Strategies to Prevent and Detect Fraudulent Responses in Online Research: A Cautionary Tale and Tutorial

Area of Science:

• Behavior analysis
• Research methodology
• Online data collection

Background:

• Online surveys are increasingly used by behavior analysts for research.
• Fraudulent responding, especially bots, poses a significant threat to the validity of online survey data.
• Existing fraud prevention strategies are not widely adopted in behavior analysis, with limited guidance available.

Purpose of the Study:

• To provide behavior analysts with a tutorial on preventing and detecting fraudulent online survey responses.
• To outline practical steps for researchers to ensure data integrity throughout the research process.
• To emphasize the importance of data protection methods in behavior-analytic research.

Main Methods:

• The tutorial is based on prior recommendations and practical experiences with a survey distributed to behavioral providers.
• It outlines a multi-faceted approach to data protection, covering survey design, implementation, and data analysis.
• The content synthesizes strategies from other fields and adapts them for behavior-analytic research.

Main Results:

• Implementing multiple data protection methods is critical for ensuring high-quality and valid study conclusions.
• Researchers must consider fraud prevention from the initial survey design through to the final data analysis.
• Journal editors and reviewers should actively evaluate online research for robust data protection measures.

Conclusions:

• Behavior-analytic researchers must adopt comprehensive strategies to mitigate the risks of fraudulent online responding.
• Ensuring data validity through rigorous protection methods is essential for the credibility of research findings.
• A collaborative effort involving researchers, editors, and reviewers is needed to uphold the integrity of online survey research in behavior analysis.