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Unmasking survey fraud: investigating data quality issues in an MTurk sample

Creative Commons 'BY' version 4.0 license
Abstract

Social scientists increasingly rely on Amazon Mechanical Turk (MTurk) for survey participant recruitment, but emerging research suggests a decline in data quality, raising concerns about its reliability. In November 2023, a sample of 221 U.S. MTurk workers was recruited for a survey experiment examining the impact of affordable housing rhetoric on self-esteem. Despite implementing several best practices for recruitment on MTurk – such as system qualifications, screening questions, and virtual private server/network detection – we found that an estimated 65–84% of the workers seeking compensation submitted fraudulent survey responses. This study details five strategies we used to identify fraudulent data: survey re-entry, duplicate demographic data, similar open-ended responses, nonsensical open-ended responses, and repeated geographic coordinates. Our findings reveal critical shortcomings in current MTurk best practices and suggest that, without additional third-party fraud prevention tools or thorough data screening procedures, the platform may no longer be suitable for rigorous academic research.

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