturity

The choice between PLS-SEM and CB-SEM depends on one central question:

How mature and well-established is the theory underlying the proposed model?

Theory maturity refers to:

• How clearly constructs are defined
• How consistently relationships have been supported
• How stable the theoretical model is across contexts

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2. When CB-SEM is appropriate

Covariance-Based Structural Equation Modeling (CB-SEM) is suitable when theory is well-developed and stable.

This typically means:

• Constructs are clearly defined and widely accepted
• Relationships are supported by a strong body of prior research
• The proposed model closely follows existing theoretical frameworks
• The research objective is theory confirmation or validation

CB-SEM focuses on:

• Reproducing the observed covariance matrix
• Evaluating overall model fit
• Assessing how well data confirm a theoretically specified model

In short, CB-SEM is appropriate when the researcher asks:

Does the data confirm the established theoretical model?

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3. When PLS-SEM is appropriate

Partial Least Squares Structural Equation Modeling (PLS-SEM) is suitable when theory is nascent, evolving, or contextually uncertain.

This includes situations where:

• Literature is limited or fragmented
• Findings across studies are inconsistent
• Constructs or relationships are newly introduced
• Existing theories are extended to new contexts
• The research objective is prediction or explanation of variance

PLS-SEM emphasizes:

• Maximizing explained variance (R²)
• Estimating complex models with limited theoretical certainty
• Supporting theory development rather than theory confirmation

PLS-SEM is appropriate when the researcher asks:

How well does the proposed model explain or predict the phenomenon?

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4. Why PLS-SEM is not a “weaker” alternative

A common misconception is that PLS-SEM is chosen because:

• Data quality is poor, or
• Theory is absent, or
• The researcher is unsure

This is incorrect.

PLS-SEM is a deliberate and legitimate choice when:

• Theoretical understanding is still evolving
• The research aims to build or extend theory
• Prediction is prioritized over exact model fit

It is not a substitute for CB-SEM—it serves a different purpose.

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5. A simple decision logic

Research Condition	Appropriate SEM Approach
Strong, established theory	CB-SEM
Stable constructs and relationships	CB-SEM
Emerging or fragmented theory	PLS-SEM
New contexts or extensions	PLS-SEM
Prediction-focused research	PLS-SEM
Model confirmation focus	CB-SEM

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6. Closing reflection

The choice between PLS-SEM and CB-SEM is not about:

• Software preference
• Sample size myths
• Mechanical decision rules

It is about epistemological alignment—matching the method to the state of theory and the purpose of inquiry.

When theory is mature, confirmation is meaningful.
When theory is evolving, explanation and prediction come first.