October 10, 2025

Better Screeners, Better Results (With Examples) 

A screener is the first line of defence against bad data. When they are poorly designed, the entire study is impacted.

Overly complex or ambiguous questions can slow recruitment, inflate costs, and frustrate respondents who have already invested their time. Worse still, they can allow the wrong participants in, or exclude the right ones, undermining the very business questions the research set out to answer. Well-crafted screeners, on the other hand, sharpen targeting, deliver cleaner data sets, and respect the time of busy healthcare professionals. They also support respondent retention, which is critical for long-term research success.

In this article, drawing on insights from Kristin Vollrath, M3 Global Research’s Global Director of Quantitative Research, we explore the most common pitfalls in screener design, and highlight practical ways to build better screeners that set projects up for success.

Common Pitfalls in Screener Design

1. Too Long or Too Complex

The most frequent challenge we see is length. Healthcare professionals are pressed for time, and long or repetitive screeners often lead to frustration and dropouts. A good screener should stay focused on essential qualification criteria, keeping non-qualifying demographic questions for the main survey. As a rule of thumb, screeners should not exceed 12 questions, with ten as the optimal length for quantitative research and fifteen for qualitative.

When screeners do take longer than expected, offering a small screen-out fee acknowledges the respondent’s effort and helps maintain engagement. While the exact amount varies, the principle is consistent: valuing participants’ time leads to higher satisfaction and stronger retention.

Complexity also creates unnecessary barriers. Grids, repeated questions, and open-ended items that cannot drive routing logic are better suited to the main survey. Likewise, demanding exact patient counts or excessive detail slows respondents down and can result in poor quality answers. Simplicity is powerful: using ranges instead of exact numbers, writing in clear and familiar clinical language, and avoiding unnecessary repetition all help respondents qualify quickly and accurately.

2. Overly Strict Criteria

Another frequent pitfall is criteria that are set too narrowly. While the intention is usually to capture only the most relevant respondents, criteria that are overly strict can backfire. Respondents who invest several minutes answering questions only to be screened out at the final stage are left with a poor experience, which can discourage future participation. From a project perspective, strict cut-offs can also limit feasibility, slow recruitment, and inflate costs.
A more effective approach is to define clear qualification thresholds while also considering where flexibility could be allowed. For example, instead of requiring an exact patient volume, offering ranges gives more breathing room while still ensuring quality. Addressing these risks early makes it easier to adjust targeting if needed and helps avoid costly delays once the study is in field.

3. Making Assumptions

One of the most common pitfalls in screener design is assuming respondent behaviour. When questions assume that a healthcare professional already uses a specific technology or treatment, for example, respondents may feel under pressure to select an option that does not reflect their reality. This not only creates frustration but also risks skewing the data from the very start. A more effective approach is to confirm usage first, then move into more detailed questions.

Less Effective

For which of the following procedures do you use robotic technology? (Select all that apply) 

  • Total knee arthroplasty 
  • Partial knee arthroplasty 
  • Total hip arthroplasty 
  • Shoulder arthroplasty 
  • Spine procedures 

Better

Do you currently use robotics or surgical navigation for any procedures?

  • Robotics
  • Surgical navigation
  • Neither

Follow-up: In approximately what percentage of the following procedures do you use robotic technology?

  • Total knee arthroplasty
  • Partial knee arthroplasty
  • Total hip arthroplasty
  • Shoulder arthroplasty
  • Spine procedures

4. Subjectivity

Subjectivity arises when screeners rely on vague frequency scales. Words such as “somewhat frequently” or “occasionally” are open to interpretation, and different respondents will understand them differently. This lack of precision can create inconsistent data and makes it harder to adjust quotas or incidence rates once fieldwork is underway. A better approach is to anchor questions in percentages or specific timeframes. This provides clearer, more actionable results and reduces the risk of ambiguity.

Less Effective

How frequently do you use each of the following robotic devices?

  • Very frequently
  • Somewhat frequently
  • Neither frequently nor infrequently
  • Somewhat infrequently
  • Not frequently at all

Better

In the past 90 days, what percentage of time have you used each of the following robotic surgical devices? Please ensure totals add up to 100%.

  • Device A 
  • Device B
  • Device C
  • Device D
  • Device E

5. Error Messages

Finally, error messages are often overlooked, yet they play a crucial role in user experience. When respondents encounter vague prompts such as “please complete all fields,” they may abandon the survey altogether. Clearer instructions, such as “please specify additional reasons for not using this product more frequently”, help respondents know exactly what to fix. Adding visual cues, such as highlighting missing fields, further reduces frustration and prevents unnecessary dropouts.
Conclusion
The difference between a smooth study and a problematic one often lies in the screener. When screeners are clear, concise, and focused only on what matters, they protect timelines, budgets, and respondent goodwill. When they are too long, too strict, or too vague, they create unnecessary barriers and compromise the quality of the insights that follow.
By paying close attention to common pitfalls and making small but meaningful adjustments, screeners become more than an entry point, they set the foundation for reliable data, satisfied respondents, and ultimately more successful research outcomes.

Strong studies start with strong screeners. At m360 Research, we help you build the right foundations for better data and better results. Contact us at info@m360research.com to find out how we can support your screener design.

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