AI can produce polished survey drafts quickly, but experienced human review is still needed to catch subtle survey-design flaws that weaken data quality.
Learn when and why to use post-task and post-test questionnaires in quantitative usability testing – and how combining both can help you pinpoint issues and improve UX.
When creating screener surveys, use fake answer options – called foils – to spot misrecruits before they join your study. Learn how to craft foils that protect your data and catch cheaters early.
In UX surveys, semantic differential scales help measure user attitudes with nuance. This video covers what they are, their pros and cons, and how to write clear, balanced adjective pairs for UX research studies.
AI-simulated users can fill in missing data and predict population-level trends. They perform better when they are based on extensive contextual information.
Likert scales measure user opinions by asking participants to rate statements. They capture nuanced feedback but can face biases. To improve accuracy, use clear questions and techniques to reduce bias.
Use inclusive language for demographic questions. Ask about gender with multiple options, use ranges for age and income, differentiate race and ethnicity, and prioritize respectful, person-first language for disabilities.
Convenience sampling is quick and cost-effective for UX research, but probability sampling is better when you need representative and generalizable data.
Surveys are great for gathering quantitative, self-reported data but shouldn't replace behavioral methods like usability testing, qualitative interviews, or data you already have through analytics.
Test your survey with three methods: Cognitive Walkthroughs check question clarity with target users, Mechanical Tests ensure software functions like skip logic, and Usability Tests observe real user interaction.
Pair qualitative methods with quantitative metrics like the System Usability Scale (SUS) for benchmarking and the Single Ease Question (SEQ) for task-level insights. Use both carefully with qualitative data.
Explore the difference between attitudinal and behavioral UX research. Learn how combining these methods offers a complete view of user interactions and perceptions.
Well-written screeners ensure that your study participants are appropriate for your research goals, improve data quality, save resources, and reduce bias.
Researchers often want to ask about sensitive topics in surveys and screeners. Handle them appropriately and delicately to avoid dropoffs and inaccurate data.
