Measuring UX: From the UMUX-Lite to the UX-Lite

For the past few years, we’ve written extensively about our research and usage of the UMUX-Lite. That research has followed the increase in popularity of this compact questionnaire. From its initial publication in 2013, the UMUX-Lite (Usability Metric for User Experience—Lite Version) has become an increasingly popular measure of perceived usability. Figure 1 shows the

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A Review of Alternates for the UMUX-Lite Usefulness Item

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

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Replicating Assessments of Two UMUX-Lite Usefulness Alternates

The original wording of the UMUX-Lite Perceived Usefulness item is “{Product}’s capabilities meet my requirements.” Since we started using the UMUX-Lite in our practice, we’ve had numerous clients ask whether it would be possible to simplify the wording of this item to more closely match the simplicity of the UMUX-Lite Perceived Ease item, “{Product} is

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Sample Sizes for Comparing Net Promoter Scores

Sample size estimation is a critical step in research planning, including when you’re trying to detect differences in measures like Net Promoter Scores. Too small of a sample and you risk not being able to differentiate real differences from sampling error. Too large of a sample and you risk wasting resources—researchers’ and respondents’ time and,

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From Statistical to Practical Significance

Hypothesis testing is one of the most common frameworks for making decisions with data in both scientific and industrial contexts. But this statistical framework, formally called Null Hypothesis Statistics Testing (NHST), can be confusing (and controversial). In an earlier article, we showed how to use the core framework of statistical hypothesis testing: you start with

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Statistical Hypothesis Testing: What Can Go Wrong?

Making decisions with data inevitably means working with statistics and one of its most common frameworks: Null Hypothesis Significance Testing (NHST). Hypothesis testing can be confusing (and controversial), so in an earlier article we introduced the core framework of statistical hypothesis testing in four steps: Define the null hypothesis (H0). This is the hypothesis that

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How Does Statistical Hypothesis Testing Work?

Statistically significant. p-value. Hypothesis. These terms are not only commonly used in statistics but also have made their way into the vernacular. Making sense of most scientific publications, which can have practical, significant effects on public policy and your life, means understanding a core framework with which we derive much knowledge. That framework is called

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5 Techniques to Identify Clusters In Your Data

Understanding who your users are and what they think about an experience is an essential step for measuring and improving the user experience. Part of understanding your users is understanding how they are similar and different with respect to demographics, psychographics, and behaviors. These groupings are often called clusters or segments to refer to the

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6 Ways to Visualize Statistical Significance

Researchers rely heavily on sampling. It’s rarely possible, or even makes sense, to measure every single person of a population (all customers, all prospects, all homeowners, etc.). But when you use a sample, the average value you observe (e.g. a completion rate, average satisfaction) differs from the actual population average. Consequently, the differences between designs

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The Difference between Observed and Latent Variables

You can’t see customer satisfaction. You can’t see usability. There isn’t a thermometer that directly measures someone’s intelligence. While we can talk about satisfied customers, usable products, or smart people, there isn’t a direct way to measure these abstract concepts. And clearly these concepts vary. We’ve all had experiences that left us feeling unsatisfied or

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