Over the past few months, we’ve conducted several studies with different versions of the seven-point Single Ease Question (SEQ®), a popular task-level metric for perceived ease-of-use. As we’ve seen with other research on rating scales, response means tend to be rather stable despite often salient changes to formatting. In our earlier SEQ research, we found
We use the seven-point Single Ease Question (SEQ®) frequently in our practice, as do many other UX researchers. One reason for its popularity is the body of research that started in the mid-2000s with the comparison of the SEQ to other similar short measures of perceived ease-of-use, the generation of a normative SEQ database, and
Near the top of the list of concerns people have when using statistics with UX data is what to do with non-normal data. If you remember only a few things from statistics class, you might recall something about data needing to look like the infamous bell curve; more specifically, it needs to be normally distributed.
For quantifying the user experience of a product, app, or experience, we recommend using a mix of study-level and task-based UX metrics. In an earlier article, we provided a comprehensive guide to task-based metrics. Tasks can be included as part of usability tests or UX benchmark studies. They involve having a representative set of users
The seven-point Single Ease Question (SEQ®) has become a standard in assessing post-task perceptions of ease. Since the SEQ’s inception, we have collected data from thousands of task experiences to generate a normalized database of scores. We have also established its strong correlation to task completion and task time. Over the years, some researchers have
When quantifying the user experience of a product, app, or experience, we recommend using a mix of study-level and task-based UX metrics. While it’s not always feasible to assess a task experience (because of challenges with budgets, timelines, or access to products and users), observing participants attempt tasks can help uncover usability problems, informing designers
In Quantifying the User Experience, we recommend using a mix of task-level and study-level metrics, especially in benchmarking studies. But what, exactly, are task-level and study-level metrics, how do they differ, and why should you collect them both? In this article, we’ll explore this common practice of collecting both types of metrics to understand the
Time is a metric we all understand so it’s no wonder it’s one of the core usability metrics. Perhaps it’s something about the precision of minutes and seconds that demands greater scrutiny. There’s a lot to consider when measuring and analyzing task time. Here are 10 of them. Task times are collected in about half
Why spend more time completing a task when it could be done in less time? Users become very cognizant of inefficient interactions and this is especially the case with tasks that are repeated often. Task time is the best way to measure the efficiency of a task and it is a metric that everyone understands.
Recently Nielsen conducted a study on the reading speeds between the printed book, Kindle and iPad. From 24 users the study concluded that the iPad took about 6.2% longer (p =.06) and Kindle about 10% longer (p <.01) to read than the same story on a printed book. From this data Nielsen concluded “Books Faster