When approaching a UX research project, one of the first things to consider is the method. And UX research has many methods. Methods can be categorized as quantitatively focused (e.g., A/B tests) or qualitatively focused (e.g., interviews). Most UX research methods can collect both qualitative and quantitative data. For example, surveys often collect both closed-ended
Surveys are an essential method for collecting data. But like all research methods, surveys have their limitations. Unless the survey is administered by a facilitator, a respondent has only the survey’s instructions, questions, and response options for guidance. Earlier we wrote about ways to improve the clarity of your questions and certain words to watch
Like in all research methods, many things can go wrong in surveys, from problems with sampling to mistakes in analysis. To draw valid conclusions from your survey, you need accurate responses. But participants may provide inaccurate information. They could forget the answers to questions or just answer questions incorrectly. One common reason respondents answer survey
When thinking about user experiences with websites or software, what is the difference between capabilities and functions? Is there any difference at all? In software engineering, a function is code that takes inputs, processes them, and produces outputs (such as a math function). The word capability doesn’t have a formal definition, but it most often
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
The Net Promoter Score (NPS) is widely used by organizations. It’s often used to make high-stakes decisions on whether a brand, product, or service has improved or declined. Net Promoter Scores are often tracked on dashboards, and any changes (for better or worse) can have significant consequences: adding or removing features, redirecting budgets, even impacting
When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed
Despite the ease with which you can create surveys using software like our MUIQ platform, selecting specific questions and response options can be a bit more involved. Most surveys contain a mix of closed-ended (often rating scales) and open-ended questions. We’ve previously discussed 15 types of common rating scales and have published numerous articles in
Like pictures and pixels on a screen, words are a type of user interface. Complex language, like complex software, can lead to misunderstanding, so words should communicate effectively while being easy to understand. The solution, to paraphrase William Zinsser, is to use words that are simple and concise—a guideline that also applies to UX questionnaires.
The two-sample t-test is one of the most widely used statistical tests, assessing whether mean differences between two samples are statistically significant. It can be used to compare two samples of many UX metrics, such as SUS scores, SEQ scores, and task times. The t-test, like most statistical tests, has certain requirements (assumptions) for its
