Effects of pay rate and instructions on attrition in crowdsourcing research

Carolyn M Ritchey, Corina Jimenez-Gomez and Christopher A Podlesnik

PLOS ONE, 2023, vol. 18, issue 10, 1-10

Abstract: Researchers in social sciences increasingly rely on crowdsourcing marketplaces such as Amazon Mechanical Turk (MTurk) and Prolific to facilitate rapid, low-cost data collection from large samples. However, crowdsourcing suffers from high attrition, threatening the validity of crowdsourced studies. Separate studies have demonstrated that (1) higher pay rates and (2) additional instructions–i.e., informing participants about task requirements, asking for personal information, and describing the negative impact of attrition on research quality–can reduce attrition rates with MTurk participants. The present study extended research on these possible remedies for attrition to Prolific, another crowdsourcing marketplace with strict requirements for participant pay. We randomly assigned 225 participants to one of four groups. Across groups, we evaluated effects of pay rates commensurate with or double the US minimum wage, expanding the upper range of this independent variable; two groups also received additional instructions. Higher pay reduced attrition and correlated with more accurate performance on experimental tasks but we observed no effect of additional instructions. Overall, our findings suggest that effects of increased pay on attrition generalize to higher minimum pay rates and across crowdsourcing platforms. In contrast, effects of additional instructions might not generalize across task durations, task types, or crowdsourcing platforms.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0292372

DOI: 10.1371/journal.pone.0292372

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