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Preventing and Protecting Against Internet Research Fraud in Anonymous Web-Based Research: Protocol for the
Kris L Hohn1, April A Braswell2, James M DeVita3
1College of Health and Human Services, School of Social Work, University of North Carolina Wilmington, Wilmington, NC, United States.
JMIR Research Protocols
|September 12, 2022
Summary
A novel Data Integrity Plan (DIP) protocol effectively safeguards anonymous web-based research data. Implementing this plan ensures reliable findings and protects participant privacy in online studies.
Area of Science:
- Online research methodologies
- Data security in digital health studies
Background:
- Maintaining data integrity is crucial for human participant safety, privacy, and research validity in internet studies.
- Fraudulent respondent activity poses a significant risk to web-based research, especially when using anonymous recruitment.
- Safeguarding data is essential when recruiting hidden or expanded populations anonymously online.
Purpose of the Study:
- To introduce a novel Data Integrity Plan (DIP) protocol for securing data during anonymous web-based collection.
- To provide a structured approach for managing data integrity in online research environments.
Main Methods:
- The study outlines a protocol for developing and implementing a specific DIP.
- The DIP addresses fraudulent activity through four key steps: risk definition, protocol planning, secure data collection/recruitment, and enrollment determination.
- This approach was applied to a large-scale mixed methods study launched in April 2021.
Main Results:
- Preliminary analyses after implementing the DIP protocol showed no instances of fraudulent activity.
- A pre-post analysis is ongoing to assess the DIP strategies' effectiveness from February 2022 to May 2023.
Conclusions:
- The DIP protocol implementation can conserve research resources and improve efficiency.
- It offers a systematic method for critical data examination, leading to more rigorous health research findings.
- Adopting the DIP protocol enhances the reliability and trustworthiness of data in online health studies.
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