Surveys

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Post-mortems and retrospective accounts are valuable for understanding what went well and what went poorly. This applies not only to investigations of critical events, such as crimes and plane accidents, but also to experiences with products and services. But the usefulness of people's recollections of events and experiences rests on the accuracy of their memories. (Measuring the perception of experiences might be just as valuable

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In UX research, both studies and surveys contain a lot of questions. Getting those questions right can go a long way in improving the clarity and quality of the findings. For example, we’ve recently written about how to make survey questions clearer. And while there are many stories of how the change of a single word in a survey question can lead to different results

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The first questionnaires appeared in the mid–18th century (e.g., the “Milles” questionnaire). Scientific surveys have been around for almost a hundred years. Consequently, there are many sources of advice on how to make surveys better. The heart of each survey is the questions asked of respondents. Writing good survey questions involves many of the principles of good writing, in addition to survey-specific guidelines and advice.

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There is plenty of debate about the best way to quantify attitudes and experiences with rating scales. And among those debates, perhaps the most popular question is the “right” number of response options to use for rating scales. For example, is an eleven-point scale too difficult for people to understand? Is a three-point scale insufficient for capturing extreme attitudes? Most research on this topic shows

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UX research and UX measurement can be seen as an extension of experimental design. At the heart of experimental design lie variables. Earlier we wrote about different kinds of variables. In short, dependent variables are what you get (outcomes), independent variables are what you set, and extraneous variables are what you can’t forget (to account for). When you measure a user experience using metrics—for example,

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To have a reliable and valid response scale, people need to understand what they’re responding to. A poorly worded question, an unclear response item, or an inadequate response scale can create additional error in measurement. Worse yet, the error may be systematic rather than random, so it would result in unmodeled bias rather than just increased measurement variability. Rating scales have many forms, with variations

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In an earlier article, we reviewed five competing models of delight. The models differed in their details, but most shared the general idea that delight is composed of an unexpected positive experience. Or, for the most part, delight is a pleasant surprise. However, there is disagreement on whether you actually need surprise to be delighted. And if you don’t need surprise, then delight is really

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Small changes can have big impacts on rating scales. But to really know what the effects are, you need to test. Labels, number of points, colors, and item wording differences can often have unexpected effects on survey responses. In an earlier analysis, we compared the effects of using a three-point recommend item compared to an eleven-point recommend item. In that study, we showed how a

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It seems like there are endless ways to ask questions of participants in surveys. Variety in question types can be both a blessing and a curse. Having many ways to ask questions provides better options to the researcher to assess the opinion of the respondent. But the wrong type of question can fail to capture what’s intended, confuse respondents, or even lead to incorrect decisions.

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We all are bombarded with surveys asking us to provide feedback on everything from our experience on a website to our time at the grocery store. Many of us also create surveys. They're an indispensable method for collecting data quickly. Done well, they can be one of the most cost effective ways to: Understand the demographics of your customers Assess brand attitudes Benchmark perceptions of

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