Moving Beyond Red Flags for Imposter Participants: A Novel Screening Strategy to Enhance Data Integrity

Abstract

Health research guides health practice and policy; therefore, data integrity is of the utmost importance. The use of incentives to compensate respondents for their discomfort and inconvenience in sharing their lived experiences has unfortunately drawn imposter participants, posing significant threats to the trustworthiness and transferability of qualitative data. A growing literature provides “red flags” and methods for identifying imposters, such as inconsistent narratives, suspicious IP addresses, and duplicate submission, but little systematic guidance exists for how to determine which data to discard and what to keep once one suspects imposters provided data. Drawing on a study where imposters were suspected early in the process of interviewing, despite many precautions taken, we propose a structured data verification strategy. This strategy involves retrospectively contacting previously interviewed participants to confirm their authenticity without soliciting additional private health information. Further, we suggest how this same method can be used proactively as part of an enhanced screening process just prior to officially starting the interview as a way of further enhancing data integrity.