Market research often goes beyond measuring surface-level metrics. Businesses don’t just want to know what customers think, they want to understand why. Structural equation modeling or SEM is a statistical method designed to uncover those deeper connections.
By analyzing how multiple factors interact, SEM helps explain outcomes like loyalty, purchase intent, or satisfaction. Instead of looking at each variable in isolation, it shows how they work together.
At Drive Research, we’ve seen SEM help brands identify barriers in the customer journey, prioritize improvements, and focus strategies where they’ll have the most impact. In this guide, we’ll walk through what SEM is, when to use it, how it works, and share a case study with one of our clients.
Structural equation modeling (SEM) is a statistical framework for analyzing complex relationships between variables. Think of it as taking a messy web of customer data and turning it into a clear diagram that shows how different factors connect and influence one another.
SEM combines elements of two popular techniques you may already know:
By blending these approaches, SEM allows researchers to look at both direct and indirect effects.
For instance, customer service quality might directly influence satisfaction, but it could also indirectly influence brand loyalty by first shaping trust. SEM captures both pathways in a single model.
To make this less complex, let’s look at a practical example. Imagine a retail brand wants to understand what drives repeat purchases. So they collect survey data from customers on several factors: product quality, website usability, customer service, trust in the brand, satisfaction, and loyalty.
If they ran a simple regression, they might find that product quality and satisfaction both predict loyalty. Useful, but incomplete.
What SEM reveals is more nuanced:
This layered view gives the retailer a clearer roadmap. Instead of guessing which improvement will matter most, SEM highlights where interventions will have the biggest ripple effects.
Not every research question requires this level of advanced analytics, but when complexity is high, it’s often the best approach.
Here are a few situations where SEM shines:
For example, in employee engagement research, SEM might reveal that recognition improves morale, which then improves retention. While in higher education, it could show how campus resources influence satisfaction through perceptions of support.
By clarifying both direct and indirect effects, SEM helps organizations avoid tunnel vision and invest where it truly counts.
The process can seem intimidating at first, but it follows a logical series of steps. Here’s a closer look at how it works in practice.
We’ll also include our “pro tips” throughout each stage to highlight best practices that make the analysis more reliable and the results more actionable.
Before any data is collected, researchers outline the “constructs” they want to measure such as trust, satisfaction, loyalty, or perceived value. These are often abstract concepts that can’t be observed directly, so they’re defined through measurable survey questions.
At this stage, it’s also important to form hypotheses about how constructs connect. For instance:
Pro tip: Keep the model grounded in real business questions. Overcomplicating it with too many constructs or theoretical links can make results harder to interpret and apply.
Next, researchers design survey questions (or use existing data) that capture the constructs. Because these ideas are abstract, each one usually requires multiple items to measure.
For example, “trust” might be assessed through statements like “I believe this brand keeps its promises” or “I feel confident this company acts in my best interest.”
Questions are often asked on a 5- or 7-point rating scale. That’s because using multiple items for each construct helps ensure reliability and gives the model enough data to identify patterns.
Pro tip: Avoid relying on single questions to measure complex ideas. Multiple indicators not only provide a more accurate picture but also strengthen the validity of the model.
Once data is collected, specialized software is needed to run the analysis. Common tools include AMOS, LISREL, R (with packages like lavaan), or Mplus.
Each platform has its strengths, but the goal is the same: estimate the relationships between constructs and determine how well the model fits the data.
At Drive Research, we pair these tools with tailored survey design so the analysis aligns directly with business objectives, not just statistical theory.
Pro tip: Don’t just rely on software defaults. Fit indices like RMSEA, CFI, and Chi-square should all be reviewed to ensure the model accurately reflects the data.
This is where the math happens. The software estimates the paths (arrows) between constructs and variables, showing which connections are strong, weak, or insignificant. Researchers then test how well the data supports the proposed structure.
If the model doesn’t fit well, adjustments are made by combining similar constructs, removing weak links, or re-examining survey items.
Pro tip: Use a large enough sample size. Because SEM estimates many parameters, models are more stable and reliable with robust sample sizes (often 400+ respondents).
Once a good fit is achieved, the real value comes in interpretation.
We recommend looking at both…
This layered view shows not only what matters, but also how improvements cascade through the customer experience.
For example, if trust indirectly drives loyalty through satisfaction, the recommendation might be to invest in initiatives that build credibility and transparency.
Pro tip: Always connect results back to business strategy. A technically correct model means little unless it helps leaders prioritize actions that improve outcomes.
Drive Research partnered with a major financial services company and its advertising agency to better understand the barriers preventing customers from choosing them at different stages of the purchase funnel.
The client already knew what drove customers to select them but lacked clarity on the obstacles standing in the way.
Key objectives included:
To meet these objectives, we recommended an online survey designed to support advanced modeling.
This method was selected because of its ability to:
Respondents rated their agreement with up to 50 barrier statements depending on the funnel stage they were in. These 5-point scale ratings became the foundation for the model. The client and competitors were rotated into the design for a full market view.
The sample included U.S. consumers who had recently used or considered the client. Augments were added to ensure reliable sample sizes for key segments.
The process included several key steps:
The survey collected responses from 4,011 consumers, resulting in a margin of error of just ±1.5%. Three models were created to reflect different stages of the purchase funnel, each highlighting the barrier themes most impactful at that point.
The modeling allowed the client to combine and separate barrier statements, producing insights that were more actionable than a flat list of barriers.
While specific findings remain confidential, the results are being used to inform marketing, product, and communication strategies which gives the client a roadmap to attract and retain customers more effectively.
Structural equation modeling is one of the most effective tools for turning complex customer data into clear, actionable insights. By revealing both direct and indirect relationships, it helps businesses understand why customers act the way they do and which levers to pull for maximum impact.
Whether you’re a data analyst looking to validate hypotheses, a customer insights professional mapping decision journeys, or a business leader setting strategy, this framework provides a way to go beyond surface-level metrics.
At Drive Research, we combine advanced analytics with custom survey design, robust fieldwork, and thoughtful reporting. The result: research you can trust to drive smarter decisions.
