How to write screener questions that actually screen

Screener questions examples, the principles behind good ones, and how to wire screen-out logic in Versive so screeners actually screen.

A screener filters out people who do not match your research criteria before they reach the rest of the study. Weak wording can admit participants whose experience does not meet those criteria. This guide covers three principles for writing screeners, examples for common research scenarios, and the screen-out logic needed to enforce them in Versive.

Three rules for a screener that actually screens

Do not telegraph the qualifying answer. A Yes or No question such as "Are you the primary decision-maker for purchasing software at your company?" reveals which answer qualifies. Some respondents may select the qualifying answer because they infer the study's purpose. Ask about the person's role in the process, or place the qualifying option among several plausible choices.

Ask about behavior, not self-declared identity. "Are you a power user of budgeting apps?" leaves "power user" open to interpretation. "In the past 30 days, about how many times did you open a budgeting app to check your spending?" asks about a specific, recent behavior. Time-bound questions produce clearer criteria than identity labels such as "expert" or "power user."

Add a red herring. Include an option that sounds plausible but does not exist or would not qualify someone, such as a made-up competitor in a multi-select list or an implausible activity. Selecting it can indicate inattention or an attempt to infer qualifying answers, giving you a defined screen-out condition.

Keep the screener short. Two to four questions is usually enough. Put it at the start of the study, and avoid stacking more than one qualifying condition into a single question because combined conditions are harder to write and debug.

Screener question examples

Adapt these examples to your product while keeping the questions behavior-based, time-bound where appropriate, and difficult to game.

1. Role and purchasing authority

"Which of these best describes your role in choosing software tools for your team?"

• I make the final decision
• I strongly influence the decision, but someone else signs off
• I use tools that other people choose for me
• I am not involved in choosing software at work

Qualifies: the first two. These options capture degrees of purchasing authority without revealing a single obvious qualifying answer.

2. Usage frequency, not self-labeled expertise

"In the past 30 days, about how many times did you use a budgeting or personal finance app?"

• Never
• 1-2 times
• 3-9 times
• 10 or more times

Qualifies: whichever range matches your study. The ranges define "power user" in terms of recent usage.

3. Recency of a specific action

"When did your company last purchase project management software?"

• Never purchased
• More than two years ago
• 6-24 months ago
• In the last 6 months

Useful for studies about recent buyers, where "have you ever bought this" is too broad to be useful.

4. Tool familiarity with a decoy

"Which of these have you used in the last month? Select all that apply."

• Slack
• Notion
• Cadenza Flow
• Asana
• None of the above

"Cadenza Flow" is not a real product. You can screen out anyone who selects it, regardless of the other options they chose.

5. Household composition, verified twice

"Do you currently have a child under 5 living in your household?"

Follow with a second question asking for the child's age range instead of repeating the Yes or No question. If the answers do not align, flag the response for exclusion.

6. Spend, in ranges

"What is your team's approximate monthly spend on project management software?"

• We do not currently pay for this category
• Under $500
• $500-$2,000
• Over $2,000

Ranges are easier to estimate than exact figures and can still separate the segments relevant to your study.

7. Company size

"How many people work at your company?"

• Just me
• 2-10
• 11-50
• 51-200
• 200 or more

Use this when your findings apply only to a specific company-size band.

8. Behavior during a specific task

"Thinking about the last time you booked a flight online, which of these did you do? Select all that apply."

• Compared prices across more than one site
• Booked directly through an airline's app or website
• Used a travel agent
• Asked a friend or family member to book for me
• None of the above

This wording grounds the answer in one concrete instance instead of asking about a general habit.

9. Attention and engagement check

"Which of the following do you do regularly? Select all that apply."

• Cook dinner at home on weekdays
• Read reviews before buying something online
• Trained for and completed an Ironman triathlon this year
• Exercise at least once a week

Selecting the implausible option can indicate that a participant is skimming or clicking without reading. Screen them out with an Includes rule on that option, using the same mechanism as the decoy in example 4.

How to wire screen-out logic in Versive

Screening requires logic that removes participants whose answers do not qualify. Put the qualifying questions first, then add a Screen out rule to each one. When an answer matches the rule, the interview ends immediately, the participant sees your customizable screen-out message, and the response is excluded from completions and insights by default.

The available condition depends on the question type. Multiple choice questions support Equals for a single option, Includes for multi-select, and Fewer than N selections. Use Includes for the red-herring pattern in examples 4 and 9, and Fewer than N selections when a respondent must choose several expected answers. Rating, NPS, star rating, and number questions support numeric comparisons such as greater than and less than. To use a numeric threshold for spend or frequency, as in examples 2 and 6, use a Number or Rating Scale question. Banded multiple choice supports only Equals, Includes, and Fewer than N selections.

If your qualifying question is open-ended, an AI question or Exploratory question can carry an AI rule instead of a fixed condition: describe the condition in plain language, such as "the participant does not mention using the product themselves," and Versive's AI evaluates each response against it. Keep AI rules specific and observable rather than interpretive; a rule like "mentions never having used the product" evaluates more reliably than "seems unfamiliar with it."

Rules run top to bottom, and the first matching rule runs. If a single screener question can disqualify someone for more than one reason, order the more specific rule first. You can chain as many rules as needed on one question. See the Logic & branching docs and Skip logic and branching, explained for other branching uses.

If you recruit through Versive's participant panel, panel projects have separate screener questions in the Recruit tab. These qualify people against your target audience before they reach the study. Apply the same three writing rules to that panel screener. See How to recruit research participants (4 ways) for a comparison of recruiting sources.

Write the screening questions first, add each screen-out rule, and preview every qualifying and disqualifying path before recruiting. For the question types available for screeners, see Survey question types, and when to use each.