If you suspect that a research submission is ineligible, duplicated, automated, or fabricated, preserve the relevant records, review several independent indicators, and consult your IRB and institutional research integrity or compliance office before excluding responses, changing procedures, or withholding compensation. A suspicious response is a reason to investigate—not proof of fraud.
First, preserve the concern without turning it into a conclusion
Keep the relevant survey records, timestamps, recruitment-source details, and other information permitted by your approved retention and access procedures. Write down what you observed—such as an apparent duplicate or an eligibility inconsistency—and keep that separate from conclusions about intent. Follow your institution’s data-management plan and policies; there is no single evidence-preservation checklist that applies to every study.
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Online survey contamination can affect how results should be interpreted and may create financial loss when participation is compensated. That risk warrants a careful review, not an automatic finding that a participant acted fraudulently.
Review multiple indicators in context
Johns Hopkins’ guidance for online survey instruments describes a range of possible checks, including eligibility screening, unique or one-time links, response limits, bot-detection or CAPTCHA features, duplicate identifiers, completion times, unusual response patterns, and qualitative consistency checks. Use signals in combination and in light of the study design; none establishes fraud on its own.
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- Eligibility and access: Check whether the submission passed the study’s approved screening and whether a link intended for one participant may have been reused.
- Duplicate and timing patterns: Look for repeated identifiers or submissions, unusually fast completion, and outlying patterns. These are prompts for human review, not automatic grounds for exclusion.
- Response consistency: Review relevant open-text answers and whether responses fit together. Unusual answers may have explanations unrelated to fraud.
- Network or location clues: Treat IP or geolocation information cautiously. People may share a household, workplace, institution, or library network; VPN use can also make geolocation misleading. IP addresses may be sensitive personal information.
- Automation controls: CAPTCHA and platform bot-detection features can help, but their effectiveness varies and they may create accessibility barriers.
Johns Hopkins’ survey-instrument fraud-prevention guidance (version January 27, 2025) cautions that additional controls can introduce bias or other undesirable outcomes. Controls may deter privacy-conscious, less tech-savvy, low-literacy, or disabled participants, so consider whether a flag reflects the target population or study setup rather than misconduct.
Check what your approved study allows before acting
Before excluding a record, withholding or delaying payment, collecting additional identifiers, or changing screening, check the consent language, IRB-approved protocol, privacy protections, compensation plan, and institutional policy. Ask the IRB and appropriate institutional officials how to handle the particular case if the approved documents do not clearly answer it. Do not add a new identity check or alter participant-facing procedures on your own.
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Johns Hopkins discusses sample consent language for verification and possible payment consequences, and advises reserving additional identifying information such as a mailing address for high-risk situations. Any collection of extra information or change in payment handling should be reviewed for consistency with the study’s approvals and applicable rules.
Choose controls by balancing detection and participant burden
Prevention is most defensible when controls are proportionate to the study’s risk and fit its approved recruitment and consent design. Compare potential detection value with false-positive risk, privacy impact, accessibility, participant burden, cost, and fit with the target population.
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| Control | Potential use | Limits and tradeoffs |
|---|---|---|
| Eligibility screening and unique or one-time links | Restrict access to eligible invited participants and reduce link sharing or reuse. | Requires setup and adds participant burden; it must fit the recruitment and consent design. |
| CAPTCHA or platform bot detection | Help reduce automated entries. | Effectiveness varies; consider accessibility and changing technology. |
| Duplicate, timing, outlier, and qualitative checks | Flag patterns for human review. | Flags are not proof; fast completion, unusual answers, or repetition may have other explanations. |
| IP or location review | Check broad geographic consistency or repeated submissions. | Shared networks can create false positives, VPNs complicate geolocation, and IP addresses may be sensitive personal information. |
| Stronger identity or address verification | May be appropriate for high-risk recruitment or unusually valuable incentives. | Increases privacy burden; Johns Hopkins suggests address collection only in high-risk cases. |
| Delayed or conditional incentive processing | Allows time for review before payment. | Must be clear in participant communications and align with approved compensation procedures. |
Escalate through the right institutional channel
Contact your IRB and your institution’s research integrity or compliance office for the applicable process. The right route depends on the institution, funder, jurisdiction, and study type; there is no universal reporting deadline for every concern about a participant’s submission.
In the United States, NIH’s process for handling research-misconduct allegations describes intake, assessment, referral, and institutional inquiry. NIH says allegations involving human research participants may also be referred to the Office for Human Research Protections (OHRP). That NIH process is not a universal reporting rule for every study or participant concern.
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For applicable FDA-regulated drug, biological product, or device investigations, FDA investigator guidance addresses investigator supervision and protecting study subjects’ rights, safety, and welfare. See the FDA’s Investigator Responsibilities guidance (October 2009) and consult your institution about how it applies.
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NIH defines research misconduct as “fabricating, falsifying, and/or plagiarizing in proposing, performing, or reviewing research, or in reporting research results. It does not include honest error or differences of opinion.” The definition and examples on NIH’s What Is Research Misconduct page (last updated August 19, 2024) concern conduct in research. A participant’s suspicious submission may compromise data without, by itself, establishing that an investigator committed research misconduct. NIH gives the example of a coordinator creating data for nonexistent participants as fabrication by research personnel; that is distinct from a participant submitting questionable data.
Document decisions and improve future prevention
Keep a record, consistent with confidentiality rules and institutional policy, of the signals reviewed, the decision about each affected record, any compensation or reporting decision, and any prevention changes. For future studies, layered controls paired with ongoing human review are more informative than reliance on a single automated test. A 2015 peer-reviewed article on online research fraud discusses the limits of individual methods and the value of multiple approaches; it is scholarly context, not a binding requirement. See Teitcher et al., “Fraudsters, Deception, and the Integrity of Online Research”.
Participant protection remains central: NIH’s Guiding Principles for Ethical Research (last reviewed June 10, 2025) describes informed, voluntary participation. Controls that affect access, privacy, or payment should be considered alongside the study’s ethical and approved procedures.
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