AIM against survey fraud.

Abstract

ObjectivesAlthough there exists a variety of anonymous survey software, this study aimed to develop an improved system that incentivizes responses and proactively detects fraud attempts while maintaining anonymity.Materials and MethodsThe Anonymous Incentive Method (AIM) was designed to utilize a Secure Hash Algorithm, which deterministically assigned anonymous identifiers to respondents. An anonymous raffle system was established to randomly select participants for a reward. Since the system provided participants with their unique identifiers and passwords upon survey completion, participants were able to return to the survey website, input their passwords, and receive their rewards at a later date. As a case study, the validity of this novel approach was assessed in an ongoing study on vaping in high school friendship networks.ResultsAIM successfully assigned irreversible, deterministic identifiers to survey respondents. Additionally, the particular case study used to assess the efficacy of AIM verified the deterministic aspect of the identifiers.DiscussionPotential limitations, such as scammers changing the entry used to create the identifier, are acknowledged and given practical mitigation protocols. Although AIM exhibits particular usefulness for network studies, it is compatible with a wide range of applications to help preempt survey fraud and expedite study approval.ConclusionThe improvements introduced by AIM are 2-fold: (1) duplicate responses can be filtered out while maintaining anonymity and (2) the requirement for the participant to keep their identifier and password for some time before returning to the survey website to claim a reward ensures that rewards only go to actual respondents.