A set of qualification questions used to filter potential research participants before a study, ensuring that those recruited match the target user profile for the research objectives. A well-designed screener prevents both over-qualification and under-qualification — the latter resulting in participants whose context is so narrow the findings don't generalize.
Common contexts
- Writing a screener that filters for weekly active users of a competing product without alerting professional survey-takers to give the 'right' answer
- Adding a behavioural question to a screener after three participants in a pilot session turned out to have no real experience with the task being studied
- Collaborating with a recruitment agency on screener criteria to ensure the participant pool reflects the product's actual demographic distribution
Use when
Write a detailed screener whenever you are recruiting participants you haven't worked with before, or when the research questions depend on participants having specific domain experience, behaviours, or life contexts that aren't visible on a panel profile.
Avoid when
Don't over-specify screener criteria to the point where the qualifying population is too narrow to recruit within your timeline — every additional criterion halves the eligible pool, and a perfectly specified screener that takes three weeks to fill defeats the purpose of timely research.
The best screeners disqualify enthusiastically — they use open-ended questions about real behaviour that professional survey respondents can't fake, rather than yes/no checkboxes that incentivize the right-sounding answer.
Real-world examples
- Airbnb's research team uses screeners to recruit participants who have booked at least two stays in the past year, excluding power users (10+ stays) and first-timers to isolate the mid-engagement behaviour they're studying.
- Healthcare.gov's usability testing screener excluded participants with insurance industry experience — a criterion that was missed in early rounds, producing misleadingly high task-success rates from atypical users.
- Nielsen Norman Group research found that screeners with more than 12 questions have a 30% higher dropout rate, suggesting that researchers must ruthlessly prioritise the 5–7 most critical qualifying criteria.