Detecting and Preventing Imposter Participants: Methods and Recommendations for Qualitative Researchers

Area of Science:

• Social Sciences
• Qualitative Research Methods
• Digital Research

Background:

• The COVID-19 pandemic necessitated a shift from traditional face-to-face qualitative data collection to remote and digital methods.
• This transition introduced challenges, including the emergence of 'imposter participants' who misrepresent themselves to join studies.

Purpose of the Study:

• To identify and categorize red flags associated with imposter participants in qualitative research.
• To propose methods and recommendations for detecting and preventing imposter participants.
• To address the structural challenges posed by imposter participants at an institutional level.

Main Methods:

• Reflection on two qualitative research case studies involving remote data collection.
• Identification and categorization of red flags during the recruitment and data collection phases.
• Development of strategies for detecting and preventing imposter participants.

Main Results:

• Several red flags were identified, categorized by research phase (recruitment and data collection).
• Methods and recommendations were developed to mitigate the impact of imposter participants on research validity.
• The study highlights the need for institutional attention to structural problems related to imposter participants.

Conclusions:

• Implementing recommended strategies is crucial for maintaining the integrity of qualitative research, especially with increasing reliance on digital recruitment.
• Addressing the issue of imposter participants requires a systemic approach involving academia, institutional review boards, publishers, and reviewers.