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In this figure, each dot represents a participant’s response in a 0-dimensional space.
By contrast, ordinal data — such as responses on a Likert scale ranging from “very bad” to “very good” — have a 1-dimensional nature because they map along a single line between two extremes. For instance, in our survey question “How frequently do you read books?” responses form a 1-dimensional dataset, representing a continuum from “Daily” to “Never.”
These examples capture the dimensions typically used in the variable-oriented approach. In the person-oriented approach, however, the number of dimensions may increase with the number of survey questions, as each question’s response is viewed as an axis.
In our 3-question survey example, for instance, the person-oriented approach sees a participant’s responses as coordinates in a 3-dimensional space, where each axis represents one survey question.
In this view, the data can span across as many dimensions as there are survey questions. But the story doesn’t end here. When adopting the person-oriented approach, we assume that latent or hidden variables influence participants’ responses. Latent Class Analysis enables us to identify and interpret these underlying variables, representing participants’ placement in a space defined by the latent variables discovered.
To deepen our understanding, let’s turn back to our example of book readers. We previously identified three users who had selected audiobooks as their preferred reading medium.
Their responses can be visualized as coordinates on a 3-dimensional graph, with each dot representing one participant:
In the person-oriented approach, our participants are initially mapped in a 3-dimensional space based on their observed responses, as we had three survey questions — observed variables. However, this is only the starting point. The X-dimensional space formed by observed responses can be refined into a simpler, more insightful space defined by latent (unobservable) variables. In our hypothetical analysis, we identified two such variables — openness to new experiences and conscientiousness, both key personality factors.
In this new, higher-level space, we no longer map individual participants; instead, we map classes or groups of participants identified through LCA. With two identified latent variables, our space becomes 2-dimensional, as illustrated below.
This approach offers a richer, more dimensional insight into user behaviors, helping us build a more comprehensive understanding of the user base and their unique characteristics.
Gaining a deeper understanding of our users allows us to better predict their behavior when introducing new features, even when we are unsure how they might interact with them. As UX researchers, we typically avoid asking future-oriented questions, as such questions often fail to accurately reflect what users will do in the future. This limitation hinders our ability to reliably forecast user behavior.
However, by leveraging the deep insights outlined in this article and understanding how users are segmented based on their personality traits, we can enhance our ability to predict their actions, decisions, and emotions when faced with new features or products.
In real-world datasets, user data seldom falls into such neat categories. Instead, distributions typically follow normal or exponential patterns, with group differences emerging as subtle shifts within these distributions. This makes LCA particularly valuable in real-world applications, where it excels at detecting anomalies and uncovering hidden structures within complex data.
This exercise highlights just how powerful Latent Class Analysis can be in user research. By combining a structured dataset — even an artificially generated one — with a method that goes beneath surface-level data, we’re able to reveal deeper patterns and traits that might otherwise go unnoticed. In a perfect world, real-world data would offer such clear divisions, but part of the value in LCA lies precisely in its ability to navigate and make sense of the messiness inherent in real data. As researchers, our goal isn’t just to classify users but to understand the complex motivations and characteristics that drive their behavior. LCA provides us a unique lens for this purpose, pushing our understanding of users beyond broad demographics into the realm of nuanced, psychology-backed insights. This journey with LCA is just the beginning — there’s always more to uncover beneath the surface.
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