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Sample Sizes for Rating Scale Confidence Intervals

Sample size computations can seem like an art. Some assumptions are involved when computing sample sizes, but it should be more math than magic. A key ingredient needed to cook up a sample size estimate is the standard deviation. You need yeast to make bread, and you need a measure of variability to make an

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rating scale data grid with foreground text reading: The Variability and Reliability of Standardized UX Scales

The Variability and Reliability of Standardized UX Scales

In an earlier article, we examined a large dataset of rating scale data. After analyzing over 100,000 individual responses from 4,048 multipoint items across 25 studies, we reported the typical standard deviations for five-, seven-, and eleven-point items. We found that the average standard deviation tended to be around 25% of the maximum range of

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How to Use the Finite Population Correction

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

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How to Compare Two Proportions with the N−1 Two-Proportion Test

Proportional data is common in both UX research and the larger scientific literature. You can use proportions to help make data-driven decisions just about anywhere: Which design converts more? Which product is preferred? Does the new interface have a higher completion rate? What proportion of users had a problem with registering? Consequently, you’ll likely want to

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Comparing Two SEQ Item Wordings

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

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Is UX Data Normally Distributed?

If you took an intro to stats class (or if you know just enough to be dangerous), you probably recall two things: something about Mark Twain’s “lies, damned lies …,” and that your data needs to be normally distributed. Turns out both are only partly true. Mark Twain did write the famous quote, but he

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Sample Sizes for Comparing SUS Scores

Microsoft Word is a widely used word processing program, part of the Microsoft Office suite of programs. While its dominance has been challenged recently by Google Docs, Word still leads on the features list, providing many features that Google’s offering lacks. But adding features can also add to bloat, making common tasks harder as users

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4 Things UX Research Tells You that Google Analytics Doesn’t

Google Analytics is an amazing tool for understanding website traffic. There’s a reason most of the top websites use it. Among other things it can tell you: – How many people visit daily, monthly, and across years and seasons – How much time people spend on pages – What pages get the most visitors –

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Predictive Customer Analytics 101: The Correlation

Want to know what customers are likely to do? You’re not alone. Most organizations would love to predict their customers’ next action or attitude. Unfortunately, there isn’t an analytics crystal ball that provides a clear and accurate picture of the future. Instead, we have to rely on the much murkier reality of past data to

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6 Tips for Better Data Analysis

At our company, we collect and examine a lot of data from various studies: usability tests, branding studies, customer-segmentation analyses, and so on. While you always can’t control the quality of the responses, or the questions asked to participants, you can make the most with the data you receive by using these six techniques the

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