Knowing that 80 people submitted a survey does not tell you the response rate. You also need to define who was invited, how many invitations count in the denominator, and which submissions qualify as valid responses.
A practical operational formula is valid responses ÷ people invited × 100. Delivery failures, ineligible contacts, partial responses, and duplicate submissions can change the result. Define those rules before calculating, and keep them consistent across reporting periods. The examples below are fictional and intended for ordinary customer or event surveys.
Start with the formula and counting rules
Response rate (%) = valid responses ÷ number of people invited × 100
If 200 people are invited and 80 submit valid responses, the rate is 80 ÷ 200 × 100 = 40%. The formula is simple; the definition of each count is the part that needs care.
For email invitations, decide whether the denominator is invitations sent or invitations confirmed as delivered. If you use sent invitations, label the metric “sent-based.” If delivery is verifiable and you use only delivered invitations, label it “delivered-based.” Keep sent, failed, and completed counts separately so the denominator does not change quietly after results arrive.
Define the denominator from the intended audience
Describe the eligible audience in one sentence, such as “people registered for the October event” or “customers whose orders shipped in October.” Then count the people who meet that definition, remove duplicates using a stated rule, and record how the invitation list was created.
| Possible denominator | Includes | Useful when |
|---|---|---|
| Invitations sent | Everyone in the send operation, including some failed deliveries | Tracking the outcome of a mailing operation |
| Invitations delivered | People whose invitation delivery is confirmed | Delivery logs are available and relevant |
| Eligible cases | People confirmed to meet the survey criteria | Ineligible or unknown-eligibility cases must be handled separately |
There is no universally correct choice for every survey. State which group the denominator represents and apply the same rule next time.
Make the target definition operational
Avoid a target description that cannot be reproduced. “Our customers” could mean anyone who has ever bought from the company, people who bought during a particular period, or active account holders. Write the inclusion period, the unit being counted, and the source list. If the unit is a person, count people rather than email messages. If the unit is a household, business, school, or event registration, state that instead.
Also decide how to handle cases whose eligibility cannot be checked. A mailing list may contain former customers, shared inboxes, or records without a usable address. Do not silently exclude uncertain cases after seeing the result. Keep them in a separate status and explain whether your operational denominator includes them. Formal sample surveys have specific procedures for estimating unknown eligibility; AAPOR's standardized definitions are more appropriate than an improvised adjustment in that setting.
These choices can be written as a short rule before data collection:
Include one record for each person registered for the event by October 1. Exclude cancelled registrations and duplicate records before sending. Keep delivery failures as a separate count. Count one valid response per eligible person, submitted before October 10.
This note makes the resulting rate easier for another person to reproduce. It also reveals when a rate should not be compared with an earlier survey that had a different target group or unit.
An open link shared publicly creates a different problem: you may not know how many eligible people were invited. Dividing submissions by page views does not usually produce a response rate among invitees. Visitors may not be eligible, and one person may visit more than once. If the invitation denominator is unavailable, report the number of responses and say that the response rate could not be calculated.
Decide what counts as a valid response
Before tallying, state whether a response counts when someone starts the survey, submits the final page, or answers a required set of questions. Keep partial completions in a separate count unless your stated rule includes them. Also define how you handle duplicate submissions and people who do not meet the target criteria.
For an anonymous survey, do not imply that duplicates can always be identified without collecting identifying information. Record that limitation. The numerator should describe what the collected data can actually support.
It helps to separate the path from invitation to usable response into statuses. For example, an invitation can be sent, delivered, opened, started, partially completed, and finally submitted. Those counts describe different stages. An email open is not evidence that the intended person read the invitation; a page visit is not evidence that a respondent belongs to the sample. Use the events your system actually records, and label them precisely.
Likewise, a person who started but did not submit can be useful to study separately, but should not be mixed into the numerator by accident. If partial answers are valuable for the analysis, decide whether to report a second measure, such as a completion rate among people who started. Keep both measures' denominators visible. This avoids making a change in the completion rule look like a change in participation.
Do not turn submissions into people without a rule
Suppose 200 people were invited and the form received 85 submissions, but several may be repeats. The calculation 85 ÷ 200 = 42.5% is a submission-to-invitation ratio. It is not necessarily the percentage of invitees who responded. To report the latter, the numerator needs to count distinct eligible invitees, and the method for identifying a repeat must be known.
In a named invitation, a unique invitation token or an authenticated respondent account may support that count, if appropriate for the survey and disclosed to participants. In an anonymous, openly shared form, such a link may not exist. Do not add personal data solely to force a deduplication step without considering whether it is necessary. Instead, report the count you can verify, state its limits, and avoid claims about unique participants that the data cannot support. If submissions exceed the invitation count, inspect forwarded links, multiple submissions, list units, and eligibility before interpreting the rate.
The simple operational formula above is not a substitute for a standardized outcome rate in academic, public-opinion, or probability-sample research. Those settings may require classifying final dispositions, eligibility, partial interviews, and cases with unknown eligibility. AAPOR provides multiple standardized formulas and guidance on these cases. Use its Response Rates Calculator and Standard Definitions when that standard applies.
Work through a fictional example
Suppose an event organizer invites 200 registered attendees. Ten invitations fail, 80 people submit valid responses, and five leave the survey unfinished.
| Rule | Calculation | Result |
|---|---|---|
| Use everyone invited as the denominator | 80 ÷ 200 × 100 | 40% |
| Exclude the ten failed invitations | 80 ÷ (200 − 10) × 100 | About 42.1% |
| Count partial submissions as valid | 85 ÷ 200 × 100 | 42.5% |
Each figure can be a useful operational measure if its definition is shown. Switching formulas midway through a report makes it difficult to tell whether the rate changed because of participation or because the counting rule changed. Record the selected formula and collection period next to the result.
In practice, the denominator may be fixed before invitations go out, while delivery outcomes arrive later. Keep a small disposition table rather than deleting failed records from the source list. In this fictional example, 200 eligible people were invited:
| Exclusive disposition | Count in this example |
|---|---|
| Delivery failure; no response received | 10 |
| Valid completed responses | 80 |
| Partial responses; no valid completion | 5 |
| Delivered invitation; no submission recorded | 105 |
These rows are mutually exclusive dispositions for this fictional example: 10 delivery failures, 80 valid completions, five partial responses, and 105 delivered invitations with no submission. The 200 invited people therefore divide into 10 undelivered and 190 delivered. With an invited denominator, 200 minus 80 completed submissions leaves 120 without a valid completion. With a delivered denominator, 190 minus 80 leaves 110 without a valid completion among confirmed deliveries. In a real system, statuses such as delivery failure and no response may overlap; do not add them as exclusive groups unless the data model supports that. Describe the actual counting rule.
An event survey may have another denominator problem: one registration can cover several attendees, or someone may register and not attend. Decide whether the survey is about registrants, attendees, or accounts before counting. The numerator must use the same unit. If one response is allowed per registration but an organization sent invitations to each attendee, document that distinction rather than putting both counts into one percentage.
Statistics Canada's archived glossary defines response rate as the proportion of a sample for which a questionnaire response is obtained. It also notes that nonresponse includes people who refuse and people who cannot be reached. See the Statistics Canada glossary. For any particular study, the sampling frame and eligibility rules still determine the appropriate denominator.
A response rate alone does not measure survey quality
A higher rate does not automatically mean more accurate results. Who was invited, who responded, how questions were worded, and whether important groups are missing can also affect interpretation.
AAPOR explains that response-rate information alone cannot establish how much nonresponse error exists, or whether it exists. Calculating the rate is a first step in understanding response patterns, not a quality score. If coverage or nonresponse bias is a concern, examine who was invited and who responded, and use methods suited to the study.
It is tempting to look for one “good” percentage. Rates are hard to compare when audiences, invitation methods, survey topics, deadlines, or counting rules differ. Compare first with earlier surveys that used the same definitions. If something changed, note it beside the result rather than treating the percentage as a universal pass/fail threshold.
If a rate falls, use it as a signal to investigate, not as an explanation by itself. Start by checking whether the number invited changed, whether delivery failures increased, and whether the collection window or eligibility rule changed. Then check whether the invitation reached the intended group and whether the survey could be completed on the devices and channels that group uses. A difference in the denominator can move the percentage even when the number of submissions stays constant.
If it is appropriate and safe to do so, compare response patterns across invitation channels or broad groups that were defined in advance. Do not publish small cells that could expose individual participants, especially when the survey is described as anonymous. A response rate by subgroup can identify where collection records differ, but it does not explain why a person did or did not respond. Follow-up research or operational logs may be needed to investigate the reason.
Where the survey supports decisions about a wider population, rate monitoring is only one part of the method. Check the sampling frame, coverage, question wording, weighting or adjustment procedures where relevant, and the possible differences between respondents and nonrespondents. Those methods depend on the design; a simple customer feedback survey should not be presented as a representative probability sample without a basis for that claim.
Keep a record that supports comparison
Use a small reporting note for each survey:
| Item | Fictional example |
|---|---|
| Audience | October event attendees |
| People invited | 200 |
| Sent / failed invitations | 200 / 10 |
| Collection period | October 1–10 |
| Valid-response rule | Completed submission from an eligible attendee |
| Valid / partial responses | 80 / 5 |
| Denominator and formula | Delivered 190; 80 ÷ 190 × 100 = about 42.1% |
| Changes or limitations | Invitation text changed; anonymous duplicates could not be checked |
Keeping the formula, audience, and period together makes future comparisons easier to interpret. For use-case form planning, see the form creation hub. For general question design, see our survey question design guide. For creating and organizing responses in FORMLOVA, see the survey form and analysis guide. The internal survey guide covers employee privacy and follow-up, the anonymous survey guide covers re-identification risks, and the form completion guide addresses abandonment within a form. You can get started with FORMLOVA if you need to create a survey form.
When publishing a report, retain the counts behind the percentage and choose one display precision, such as one decimal place. If one report shows 35% and another shows 42%, the difference is 7 percentage points. Saying “up 7%” can mean a relative increase and therefore be read differently. Prefer “up 7 percentage points,” and show the original numerator and denominator when the audience needs to verify the calculation.
Compare collection periods consistently as well. A cumulative rate from launch to deadline is not directly comparable with a rate measured after the first three days. A survey that sends one invitation and another that uses several reminders also have different collection conditions. If interim results are shared, freeze both numerator and denominator at the same timestamp and label the result provisional. Recalculate the final rate after the collection window closes using the original rule.
Before publishing the survey or reporting results
- State who is eligible to respond.
- Label the denominator as sent, delivered, or eligible invitations.
- Define valid, partial, duplicate, and ineligible responses.
- Keep delivery failures separate from completed responses.
- Confirm whether an open link provides a usable invitation count.
- Record changes from earlier surveys beside the rate.
- Avoid treating response rate alone as proof of low bias or high quality.
Sources
- AAPOR: Response Rates Calculator
- AAPOR: Standard Definitions
- Statistics Canada: Appendix D. Glossary of survey terms
Disclosure and Verification
The AAPOR calculator, AAPOR standard definitions, and the archived Statistics Canada glossary were checked on October 7, 2026. All numerical examples are fictional operational examples. The simple formula is for documenting ordinary distributed surveys; it is not a substitute for standardized rates or quality assessment in probability-sample research.
