You’ve decided to conduct a survey. Congratulations! Now it’s time to get into the details. In our experience, one of the most soul-crushing difficulties of running surveys is the process of defining and finding participants. In this article, we’ll go over some of the logistical details you’ll want to sort out before launching your survey.
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
In an earlier article, we showed that only about 9% of panel participants will eventually complete a study in which they are asked to think aloud. That is, if you need ten usable think-aloud videos, expect to invite around 111 participants. On the surface, this means you’ll need to plan for a lot of people
Ranking questions are a popular way to understand how respondents in UX research prioritize items such as product features, habits, purchases, color schemes, or designs. Forcing participants to make tradeoffs on what’s most important versus least important helps avoid the “everything is important” problem you get when you ask respondents to simply rate how important
How do you measure tech savviness? For several years (since 2015), we’ve been on a mission to develop a valid and practical measure. In our earlier articles, we have Reviewed the literature. We reviewed the literature on tech-savvy measures and found three key approaches to measuring tech-savviness by assessing (1) what a person knows, (2)
In an earlier article, we described a pilot study from 2015 in which we investigated how to measure tech savviness. Building on the published literature, we generated candidate items that measured three aspects of tech savviness: what people know, what people do, and what people feel. In that pilot study, we assessed knowledge using a
How do you measure tech savviness? Abstract constructs such as usability, trustworthiness, intelligence, and desirability can be difficult to measure. The same applies to tech savviness. But to paraphrase Potter Stewart, we know a tech-savvy person when we see one. Tech savviness should matter to UX researchers. When we measure an experience, we don’t want
What is the impact if you sample a lot of your population in a survey? Many statistical calculations—for example, confidence intervals, statistical comparisons (e.g., the two-sample t-test), and their sample size estimates—assume that your sample is a tiny fraction of your population. But what if you have a relatively modest population size (e.g., IT decision-makers
