Introduction
If you’re conducting research using Partial Least Squares Structural Equation Modeling (PLS-SEM), you’ve probably encountered the term Higher-Order Construct (HOC). For many researchers, this is one of the most confusing concepts when building a structural model.
The good news is that once you understand the logic behind higher-order constructs, they become much easier to work with.
This article explains what higher-order constructs are, why researchers use them, and how they are modeled in SmartPLS.
What Is a Higher-Order Construct?
A higher-order construct is a latent variable that is represented by two or more lower-order constructs.
Instead of being measured directly by questionnaire items, it is measured through several dimensions, where each dimension is itself a latent construct measured by multiple indicators.
Think of it as a hierarchy.
For example:
Customer Experience
- Service Quality
- Product Quality
- Website Experience
- Customer Support
Each of these dimensions has its own survey items. Together, they represent the broader concept of Customer Experience.
Why Do Researchers Use Higher-Order Constructs?
Many concepts in management, marketing, psychology, and social sciences are multidimensional.
For example:
- Brand Equity
- Customer Engagement
- Organizational Performance
- Service Quality
- Employee Well-being
- Innovation Capability
Representing these concepts as a single construct may ignore their complexity.
A higher-order construct allows researchers to capture the complete picture while keeping the structural model organized and theoretically meaningful.
Lower-Order Constructs vs Higher-Order Constructs
A Lower-Order Construct (LOC) is measured directly using questionnaire items.
Example:
Service Quality
- SQ1
- SQ2
- SQ3
- SQ4
Product Quality
- PQ1
- PQ2
- PQ3
- PQ4
These dimensions together form a Higher-Order Construct such as Customer Experience.
Types of Higher-Order Constructs
Researchers generally encounter four common measurement types.
Reflective–Reflective
The higher-order construct reflects the lower-order constructs, and each lower-order construct reflects its indicators.
This is one of the most common approaches in marketing research.
Reflective–Formative
The lower-order constructs collectively form the higher-order construct.
This approach is common when each dimension contributes a unique aspect of the overall concept.
Formative–Reflective
The higher-order construct is formed by dimensions, while the lower-order constructs are measured reflectively.
Although less common, it appears in certain theoretical models.
Formative–Formative
Both the higher-order construct and the lower-order constructs are formative.
This approach is relatively rare and requires strong theoretical justification.
When Should You Use a Higher-Order
Construct?
A higher-order construct is appropriate when:
- Your theory clearly states that a concept has multiple dimensions.
- Each dimension represents a distinct aspect of the concept.
- The dimensions work together to explain a broader phenomenon.
- Modeling each dimension separately would make your structural model unnecessarily complex.
Avoid using higher-order constructs simply to reduce the number of variables.
Theory—not software convenience—should always guide your decision.
Higher-Order Constructs in SmartPLS
SmartPLS provides several approaches for estimating higher-order constructs.
The most commonly used methods include:
- Repeated Indicators Approach
- Two-Stage Approach
- Hybrid Approach
The most appropriate method depends on your measurement model and research design.
Common Mistakes Researchers Make
Some frequent mistakes include:
- Creating a higher-order construct without theoretical support.
- Confusing reflective and formative measurement.
- Ignoring multicollinearity among dimensions.
- Failing to assess reliability and validity before estimating the higher-order construct.
- Choosing an estimation approach without understanding its assumptions.
These mistakes can affect the validity and interpretation of your results.
Practical Example
Imagine you are studying Influencer Authenticity.
Instead of measuring authenticity with a single scale, you may conceptualize it using several dimensions, such as:
- Expertise
- Truthful Endorsement
- Visibility
- Uniqueness
- Sincerity
Each dimension contains multiple questionnaire items.
Together, these dimensions represent the broader construct of Influencer Authenticity.
This is a typical example of a higher-order construct in marketing research.
Advantages of Higher-Order Constructs
Using higher-order constructs offers several benefits:
- Represents complex concepts more accurately.
- Reduces model complexity.
- Improves theoretical clarity.
- Makes structural models easier to interpret.
- Aligns statistical modeling with conceptual frameworks.
Final Thoughts
Higher-order constructs are an important feature of PLS-SEM because they allow researchers to model complex, multidimensional concepts in a theoretically meaningful way.
The key is to let theory drive the measurement model. Before creating a higher-order construct, ask whether the dimensions genuinely represent different aspects of the same overarching concept. If they do, a higher-order construct can provide a more accurate and insightful representation of your research model.
As you continue learning SmartPLS, mastering higher-order constructs will strengthen both your measurement models and your overall research quality.
Frequently Asked Questions
What is a higher-order construct in SmartPLS?
A higher-order construct is a latent variable represented by multiple lower-order constructs rather than being measured directly by indicators.
What is the difference between higher-order and lower-order constructs?
Lower-order constructs are measured directly using indicators, whereas higher-order constructs summarize multiple lower-order constructs into a broader concept.
When should I use a higher-order construct?
Use one when theory clearly states that a concept consists of multiple dimensions that together represent a broader construct.
Which method is best for estimating higher-order constructs in SmartPLS?
The choice depends on your measurement model. Common approaches include the Repeated Indicators, Two-Stage, and Hybrid approaches.
Suggested Further Reading
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM).
- Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM.
- Sarstedt, M., Hair, J. F., Ringle, C. M., et al. Publications on PLS-SEM and hierarchical component models.