What Is Survey Incidence Rate and Why Does It Matter?
The survey incidence rate is the percentage of screened respondents who meet a study’s eligibility requirements. We use it to estimate how many people we need to screen to find the audience a survey requires.
If your project needs 400 completed surveys, inviting 400 people won’t necessarily get you there. Some won’t qualify. Others may qualify but leave before finishing.
For a quantitative research project, we need to account for those differences before setting a recruitment target. This starts with defining who qualifies and estimating how often we’ll find them in the sample we plan to use.
How Do We Calculate Survey Incidence Rate?
We divide the number of qualified respondents by the number of respondents screened, then multiply by 100.
(IR) Incidence rate (%) = (Qualified respondents ÷ Respondents screened) × 100
For the calculations below, we count people whose eligibility we could establish. That includes those who qualify and those who fail an eligibility requirement.
We keep people who abandon screening before we can establish eligibility outside this calculation and report them separately. People who never open an invitation don’t belong in this denominator either.
Survey Incidence Rate Calculation: A Worked Example
Suppose we need feedback from people who bought a dishwasher within the past 12 months.
In this hypothetical study, 500 people answer enough screening questions for us to confirm whether they qualify.
| Screening outcome | Number of respondents |
|---|---|
| Meet the study’s eligibility requirements | 100 |
| Do not meet the requirements | 400 |
| Total with a known screening outcome | 500 |
| Incidence rate | 20% |
The calculation is: (100 ÷ 500) × 100 = 20%
We found one qualified respondent for every five people screened.
However, we haven’t necessarily collected 100 completed interviews. Qualification tells us who can continue into the survey, not who will finish it.
Check How the Incidence Rate Is Being Calculated
Not every sample provider calculates the incidence rate in exactly the same way. Some calculate it after pre-targeting and exclude outcomes such as quota-full respondents from the IR calculation. Before we compare rates or build a fieldwork estimate, we confirm what counts in the numerator and denominator.
Two reported incidence rates are only comparable when they measure the same thing.
Why Incidence Rate Matters in Quantitative Research
A project requiring 400 qualified participants could involve screening 800 people or several thousand. The expected incidence rate changes that estimate.
It Shows How Much Screening We Need
The table below illustrates the screening required to find 400 qualified respondents at different assumed rates.
| Assumed incidence rate | Qualified respondents required | Approximate people to screen |
|---|---|---|
| 50% | 400 | 800 |
| 20% | 400 | 2,000 |
| 10% | 400 | 4,000 |
| 5% | 400 | 8,000 |
These are planning calculations, not industry benchmarks. They assume the incidence rate stays constant and do not yet account for losses after qualification.
At 20% incidence, we would expect to complete about 2,000 eligibility checks to find 400 qualified people. We would then allow for qualified respondents who don’t provide an accepted, completed response.
Suppose we expect 80% of qualified respondents to finish and pass the study’s quality checks. The estimate becomes:
Required screenings = Target accepted responses ÷ (Incidence rate × Accepted-completion proportion)
Using our assumptions: 400 ÷ (0.20 × 0.80) = 2,500 screenings
We would therefore plan for approximately 2,500 completed eligibility checks, not 2,000. Invitations would require a further allowance for people who never reach a screening outcome.
It Helps Us Assess Recruitment Costs
Lower incidence usually means more screening effort for each qualified respondent. Depending on the provider’s pricing model, that can increase the cost per completed interview. A difficult-to-reach audience may also need a longer fieldwork period.
Before agreeing to a budget, we recommend asking:
- What incidence rate does the quote assume? Check whether the provider expects a broadly recruited audience or one it has already targeted.
- What happens if the actual rate is lower? Agree how the team will review recruitment costs and the delivery schedule.
The percentage alone won’t tell us when the project will finish. We also need to know how many suitable people the provider can reach during the fieldwork window.
Is Incidence Rate the Same as Response Rate?
No. Incidence concerns eligibility. Response rate concerns participation, while completion rate tracks whether people finish after starting. These measures use different denominators.
For project planning, we keep the following measures separate:
| Measure | The question it answers |
|---|---|
| Survey incidence rate | What share of screened respondents meets the eligibility criteria? |
| Survey response rate | What share of the eligible sample participates, based on the response-rate definition being used? |
| Post-screening completion rate | What share of qualified respondents entering the main survey finishes it? |
| Usable-response yield | What share of screened respondents ultimately provides an accepted, completed response? |
We keep these measures separate because each one points to a different fieldwork problem. This separation helps us investigate the right problem. When many people qualify but few finish, we should examine what happens inside the questionnaire rather than assume we need a different audience.
What Should We Check When the Incidence Rate Is Too Low?
We start by finding out why people fail screening. Expanding recruitment before checking the screener could repeat the same problem with a larger audience.
Check Where We’re Recruiting
A broad panel may contain relatively few people who meet a specialist requirement. Targeting respondents using relevant profile information can change the proportion who qualify after entering the survey.
For a B2B research study about software purchasing, we would look for people involved in buying decisions. Recruiting anyone who works in IT would be a much broader starting point.
We also need to confirm what the provider knows about its respondents. A matching job title doesn’t answer every eligibility question.
Review Every Exclusion
We recommend separating essential requirements from details that would simply be useful to know.
Suppose the research needs feedback from recent buyers. Does “recent” mean the past four weeks, or would the past six months answer the business question?
We shouldn’t widen that window just to raise the rate. But we should ask whether the original restriction serves a research purpose.
Keep the criteria the study needs. Challenge restrictions that don’t change what we need to learn.
Test the Screening Questions
Unclear wording can produce answers we didn’t intend to measure. Faulty survey logic can also send respondents down the wrong path. Before fieldwork, we test the screening questions and verify that the survey logic works as intended. This includes checking wording, answer options and every qualification route. AAPOR’s survey guidance similarly recommends questionnaire pretesting and programming checks before launch.
Our review should cover:
- Meaning: Will respondents understand the eligibility question as we intend?
- Answer options: Can people describe their circumstances accurately?
- Routing: Does each answer lead to the correct next step?
- Exit reasons: Can we identify which requirement caused the screen-out?
Our data collection and analytics services cover survey design and scripting, including the question logic that controls these routes.
Separate Ineligibility From Other Outcomes
Someone may meet the audience criteria but arrive after we have filled their segment’s quota.
For the eligibility-based calculation used here, we wouldn’t label that person ineligible solely because the quota closed. We would record the quota outcome separately.
We would also separate duplicate entries from genuine screening failures. Otherwise, the team could mistake a data-quality problem for evidence that the target audience is difficult to find.
This follows the broader reporting principle of recording distinct survey outcomes rather than combining every unsuccessful interview into one category.
How Can We Estimate Incidence Before a Full Launch?
A small initial launch lets us check our assumptions before releasing the survey to the full sample. When the expected incidence rate is uncertain, we recommend starting with a controlled pilot. It gives us an early view of how often the target audience qualifies before the full sample is released.
We recommend using the same eligibility rules and sample source planned for the main study. Then review where respondents fail screening and whether qualified participants continue through the questionnaire.
A pilot estimate still carries uncertainty. If a small test suggests 20% incidence, we can also calculate recruitment needs at 15% to see how much room the budget allows.
We should check important segments separately, too. An overall rate may look workable while one required group remains difficult to recruit.
Testing the recruitment process before full fieldwork also gives us a chance to correct practical problems. AAPOR’s survey-testing guidance recommends piloting survey procedures rather than checking the questionnaire alone.
What Is a Good Incidence Rate in Survey?
There is no single percentage that suits every study. We need to judge the rate against the audience definition and the sample source.
A high rate among a preselected customer list doesn’t show that the same proportion of the general population would qualify. It tells us about the people we screened from that list.
Nor does a high incidence rate prove that the final sample represents the audience correctly. We still need to examine who the sample covers and whether our recruitment approach leaves relevant people out.
For planning, the useful question is whether we can recruit enough eligible respondents within the available budget and fieldwork period without changing who the study needs to represent.
Plan Recruitment Around Your Incidence Rate
A survey incidence rate calculation helps us estimate the screening effort behind a recruitment target. To plan completed interviews, we also need to account for what happens after people qualify.
At PrizmData, we connect these recruitment questions to the purpose of the quantitative research. We recommend testing the assumptions before committing to the full launch, then adjusting the plan using the screening results.
That gives your team a clearer basis for setting a realistic sample target and agreeing on the fieldwork budget.
Frequently Asked Questions
No. A low incidence rate may simply mean the target audience is narrow or difficult to reach. What matters is whether the screening criteria match the research objective and whether the sample reaches the audience the study is meant to represent.
Yes. The proportion of people who qualify can vary by country, customer segment or sample source. We therefore avoid applying one incidence-rate assumption across every market without checking whether the target audience has a similar profile.
Not automatically. We first check where respondents are screening out and whether every eligibility condition is essential to the research. We only change the criteria when the revised definition still represents the audience the study needs to understand.
