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Investigating Participant Honesty in Mechanical Turk: Coin Flipping Experiment Insights

This study by Franklin Kramer examines the factors influencing participant honesty in Mechanical Turk environments through a series of experimental manipulations involving coin flipping. By leveraging the unique characteristics of crowdsourcing platforms, the research investigates hypotheses related to dishonesty, transparency in results, and the impact of worker representation. The findings underscore the importance of understanding participant behavior to enhance the validity of data collected via Mechanical Turk, revealing that context and awareness significantly affect honesty in responses.

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Investigating Participant Honesty in Mechanical Turk: Coin Flipping Experiment Insights

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  1. Variables Affecting Participant Honesty in Experimental Design: Further Manipulations of Coin Flipping in Mechanical Turk By Franklin Kramer

  2. What is Mechanical Turk? • Crowdsourcing web service • Have turkers complete HITs for small amounts of money (most being 1-25 cents) • Can filter workers by specific criteria • Typical worker earns $1.40 (Horton & Chilton) • US Turkers are fairly representative of general population- much better than University participants (Ipeirotis)

  3. Background • Original experiment (Robert Mille) • 31/19 • Coin Flips with Bonuses (same or different) • 32/18, 23/27 • Coin Flips: A further look (time) • Die rolls (Suri et. al)

  4. Hypotheses to Test: • If people are going to lie, they will tend to choose the first result offered. • People are more likely to give honest answers when dishonesty is more transparent in potential results given. • Price will have little effect on results. • People are less likely to lie when they are more aware of what is at stake. • People will be less likely to lie when they feel representative of a group they belong to.

  5. Why? • Lying is a very interesting social phenomena • Benefit to lying minimal • Only detectable on large scale • MT is fairly representative • Valuable to understand how to best give valid results in MT

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