Suspicious and fraudulent online survey participation: Introducing the REAL framework

Abstract

Online survey research has significantly increased in popularity in recent years. With its use, researchers have a new set of concerns about data collection and analysis to consider, including the possibility of fraudulent survey submissions. The purpose of this article is to demonstrate to survey researchers an innovative and systematized process for addressing online survey fraud over the course of collecting survey data, especially when respondents collect incentives for participation. We provide the Reflect, Expect, Analyze, Label Framework, which includes four sets of guiding questions for use by online survey researchers to plan for addressing survey fraud and making determinations about the inclusion or exclusion of participant submissions from the dataset based on level of suspicion. We also provide a full case example utilizing the Reflect, Expect, Analyze, Label Framework as an appendix. Those wanting to apply the Reflect, Expect, Analyze, Label Framework should keep in mind several considerations as they apply it, including determining logistical needs ahead of survey implementation, considering the ethical issues related to including or excluding data in a study, and considering the issues related to providing incentives for participating in research. Future research should assess the frequency of survey fraud, investigate the reasons for its occurrence and explore the role social networks may play in fraudulent participants sharing information. We suggest that researchers consider online survey fraud as an issue over the lifespan of their survey and apply the guiding questions we present to address the issue throughout.