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Related Concept Videos

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Exploring Methods to Mitigate Fraud in Web-Based Surveys: Multicase Study Analysis.

Madeleine Ennis1, Regina-Maria Renner1, Claudia Morando-Stokoe2

  • 1Department of Obstetrics and Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.

Journal of Medical Internet Research
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

Web-based surveys are prone to fraud, especially with incentives. This study highlights robust fraud detection methods, including personal identifiers, crucial for ensuring data integrity in online research.

Keywords:
abortiondata collectionfraudlong-acting reversible contraceptionmethodspregnancyreimbursement incentivesurveys and questionnaireswomen’s health

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Area of Science:

  • Online Research Methods
  • Data Integrity
  • Survey Methodology

Background:

  • Web-based surveys offer cost-effective reach but are vulnerable to fraud.
  • Limited literature exists on mitigating fraudulent responses in online studies.
  • Ensuring data quality requires effective fraud prevention and detection strategies.

Purpose of the Study:

  • To analyze and compare strategies for preventing, detecting, and removing fraudulent responses in web-based surveys.
  • To investigate four case studies of online surveys that experienced fraudulent responses in Australia and Canada.
  • To inform researchers and institutional review boards (IRBs) on best practices for data integrity.

Main Methods:

  • A descriptive multiple case study approach was used, examining four Australian and Canadian web-based surveys.
  • Strategies for primary (prevention), secondary (detection), and tertiary (removal) fraud mitigation were described.
  • Data collection involved recruitment of family planning service users or clinicians.

Main Results:

  • All four studies used generic links and incentives, increasing fraud risk.
  • Tertiary fraud detection strategies were implemented, removing 16%–45% of respondents.
  • Personal identifiers enhanced fraud detection, while complex algorithms and manual review were used when identifiers were not feasible.

Conclusions:

  • Web-based surveys, especially with incentives, face significant fraud risks.
  • Robust fraud detection methods are essential for reliable online survey data.
  • Adaptable fraud mitigation criteria can enhance data integrity across various survey contexts.