Concerns and recommendations for using Amazon MTurk for eating disorder research.

Abstract

OBJECTIVE\nOur original aim was to validate and norm common eating disorder (ED) symptom measures in a large, representative community sample of transgender adults in the United States. We recruited via Amazon Mechanical Turk (MTurk), a popular online recruitment and data collection platform both within and outside of the ED field. We present an overview of our experience using MTurk.\n\n\nMETHOD\nRecruitment began in Spring 2020; our original target N was 2,250 transgender adults stratified evenly across the United States. Measures included a demographics questionnaire, the Eating Disorder Examination-Questionnaire, and the Eating Attitudes Test-26. Consistent with current literature recommendations, we implemented a comprehensive set of attention and validity measures to reduce and identify bot responding, data farming, and participant misrepresentation.\n\n\nRESULTS\nRecommended validity and attention checks failed to identify the majority of likely invalid responses. Our collection of two similar ED measures, thorough weight history assessment, and gender identity experiences allowed us to examine response concordance and identify impossible and improbable responses, which revealed glaring discrepancies and invalid data. Furthermore, qualitative data (e.g., emails received from MTurk workers) raised concerns about economic conditions facing MTurk workers that could compel misrepresentation.\n\n\nDISCUSSION\nOur results strongly suggest most of our data were invalid, and call into question results of recently published MTurk studies. We assert that caution and rigor must be applied when using MTurk as a recruitment tool for ED research, and offer several suggestions for ED researchers to mitigate and identify invalid data.