Choosing the Right Regression for Real Human Data

UX research has gone through a quiet revolution. What used to be mostly interviews, usability testing, and expert reviews has expanded into a field deeply driven by data. Today we are not just listening to what users say. We analyze behavioral logs, interaction timing, psychometrics, conversion funnels, eye-tracking traces, error counts, retention curves, and long-term engagement metrics. Our questions have also become sharper. We no longer ask simply which design people like better. We ask what drives satisfaction, which behaviors predict churn, and where friction actually emerges in real user journeys.

This shift toward quantitative UX has exposed a surprising gap in practice. Most teams still rely on the same old statistics toolkit they learned in school: t-tests, ANOVA, and basic linear regression. Those tools are easy to use and easy to explain, but they do not match the kind of data UX produces. Human behavior almost never follows the neat mathematical assumptions those tests require.

Reaction times are skewed. Task success is binary. Error counts stack up as long tails. Survey ratings are ordinal, not truly numeric. User engagement metrics are bounded between 0 and 1. And repeated measurements from the same participant violate independence assumptions. When we treat this messy data as if it were clean, continuous, and normally distributed, the results look “scientific,” but the conclusions can be dangerously wrong.

Modern UX research needs models that respect how behavioral data is actually generated. That is where advanced regression enters the picture. Below I walk through the major regression families that matter in UX, why they exist, and when they are the right tool to use.

Linear Regression

Linear regression is still the foundation of everything that follows. It models a continuous outcome as a weighted sum of predictors and works well when assumptions are reasonably satisfied: linearity, independence, normally distributed errors, and constant variance.

In UX, its most common use is key driver analysis. Here we measure multiple experience attributes such as “navigation ease,” “search relevance,” or “interface clarity,” and use regression to estimate which factors drive a global metric like SUS or NPS. The standardized coefficients let product teams prioritize work based on impact.

Linear models work when the outcome has enough spread and behaves approximately normally. Time-on-task can be analyzed after log-transformation. Revenue metrics and aggregate psychometric scores are often treated as continuous for practical reasons.

Where linear regression breaks down is when its assumptions no longer hold. Raw reaction times are strongly skewed. Task success is binary. Satisfaction ratings are bounded. Error counts cannot be negative yet linear regression will happily predict negative outcomes anyway. In these conditions, the classical linear model becomes unreliable.

When the assumptions fail, we do not force the data to fit the model. We change the model to fit the data.

Polynomial Regression

Human behavior is rarely linear. Learning curves show fast early improvement followed by plateau. Option overload shows rising satisfaction until cognitive load triggers decline. Both patterns are curved.

Polynomial regression captures this by adding higher-order terms to the standard linear model. Instead of fitting a straight line, the model bends to reflect real behavioral shapes.

In UX this is crucial for:

• Learning curve analysis, where task time drops sharply over early trials and then stabilizes.

• Choice complexity studies, where satisfaction rises with options and then collapses under overload.

Without polynomial terms, linear models misrepresent these effects and leave systematic patterns in the residuals. Polynomial regression fixes this underfitting problem, though it must be used conservatively. Too many degrees create unstable “wiggles” that model noise instead of signal.

Logistic Regression

Much of UX analytics deals with yes-or-no outcomes. Did the user convert. Did they complete the task. Did they churn. Linear regression is mathematically inappropriate here because it predicts values below zero or above one.

Logistic regression solves this by modeling probabilities directly using an S-shaped link function. Predictions are naturally bounded between 0 and 1 and interpret as likelihoods of events, not raw scores.

In UX practice, logistic regression is everywhere:

• Task success modeling.

• Funnel analysis with covariate adjustment.

• Controlled A/B tests where demographic or device differences must be accounted for.

For communication, coefficients are converted to odds ratios. Saying “daily login increases renewal odds by 3.5 times” is far more useful to product teams than raw beta coefficients.

Multinomial Regression

Users often select from more than two options: pricing tiers, navigation paths, support channels.

Multinomial logistic regression models probabilities across multiple unordered categories. It treats each option as a distinct outcome rather than forcing them into a fake numeric scale.

This is essential for:

• Understanding navigation choices.

• Forecasting pricing tier selection.

• Designing purchase pathways segmented by user attributes.

Linear approaches fail here because categorical outcomes have no inherent numeric order. Multinomial models handle this correctly by generating probability distributions across all possible choices.

Ordinal Regression

Likert scales dominate UX surveys but are not continuous measurements. The “distance” between ratings is psychological, not numeric. The leap from 4 to 5 is rarely the same as from 2 to 3.

Ordinal logistic regression respects this ranking structure by modeling cumulative category probabilities rather than treating ratings as numeric points.

This is ideal for:

• Satisfaction scale analysis.

• Severity ratings.

• Net Promoter classification drivers.

Linear regression averages distort scale meaning and compress important psychological transitions. Ordinal models preserve those transitions and provide more honest effects tied to human interpretation.

Poisson and Negative Binomial (Count Models)

UX teams often measure counts: number of errors, number of clicks, support tickets, feature opens. Count data are discrete and non-negative. Linear regression again fails by outputting impossible predictions.

Poisson regression models counts directly, but with the strict assumption that variance equals the mean. UX data almost never follows that rule. Real datasets show overdispersion: most users generate very low counts while a few power or struggling users produce extreme values.

Negative binomial regression introduces a dispersion term that absorbs this variability. It is the standard for:

• Support ticket counts.

• Feature usage intensity.

• Behavioral event tracking with heavy tails.

Using Poisson under overdispersion dramatically underestimates uncertainty and produces false statistical positives. Negative binomial restores correct inference.

Zero-Inflated Models

Many UX datasets show two simultaneous patterns. A majority of users never engage with a feature at all while those who do engage vary widely in intensity. Standard count models struggle to fit the spike at zero alongside the long-tail usage distribution.

Zero-inflated models split the problem into two questions:

• Did the user engage at all. (Binary process)

• If they did, how much did they engage. (Count process)

This is immensely valuable in UX because it separates:

• Findability failures from

• Utility failures

For example, are users not exporting files because they cannot locate the export feature or because once found, the feature is weak? Zero-inflated models directly test that difference.

Beta Regression

Many UX metrics are proportions bounded between 0 and 1:

• Scroll depth.

• Completion rates.

• Percent watched.

Linear regression misbehaves here because variance naturally shrinks near the boundaries and predictions can fall outside possible limits. Beta regression respects the bounded nature of the data and adapts to heteroscedasticity inherent in proportions.

It provides stable and realistic modeling of engagement metrics where values approach 0 or 1.

Mixed-Effects Models

UX experiments often collect multiple observations per user or per stimulus. Without correction, these clustered observations break the independence assumption required by most regression models.

Treating 1,000 fixations from 10 users as independent samples falsely inflates statistical power. This is pseudoreplication.

Mixed-effects models solve this by including:

• Fixed effects for experimental manipulations.

• Random effects for individual users or stimuli.

In UX this is indispensable for:

• Within-subjects A/B testing.

• Longitudinal satisfaction tracking.

• Eye-tracking via Areas of Interest across trials.

The model automatically separates variation within individuals from differences between individuals, ensuring valid inference.

Robust and Quantile Regression

UX data often contains massive outliers from distractions, misclicks, or sensor errors.

Robust regression downweighs extreme points so that they cannot distort overall conclusions.

Quantile regression shifts attention away from the “average user” and toward the edges of performance. It enables teams to ask whether design changes improve outcomes for the slowest users, accessibility populations, or degraded hardware conditions even when averages remain unchanged.

Time-Series Regression

Metrics over time violate independence assumptions because today’s values are linked to yesterday’s.

Time-series models incorporate these dependencies and are essential for:

• DAU forecasting.

• Support load planning.

• Measuring impact of major releases or outages.

Without time-series correction, standard regressions underestimate uncertainty and exaggerate intervention effects.

Bayesian Regression

Usability studies often operate with tiny samples where conventional significance testing collapses.

Bayesian regression incorporates prior evidence and yields probability statements rather than binary significance outcomes. This is especially suited to iterative design where evidence accumulates over cycles rather than appearing all at once.

Statements like “there is a 92 percent chance that the new design improves success rates” reflect decision realities far better than traditional p-values.

Final Thought: Your Model Is a Design Decision

The regression model you pick is not just a technical choice. It decides how you see your users. Choosing a linear model assumes the world is smooth and average. Choosing ordinal models respects psychological categories. Zero inflated models separate discovery from utility. Mixed models recognize that users are not interchangeable data points. Quantile regression focuses on the margins rather than the mythical “average user”. Bayesian methods let you build knowledge over time instead of starting from zero with every new study.

As UX data becomes richer and more complex, we have to be more careful about the tools we use to interpret it. Advanced regression is not about showing off complicated math. It is about using models that are honest about how human behavior is bounded, skewed, noisy, and clustered.

At PUXLab, this is exactly the work we do. We help teams match the right statistical model to the right UX question, and turn messy product data into decisions you can defend in front of leadership, clients, or even a court. If you want to stress test your current analytics, choose better models for your UX studies, or design a more advanced quant pipeline, feel free to message us.