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Running experiments on Amazon Mechanical Turk

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TLDR
The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Abstract
Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.

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Journal ArticleDOI

Comparing Amazon’s Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature

TL;DR: The literature overwhelmingly concludes that MTurk is an efficient, reliable, cost-effective tool for generating sample responses that are largely comparable to those collected via more conventional means.
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What Motivates Effort?: Evidence and Expert Forecasts

TL;DR: The authors conducted a large-scale real-effort experiment with eighteen treatment arms and found that monetary incentives work largely as expected, including a very low piece rate treatment which does not crowd out effort; the evidence is partly consistent with standard behavioural models, including warm glow, though they do not find evidence of probability weighting; and the psychological motivators are effective but less so than incentives.
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Large-scale analysis of test–retest reliabilities of self-regulation measures

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Gender differences in evaluation of new business opportunity: A stereotype threat perspective

TL;DR: The authors explored differences between men and women on evaluation of new business opportunities and found that men reported higher opportunity evaluation than women when no gender stereotypical information was presented, whereas women evaluated the business opportunity equally favorably when entrepreneurs were described using gender-neutral attributes.
References
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Amazon's Mechanical Turk A New Source of Inexpensive, Yet High-Quality, Data?

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Journal ArticleDOI

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