Creating quantitative personas using latent class analysis
How the person-oriented approach facilitates the creation of statistical personas.
Have you ever wondered if there’s a better way to understand your users beyond simple survey metrics such as averages and medians? In my previous article, I discussed the person-oriented approach and compared it to the variable-oriented approach, describing how the person-oriented approach sees users as whole, unique entities, while the variable-oriented approach breaks users into parts and misses the big picture. Here, I will explain how much of a difference the person-oriented approach can make in your analyses as a UX researcher and how it will help you create data-driven personas. To do this, I will use artificial data generated by ChatGPT-4o from a survey I created.
To better understand the user base and create quantitative personas, a UX researcher usually conducts surveys aimed at gathering insights into users’ behavior, habits, and past experiences. In this article, we assume the role of a UX researcher at a startup developing a product related to books, and we are interested in the users’ reading habits. To find out more about these habits, we have run a survey containing the following questions:
Imagine you have gathered 1000 responses for this short survey (the following visualizations are based on the data generated via Chat-GPT 4o, you can find the dataset here). How would you report the results?
Taking the routine, variable-oriented approach, you would answer these questions like this:
While such data are informative, they often lack meaningful connections between different responses. For instance, you may know how many users prefer reading audiobooks, but it does not reveal how this preference correlates with other aspects, such as reading frequency or context. In the variable-oriented approach, these relationships are analyzed separately using correlations. For example, you may find that individuals who read in the mornings are more likely to prefer audiobooks. Although these correlations can be insightful, they fail to provide a comprehensive picture of the holistic identity of your users.
To address this limitation, you may include demographic questions in your survey, such as age, gender, education level, or income, to provide more context and depth. However, demographics alone are insufficient to understand users’ mindsets or predict their future behaviors.
To truly understand who the users are, we need a deeper analysis. This is where the person-oriented approach becomes invaluable.
The use of person-oriented analysis to achieve deeper user insights
In the person-oriented approach, rather than analyzing each survey question independently, the goal is to understand participants as a whole. This involves identifying clusters of users with similar behaviors. To accomplish this, you can employ Latent Class Analysis (LCA).
The meaning of “latent” in latent class analysis
Before exploring how the person-oriented approach utilizes Latent Class Analysis (LCA), it’s essential to understand the term “latent.” In this context, “latent” refers to something that exists but is not immediately visible or directly measurable. LCA identifies these hidden variables — underlying patterns or traits that go beyond the observable data, such as responses to survey questions. This method allows researchers to uncover and interpret these unseen factors that shape observable behaviors, classifying users based on these deeper, often unmeasured, characteristics.
The person-oriented approach builds on this foundation by enhancing your analysis in three key ways:
- Discovering your participant groups — helping you identify distinct groups within your user base.
- Revealing the unobservables — uncovering hidden patterns that typical survey metrics miss.
- Adding dimensionality to the data — enabling a richer, more nuanced view of users’ behaviors and motivations.
In the following sections, we will explore each of these aspects in depth and illustrate how they come to life in our example research project.
1. Discovering your participant groups
In this illustration, we observe three distinct participants who have chosen audiobooks as their preferred reading medium. While all of them share this preference, their behaviors and preferences differ significantly. These differences become clear when we analyze their responses across all survey questions. For example, a participant who listens to audiobooks only a few times a month during their commute contrasts sharply with someone who listens daily every morning.
Rather than analyzing each survey question separately, this approach examines each participant’s entire set of responses across all questions. For example, a participant might indicate they listen to audiobooks while commuting a few times a month. These complete sets of responses are then classified using Latent Class Analysis (LCA), allowing us to group participants based on shared characteristics.
By applying LCA to our mock dataset, we identified two distinct participant groups, known as Latent Classes:
Group 1: The steady scholars
Group 2: The spontaneous explorers
In the charts above, you see the probabilities with which each group of users answered our questions. As shown, these two groups provided notably different responses. This insight allows us to take the next step: Identifying the underlying variables or characteristics that distinguish these groups from one another.
2. Revealing the unobservables
Latent Class Analysis (LCA) aims to infer unobservable variables from observable ones. In this study, the observable variables are:
- Conditions in which users read books
- Users’ reading mediums
- Users’ reading frequency
LCA enables us to go beyond these surface-level variables, linking them together to create a more comprehensive picture that adds depth to our understanding of user behavior.
To identify the unobservable variables and interpret these groups, we need to examine the patterns in their responses. The Steady Scholars (group 1), for example, show a strong preference for physical books — a more conservative choice. They also tend to read daily, suggesting a propensity for maintaining routines. This group’s second-most likely choice is reading a few times a week, and their selected reading times are regular and rhythmic, indicating a set routine. Overall, these patterns imply that the steady scholars may be conscientious, routine-oriented, and possibly more conservative in their habits.
In contrast, The Spontaneous Explorers (group 2) lean toward more modern reading mediums, unlike the more traditional preferences seen in The Steady Scholars. They also show little regularity in reading frequency and reading conditions, suggesting a preference for novelty and spontaneity. This pattern implies a group of individuals who may be more novelty-seeking and less likely to adhere to strict routines, showing a lower level of conscientiousness compared to the steady scholars.
In summary, these interpretations reveal two key factors differentiating the groups: openness to new experiences and conscientiousness. These two factors, which we might call our latent variables, represent the deeper traits underlying the observed behaviors. Interpreting these latent variables, however, requires a strong understanding of psychological theories of personality to draw meaningful conclusions.
Taking a look at what we have done here, we understand that the flow of interpretation, coming up with classes, and identifying the latent variables is as shown below:
Finding the unobservable variables
Identifying unobservable variables requires a solid theoretical foundation, often found in personality psychology. Because these participant groups are likely to differ qualitatively, their traits are assumed to be rooted in stable personality characteristics rather than temporary states. Personality psychology provides scientifically grounded theories to guide this analysis, focusing on enduring traits that can distinguish between groups.
Once you have identified potential personality traits that correspond to the latent classes you’ve found, you can generate various hypotheses to explore further. In our example, we inferred that openness and conscientiousness might underlie the observed behaviors in each group. With these assumptions, we can hypothesize additional characteristics and behaviors that may be associated with each group:
Additional traits of users with high openness to experience:
- Early adoption of new features or services
- Higher frequency of browsing
- Tendency to explore a variety of genres
Additional traits of users with high conscientiousness:
- Greater loyalty to the platform
- More frequent usage
- Higher likelihood of engaging with triggered notifications
It is important to recognize, however, that not all behavioral differences stem from personality traits; environmental and social contexts can also shape user behaviors and should be considered in the analysis.
3. Adding dimensionality to the data
Each dataset we work with can be thought of as having a dimensionality, especially when visualized. Consider binary data, for example, where responses to yes/no questions could be represented by a single dot that appears when the answer is “yes” and doesn’t appear for “no.” This type of data is essentially 0-dimensional, as it contains only presence or absence. Let’s revisit one of the variable-oriented results displayed earlier:
The data from this question can be viewed as 0-dimensional. It is composed of four binary questions (e.g., “Do you usually read physical books?”), and each participant’s response can be represented by a single dot. With a sample of 1,000 responses, we have a set of 0-dimensional data points — each dot representing a participant’s answer to these yes/no questions.
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.
Why these analyses matter
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.
This is not how real-world data usually looks
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.
Final thoughts
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.
