Avoid Fraud in Online Surveys

We tend to think of ethical research behavior on the part of the researcher. However, we need to be prepared for the possibility that participants will behave unethically. In online survey research, particularly when there is an incentive, rogue participants can wreak havoc. They race through, clicking without reading the questions, or leave important questions unanswered. Or, they try to collect the incentive more than once by changing their names and submitting multiple times. Online surveys are incredibly useful for quantitative, qualitative, and mixed methods research, so what can we do?

Dr. Jennifer Lawlor and her co-researchers developed the Reflect, Expect, Analyze, Label (REAL) Framework to address this problem. In this interview she discusses the framework’s four sets of guiding questions and walks through examples.

Dr. Jennifer Lawlor explains the Reflect, Expect, Analyze, Label Framework.

Want to learn more about the REAL Framework?

Read this open access article by Jennifer and her co-researchers:

Lawlor, J., Thomas, C., Guhin, A. T., Kenyon, K., Lerner, M. D., & Drahota, A. (2021). Suspicious and fraudulent online survey participation: Introducing the REAL framework. Methodological Innovations, 14(3). https://doi.org/10.1177/20597991211050467

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.

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