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
We developed the SUPR-Qm® in 2017 to measure the quality of the mobile app experience. Its original form had 16 items. That is long for a UX questionnaire (e.g., the SUS has ten and the SUPR-Q® has eight). The reason it had 16 items was that it was developed using a technique called Rasch analysis,
User experience research has a wide variety of methods. The variety can be both inspiring and daunting—where do you start, what should you master? We recommend two approaches to extend your knowledge of UX methods. First, understand how the many methods relate by reviewing our taxonomy. Second, understand which methods are used the most and
Pets aren’t just family; they’re big business. The expectation for the U.S. is that owners will spend a total of $157 billion by the end of 2025 and will spend close to $200 billion in 2030. In 2024, U.S. pet owners spent over $28 billion buying pet food and supplies online (up 2.6% year-over-year), with
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
The word “standardized” conjures memories of high-stakes tests. In the context of UX research, when we talk of standardization, we’re often referring to standardized questionnaires. Standardized questionnaires have gone through the process of psychometric validation. That means the items being used have gone through dozens or hundreds of possible variations, and the final versions are
We know that for a lot of UX professionals, quantitative UX research sounds like an oxymoron. You might have been involved in a few debates that included topics like: Whether you should even use numbers in UX research Which is the best UX metric to use Whether you’re using the right statistical test on your
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.
“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
Did the student write the paper with AI? Did the teacher review the paper with AI? Did the respondent use AI to answer the survey? Did the researcher use AI to detect responses that were AI generated? Was this article written with AI? Was the LinkedIn post about this article written with AI? There’s hardly
