Screening
The process of evaluating potential research participants against eligibility criteria before they enter a study. Good screening protects data quality; bad screening wastes everyone's time.
Definition: The process of evaluating potential research participants against eligibility criteria before they enter a study. Good screening protects data quality; bad screening wastes everyone's time.
Screening is the gatekeeper between your recruiting pool and your actual study participants. A screening questionnaire determines whether someone meets your criteria—demographics, behaviors, experience levels, or product usage—before you invest time in a session.
What Makes Good Screening
- Disguise your target: Do not ask "Are you a project manager?" when that is your screener criterion. Ask "Which of the following best describes your role?" with multiple options. Leading questions attract people who game their answers to qualify
- Verify, do not trust: Self-reported expertise is unreliable. Include knowledge checks or behavioral questions that only genuine users of your target product or domain can answer
- Keep it short: Every additional question lowers your completion rate. Focus on criteria that actually matter for your research question
The Screening-Incidence Connection
Every screening criterion you add lowers your incidence rate. Requiring "uses Figma daily AND manages a team of 5+ AND is based in DACH" might describe your ideal participant, but it creates a needle-in-a-haystack problem for recruiting.
The Cost of Bad Screening
A participant who slips through screening and does not match your target profile is worse than an empty slot. Their data introduces noise, and you often cannot tell until analysis that something is off.
Related Terms
Incidence Rate
The percentage of people who respond to a recruitment invitation that actually qualify for your study based on screening criteria. A low incidence rate means most respondents will be screened out.
Recruiting
Finding and enrolling qualified participants for a research study. The single biggest bottleneck in applied research—and the one most teams underestimate.
Sampling Bias
A systematic error introduced when your research sample does not represent the population you are trying to study. The most common and most overlooked threat to research validity.