The Anatomy of a Survey Question

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,

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“Does What I Need It to Do”: Assessing an Alternate Usefulness Item

The UMUX-Lite is a two-item standardized questionnaire that, since its publication in 2013, has been adopted more and more by researchers who need a concise UX metric. Figure 1 shows the standard version with its Perceived Ease-of-Use (“{Product} is easy to use”) and Perceived Usefulness (“{Product}’s capabilities meet my requirements”) items.   Figure 1: Standard

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A Decision Tree for Picking the Right Type of Survey Question

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.,

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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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Classifying Survey Questions into Four Content Types

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

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Do Too Many Response Options Confuse People?

Advice on rating scale construction is ubiquitous on the internet and in the halls of organizations worldwide. The problem is that much of the advice is based not on solid data but rather on conventional wisdom and what’s merely thought to work. Even published papers and books on survey design can present a perspective that

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Four Types of Potential Survey Errors

When we conduct a survey, we want the truth, even if we can’t handle it. But standing in the way of our dreams of efficiently collected data revealing the unvarnished truth about customers, prospects, and users are the four horsemen of survey errors. Even a well-thought-out survey will have to deal with the inevitable challenge

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What Do You Gain from Larger-Sample Usability Tests?

We typically recommend small sample sizes (5–10) for conducting iterative usability testing meant to find and fix problems (formative evaluations). For benchmark or comparative studies, where the focus is on detecting differences or estimating population parameters (summative evaluations), we recommend using larger sample sizes (20–100+). Usability testing can be used to uncover problems and assess the

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Are Sliders Better Than Numbered Scales?

There are many ways to format rating scales. Recently we have explored Labeling neutral points Labeling all or some response options Altering the number of response options Comparing agreement vs. item-specific endpoint labels Each of these formatting decisions has a variety of opinions and research, both pro and con, in the scientific literature at large.

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Three Questionnaires for Measuring Voice Interaction Experiences

Human: Computer, can you recognize speech? Computer: I think you said, can you wreck a nice beach? Both the quality of synthesized speech and the capability of communicating with a computer using your voice have come a long way since the debut of this technology in the 1970s. One of the most famous synthetic voices

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