UX Researcher @ Perceptual User Experience Lab | Human-AI Interaction Researcher @ University of Arkansas at Little Rock
If you’re a UX researcher curious about what Structural Equation Modeling (SEM) can actually do for your work, you’re in the right place. Let’s say you’re working on a grocery planning app. Users enter ingredients they have, and the app recommends recipes. Now you want to understand how to make that experience better. You might have some intuitive ideas: maybe if the app is easy to use, the personalization feels stronger. If personalization improves, satisfaction goes up. And when users are satisfied, they’re more likely to stick around. But how do you test that whole chain of relationships at once? That’s exactly what SEM is built for. So what is SEM? It’s a statistical framework that helps you test how different aspects of a user’s experience are linked - simultaneously. Unlike traditional methods that analyze one relationship at a time, SEM lets you look at the full picture, including both visible data (like task success or ratings) and hidden concepts (like trust or satisfaction). These hidden concepts are called latent variables. You don’t measure them directly, you estimate them through things like survey questions. For example, satisfaction might be reflected in responses like “I enjoy using this app” or “This app meets my needs.” SEM is especially helpful because UX is never just one thing. Users’ feelings and behaviors are shaped by a web of interconnected elements like ease of use, trust, enjoyment, and perceived usefulness. If you want to know what really drives continued use, you need to model the whole system, not just isolated parts. This kind of modeling lets you go beyond surface-level stats. You can separate the things you observe (like a 1-5 star rating) from the psychological constructs you care about (like satisfaction). You can also identify which features influence others indirectly, such as how ease of use might boost satisfaction by first improving personalization. You can even account for measurement error and compare different user groups, like first-time users versus power users. Let’s bring it back to our grocery app. You might collect data on how easy users find the app to navigate, how personalized the recommendations feel, how satisfied they are overall, and whether they intend to keep using it. SEM lets you test how each of those pieces fits together. The results might show that ease of use drives personalization, which increases satisfaction, which in turn predicts continued use. It’s a roadmap for product decisions. If you’re new to SEM, don’t worry. Start by learning the basics of regression and factor analysis. From there, tools like AMOS (great for visual modeling) or R’s lavaan package (great if you like code) can take you further. Two great books for getting started are Barbara Byrne’s Structural Equation Modeling with AMOS and Rex Kline’s Principles and Practice of SEM.