The standard deviation is the most common way of measuring variability or “dispersion” in data. The more the data is dispersed, the more measures such as the mean will fluctuate from sample to sample. That means higher variability (higher standard deviations) requires larger sample sizes. But exactly how much do standard deviations—whether large or small—impact
Are customers more satisfied this quarter than last quarter? Do users trust the brand less this year than last year? Did the product changes result in more customers renewing their subscriptions? When UX researchers want to measure attitudes and intentions, they often ask respondents to complete multipoint rating scale items, which are then compared with
Sample size estimation is an important part of study planning. If the sample size is too small, the study will be underpowered, meaning it will be incapable of detecting sufficiently small differences as statistically significant. If the sample size is too large, the study will be inefficient and cost more than necessary. A critical component
Is product satisfaction above average? Is it best in class? Do customers have a favorable or unfavorable opinion of the current product? When UX researchers want to measure attitudes and intentions, they often ask respondents to complete multipoint items like the one shown in Figure 1. It’s also common to set a target benchmark for
The number of scale points, labeling points, and biases toward the left side of a scale … For over a hundred years, researchers have debated the effects of numerous rating scale attributes. At MeasuringU, we’ve contributed to these debates, publishing empirical research to advance evidence-based practice in UX research. For some examples, see our summary
The UMUX-Lite is a popular two-item measure of perceived usability that combines perceived ratings of Ease and Usefulness, as shown in Figure 1. Figure 1: Standard version of the UMUX-Lite (standard item wording with five-point scales). Since we began regularly using the UMUX-Lite in our practice, we’ve had numerous clients ask whether it
A blank page can lead to writer’s block. Writing survey questions can also seem like trying to write the Great American Novel. It can be particularly daunting knowing that subtle word changes may lead to unanticipated responses. The good news is that you don’t have to start from scratch each time. Instead, you can follow
We’ve written extensively about question types, the elements of good and bad writing, why people forget, and common problems with survey questions. But how do you get started writing questions? Few professionals we know have taken a formal course in survey development and instead rely on their experiences or best practices. Despite being called questions,
Crafting survey questions involves thinking first about the content and then about the format (form follows function). Earlier, we categorized survey questions into four content types (attribute, behavior, ability, or sentiment) and four format classes (open-ended, closed-ended static, closed-ended dynamic, or task-based). As with any taxonomy, there are several ways to categorize response options (e.g.,
In architecture, form follows function. In survey design, question format follows content. Earlier we described four classes of survey questions. These four classes are about the form, or format, of the question (e.g., open- vs. closed-ended). But before you can decide effectively on the format, you need to choose the content of the question and
