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
“What sample size do I need?” We’ve all been trained from years of math education to expect a single answer to that question—a single sample size number. But earlier, we warned against the quixotic quest to identify the one true sample size to use for UX research—the “magic number.” Because sampling error is real but
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
One of the more challenging things about learning math in general (and statistics in particular) is how the formulas, often with Greek symbols, translate to things we can see and experience. The abstractness of these formulas often means we just have to take them at face value, believing that someone smarter than us made sure
Happy New Year from all of us at MeasuringU®! In 2024, we posted 47 articles and continued to add features to our MUiQ® UX testing platform to make it even easier to develop studies and analyze results. We hosted our 11th UX Measurement Bootcamp—a blended virtual event attended by an international group of UX practitioners
You invite your friends over for a gathering at your house. As the party gets started, you watch in horror as a friend trips on the edge of your carpet before she even takes the first sip of her Cabernet (now spilled on your white couch). You rush to fix the carpet. But before you
What sample size should you use for a problem discovery (formative) usability study? In practice, the answer is based on both statistics AND logistics. A statistical formula will tell you an optimal number to select. But the real-world logistical constraints of budgets, recruiting challenges, and time will often dictate the maximum number of participants you
“How many participants should you run in a usability study?” How many times have you heard that question? How many different answers have you heard? After you sift through the non-helpful ones, probably the most common answer you’ve heard is five. You might have also heard that these “magic 5” users can uncover 85% of
One box, two box, red box, blue box … Box scoring isn’t just something they do in baseball. Response options for rating scale data are often referred to as boxes because, historically, paper-administered surveys displayed rating scales as a series of boxes to check, like the one in Figure 1. Figure 1: Illustration of “boxes”
Few things seem to elicit more opinions, exaggerations, and accusations than rating scale response options. From the “right” number of points, the use (or not) of labels, and the presentation order (to name a few), it seems we all have thoughts on what to do (or not to do) regarding conventions and “rules” when selecting
