UX Researcher @ Perceptual User Experience Lab | Human-AI Interaction Researcher @ University of Arkansas at Little Rock
Missing data is one of those things that every UX or behavioral researcher has to deal with, but few of us are taught how to handle it well. We often default to deleting rows or filling in averages, just to get the analysis moving. But the method you choose to handle missing data isn’t just a technical decision. It can make or break the validity of your insights. Let’s say your dataset is mostly clean, with just a few missing points scattered randomly. In that case, listwise deletion or simple mean imputation might do the trick. But the moment your missingness is related to user characteristics- say, older users skipping certain questions - you’ve entered trickier territory, where those basic methods fall apart. For survey-style data, especially when variables relate to each other (like age, satisfaction, and ease of use), MICE - Multiple Imputation by Chained Equations - remains a rock-solid choice. It doesn’t just guess once; it builds multiple complete datasets based on patterns in your data, then combines the results so that your uncertainty is preserved. This is great for drawing valid conclusions without pretending the missingness never happened. If you're looking for something faster or easier to implement, K-Nearest Neighbors can be a good option. It finds similar users and borrows their values to fill in gaps. It’s intuitive and can work surprisingly well - especially with smaller datasets. Random Forest imputation is another great option when your dataset has both numerical and categorical data. It uses decision trees to learn patterns and works well even when your data is messy or non-linear. Once you start working with larger or more complex datasets - think logs, sensor data, or time-series interactions - AI-based methods start to shine. Autoencoders are neural networks that learn how to compress and reconstruct your data. If trained correctly, they can guess missing values in a way that reflects deep structure in your dataset. GANs, like GAIN, go a step further by learning to create data that’s indistinguishable from the real thing. They’re especially useful when missing data isn’t random and follows hidden patterns. Recently, transformer-based models like SAITS and ReMasker have pushed things even further. These models use attention mechanisms to find long-range patterns across time or across features. They’re particularly useful in behavioral and biometric research where data is sequential, noisy, and full of gaps. Still, none of these methods are perfect in isolation. That’s why ensemble methods are becoming more common - combining multiple imputation models into one workflow that balances their strengths. This is especially helpful in mixed-method UX studies where you might have numerical data, categorical survey responses, and even behavioral logs in the same dataset.