Recruiting participants for research is expensive. It’s also rife with problems: Are these people really who they say they are? Are they actually paying attention? Or is the data from some survey farm where people click through and make money? AI is disrupting UX research. But the disruption is leading to more software, not less.
Should the sample size n be greater than 30? If you’ve taken any introductory statistics course or an AP statistics class (or helped your child with it), you’ve encountered the n ≥ 30 rule. The “magic number 5” rule we’ve written extensively about applies (with its important caveats) to problem discovery for usability testing. But the
The UX-Lite® is an increasingly popular UX metric. There’s a reason for its popularity. It’s a simple two-item questionnaire that measures perceptions of the user experience of any interface (product, app, website). Its two five-point items are combined and scaled to generate an overall score and subscale scores on ease and usefulness from 0 to
Satisfaction is different than ease of use. But they are both attitudes. We provided the conceptual foundation for what satisfaction is, how it differs from perceived ease of use, and how both can be collected at the overall product level (also called the study level) or at the task level. So, while we know they
As professionals, we’re judged on what we produce. So-called deliverables are the artifacts produced by researchers. But what are UX research deliverables? Deliverables are almost always a digital record of inputs, outcomes, and recommendations in a document or presentation. But delivering documents and presentations fits the description of just about all knowledge worker output, so
Scatterplots are powerful tools for visualizing data, especially when data is continuous and unbounded (or nearly so). For example, Figure 1 shows the relationship between concurrently collected System Usability Scale (SUS) and UX-Lite® data for 40 consumer software products. Figure 1: Example of scatterplot of concurrently collected SUS and UX-Lite data. Examination of the scatterplot
We use our mobile phones a lot. Planning trips, sending money, following our favorite influencers, keeping in touch with friends and family. While it seems commonplace now, the capabilities of our mobile phones and their applications provide a high level of convenience and speed (for better or worse) to our leisure and business. The mobile
Banking isn’t limited by borders. No matter what language you speak, there’s a universal need to save and access money, check balances, transfer money, and pay people. What’s also universal with digital banking is the inevitable friction caused by security concerns and troublesome user interfaces. While our previous SUPR-Q® benchmark reports have focused on U.S.
AI is rapidly changing. By the time we write about the latest features and performance benchmarks, they are replaced by newer features and benchmarks. But are all those features and benchmarks noticed by users? Perhaps. The speed of change in AI shouldn’t stop us from taking a snapshot of the user experience. Even with rapidly
Five users are enough. Or do you need a large sample size to make statistically significant claims? One of the enduring controversies and sources of confusion in UX research concerns sample size. Part of the reason for the confusion is that there are different perspectives; some are more vocal than others. This isn’t different from
