01 · The Question
Should you estimate how many people will qualify before you commit?
You have defined the population, written your inclusion and exclusion criteria, and determined approximately how many participants the study needs. There is still an awkward question: what proportion of the people you could approach will actually qualify?
Researchers sometimes postpone that question until recruitment begins. That may be reasonable when eligibility is obvious and nearly everyone in the accessible population is expected to qualify. It becomes much riskier when several criteria must be satisfied simultaneously, the relevant characteristics are uncommon, or little is known about the population.
An eligibility estimate will rarely tell you exactly how many participants you will find. Its purpose is more practical: to determine whether the study remains plausible under reasonable assumptions before substantial time, approvals, funding, or other resources are committed.
02 · The Short Answer
Estimate eligibility when it could determine whether the study is feasible
In Brief
Yes, you should usually estimate participant eligibility before committing to a study when uncertainty about who will qualify could materially affect whether you can obtain the required sample.
The estimate does not need to predict recruitment perfectly. Its purpose is to replace an unexamined assumption with a defensible range based on available records, previous studies, site information, preliminary screening, or other relevant evidence.
03 · What You Need to Know
Eligibility estimation is an early feasibility check, not a promise about recruitment
What exactly are you estimating?
An eligibility estimate concerns the proportion of people entering an appropriate screening pool who are expected to satisfy the study's eligibility criteria. In recruitment research, the eligibility fraction is commonly defined as the number eligible divided by the number screened.
Before recruitment, you usually do not have an observed eligibility fraction for your exact study. You are therefore estimating it from other evidence. The usefulness of that estimate depends heavily on whether the evidence resembles your intended population, settings, eligibility criteria, and recruitment pathway.
Why estimate eligibility before the study begins?
The calculation works backward from the sample you need toward the number of people who may need to enter screening. If only a small proportion of potentially available people are expected to qualify, a study that appears straightforward on the basis of population size can become difficult very quickly.
This is why determining whether enough eligible participants actually exist should not be postponed automatically until recruitment. Eligibility is one of the assumptions supporting your recruitment plan.
Published recruitment research illustrates why the distinction matters. Eligibility fractions can vary considerably among studies and settings, and the number screened may be substantially larger than the number ultimately enrolled. That variation makes a generic eligibility percentage a poor substitute for study-specific evidence.
When is an eligibility estimate particularly important?
Estimating eligibility becomes more consequential as your criteria become more restrictive. A study requiring several characteristics simultaneously may have a much smaller qualifying pool than the broader population suggests.
The same applies when the relevant characteristic is uncommon, when recruitment depends on a narrow clinical or educational subgroup, or when you have only a modest number of potentially available people. In studies of rare or hard-to-reach populations , even a modest error in the assumed eligibility rate may have substantial consequences.
An estimate is also useful when the proposed study needs a large sample relative to the available pool, when recruitment must be completed within a short period, or when obtaining additional sites later would be difficult.
Where can an eligibility estimate come from?
The strongest source depends on the study. Aggregate institutional records may indicate how many people satisfy readily recorded criteria. A hospital or registry may be able to provide an authorized feasibility count. Previous studies may report how many people were screened and how many were eligible. An existing program may have administrative information relevant to the proposed criteria.
When access to such information depends on another organization, it may be sensible to contact potential sites or data providers before finalizing the research question . What can be shared before formal approval will vary, so preliminary feasibility inquiries should respect institutional and ethical requirements.
When direct evidence is unavailable, you can sometimes combine defensible information about the major criteria to establish a plausible range. This requires caution. Criteria may be correlated, and multiplying separate prevalence estimates as though they were independent can produce a misleading result.
Use a range when precision would be artificial
Suppose you know that 800 people could plausibly enter screening, but the available evidence cannot tell you whether 25%, 35%, or 45% will qualify. Reporting an estimated eligibility rate of exactly 34% would imply more knowledge than you possess.
A more useful approach is to examine several plausible scenarios:
Assumed eligibility
Potentially screened
Estimated eligible
25%
800
200
35%
800
280
45%
800
360
If your study requires 250 enrolled participants, the difference between these scenarios is not cosmetic. Under the 25% assumption, there would not even be 250 eligible people. Under the other scenarios, enough people might qualify, but enrollment would still depend on whether eligible individuals can be reached and are willing to participate.
Eligibility is only one conversion in the recruitment pathway
A common planning mistake is to calculate the expected number eligible and stop there. Eligibility does not equal enrollment.
Potential pool People who could plausibly enter the recruitment pathway.
Screened People whose eligibility is actually assessed.
Eligible People who satisfy the study criteria.
Enrolled Eligible people who ultimately enter the study.
Recruitment research distinguishes the eligibility fraction from the enrollment fraction, which is the proportion of eligible individuals who actually enroll. A favorable eligibility estimate therefore cannot answer whether people will actually agree to participate .
Do not treat a borrowed eligibility rate as a universal constant
Previous studies can provide useful evidence, but their eligibility fractions belong to particular populations and recruitment processes. A study conducted in a specialist clinic may screen a very different population from a community-based study. A study using broad criteria may provide little guidance for a protocol with several restrictions.
Before borrowing an eligibility estimate, compare the source study with your proposed study. Consider the population, setting, recruitment source, time period, and criteria used. The less comparable they are, the wider your uncertainty should probably be.
04 · A Practical Example
Estimating eligibility before a student commits to recruitment
Hypothetical Example
A thesis requiring teachers with a specific combination of experience
A graduate student needs approximately 150 completed survey responses from teachers who have used generative AI for instructional planning for at least one academic year. The cooperating schools collectively employ 1,100 teachers.
Identify the realistic screening pool School-level information suggests that approximately 700 teachers could realistically receive the recruitment invitation during the planned period.
Estimate eligibility Preliminary aggregate information suggests that perhaps 25% to 40% may satisfy the required duration and type of AI use.
Calculate the range The estimate produces approximately 175 to 280 eligible teachers.
Compare with the requirement The study needs 150 completed participants. Even the optimistic estimate does not leave an enormous margin once non-participation and incomplete responses are considered.
Act before committing The student investigates whether additional schools can be included and whether the one-year requirement is scientifically necessary before finalizing the protocol.
The estimate did not prove that recruitment would fail. It exposed a recruitment assumption that deserved attention while the design could still be changed relatively easily.
06 · What This Means for You
Match the effort of estimation to the risk of being wrong
Not every study needs an elaborate preliminary eligibility investigation. The amount of work should reflect the consequences of uncertainty.
A simple decision framework
If eligibility is broad, obvious, and well documented
A simple evidence-based estimate may be sufficient.
If several restrictive criteria must occur together
Estimate the combined eligibility rate rather than relying on the size of the broader population.
If available evidence is weak but the eligible pool appears comfortably large
Use a conservative range and document the uncertainty.
If feasibility changes dramatically across plausible eligibility assumptions
Obtain better evidence or test the assumption before committing to the full study.
The principle is straightforward: uncertainty matters most when the study's success depends on the uncertain assumption being favorable. If your study remains feasible even under a conservative eligibility estimate, additional precision may add little. If the entire design works only under the most optimistic estimate, the uncertainty deserves investigation.
07 · A Quick Checklist
Before relying on an eligibility estimate, check these
Before committing to the estimate, check:
Define the inclusion and exclusion criteria clearly.
Identify the population that could realistically enter screening.
Look for evidence about how many people satisfy the criteria together.
Prefer local or closely comparable evidence when available.
Use a plausible range when the evidence does not justify a precise percentage.
Test whether the study remains feasible under a conservative eligibility assumption.
Keep eligibility separate from willingness and actual enrollment.
Investigate further when the study succeeds only under an optimistic estimate.
09 · The Bottom Line
Estimate eligibility when your study depends on it
The Bottom Line
You should estimate participant eligibility before committing when uncertainty about how many people will qualify could materially change whether the proposed study is feasible.
You rarely need a perfect forecast. A defensible range based on relevant evidence is often enough to reveal whether the study is robust to uncertainty or depends on an optimistic assumption that should be investigated before recruitment begins.
11 · Cite this Guide
How to Cite This Guide
This guide is intended to be read, shared, and used in research, teaching, and academic work. If you draw on its ideas, explanations, or other content, please acknowledge the source by citing the guide. Doing so gives appropriate credit and helps your readers locate the original resource.
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