title: The relationship between motivation, monetary compensation, and data quality among US- and India-based workers on Mechanical Turk.
abstract: In this study, we examined data quality among Amazon Mechanical Turk (MTurk) workers based in India, and the effect of monetary compensation on their data quality. Recent studies have shown that work quality is independent of compensation rates, and that compensation primarily affects the quantity but not the quality of work. However, the results of these studies were generally based on compensation rates below the minimum wage, and far below a level that was likely to play a practical role in the lives of workers. In this study, compensation rates were set around the minimum wage in India. To examine data quality, we developed the squared discrepancy procedure, which is a task-based quality assurance approach for survey tasks whose goal is to identify inattentive participants. We showed that data quality is directly affected by compensation rates for India-based participants. We also found that data were of a lesser quality among India-based than among US participants, even when optimal payment strategies were utilized. We additionally showed that the motivation of MTurk users has shifted, and that monetary compensation is now reported to be the primary reason for working on MTurk, among both US- and India-based workers. Overall, MTurk is a constantly evolving marketplace where multiple factors can contribute to data quality. High-quality survey data can be acquired on MTurk among India-based participants when an appropriate pay rate is provided and task-specific quality assurance procedures are utilized.
authors.affiliation: Department of Psychology, Lander College, 75-31 150th Street, Flushing, NY, 11367, USA, leib.litman@touro.edu.
authors.forename: Leib
authors.initials: L
authors.lastname: Litman
authors.forename: Jonathan
authors.initials: J
authors.lastname: Robinson
authors.forename: Cheskie
authors.initials: C
authors.lastname: Rosenzweig
mesh_terms: D000328:Adult; D063045:Crowdsourcing / Q000191:economics / Q000379:methods; D005260:Female; D006801:Humans; D007194:India; D020407:Internet; D008297:Male; D040541:Marketing / Q000379:methods; D009042:Motivation; D012107:Research Design; D012456:Salaries and Fringe Benefits; D012942:Social Sciences / Q000379:methods / Q000592:standards; D013647:Task Performance and Analysis; D014481:United States