Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 26, 2024
Date Accepted: Jan 25, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Strengthening Data Integrity in Web-Based Research: Lessons From Verifying and Validating Participant Data In An Online Research Study With Black Cisgender Women
ABSTRACT
Background:
Online research studies offer one way to engage more Black women in health-related research. However, the widespread use of web-based data collection for online studies poses a high risk of fraudulent responses and raises concerns about data integrity, particularly when online recruitment efforts include a standard link and offer monetary incentives.
Objective:
The purpose of this study was to assess the strengths and limitations of fraud deterrent and fraud detection procedures implemented in an incentivized, cross-sectional, online study about HIV prevention and sexual health with Black, cisgender women living in Texas.
Methods:
Data for the current study came from a cross-sectional web-based survey that examined factors associated with potential PrEP use among a convenience sample of adult, Black cisgender women from three metropolitan areas in Texas. Each eligibility screener and associated survey entry was evaluated by 4 fraud deterrent features and 7 fraud detection benchmarks with corresponding decision rules.
Results:
Among the 5862 individuals who provided consent and initiated eligibility screener, there were 155 respondents who consented, were eligible, passed fraud deterrent and fraud detection, and were enrolled into the online study. In this paper we discuss lessons learned and recommendations for leveraging available features in survey software programs to help deter ‘bots’ and enhancing fraud detection procedures beyond relying on survey software options.
Conclusions:
Effectively identifying fraudulent responses in web-based surveys is an ongoing challenge. The data validation approach used in the current study establishes a robust protocol for identifying genuine participants, thereby contributing to the removal of false data from study findings. By sharing experiences and implementing thorough fraud deterrent and detection protocols, researchers can maintain data validity and contribute to best practices in web-based research.
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