What should you do when your sample doesn’t match the known population composition on key variables like prior experience? One approach is to weight your data to rebalance the sample. In a previous article, we discussed how to weight means (such as from rating scales) when there are differences between group proportions in a sample
Mobile apps are different from websites. People have different expectations for a mobile app and how it can integrate with their phone and data. While the mobile app experience is similar in many ways to other interfaces such as websites and software, mobile apps are distinct enough that we feel they deserve their own questionnaire.
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
Early and often is not just advice for voting in Chicago—it’s also one of the key principles for designing for a usable experience. Testing an experience while it’s still in its prototype stage allows you to find and fix problems before they become difficult and expensive to fix. User experiences with prototypes (even low fidelity
In a previous article, we discussed the pros and cons of using weights to compensate for differences between a sample and a reference population. Due to its risks, the consensus about weighting is that it’s a method of last resort when (1) it’s critically important for proportions of sample groups to match a reference population
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
