Conducting behavioral research on Amazon's Mechanical Turk

Area of Science:

• Behavioral Science
• Social Science Research
• Human-Computer Interaction

Background:

• Online labor markets offer new avenues for behavioral research.
• Amazon Mechanical Turk (MTurk) is a prominent platform for crowdsourced tasks.
• Researchers seek efficient and cost-effective methods for subject recruitment and data collection.

Purpose of the Study:

• To demonstrate the utility of Amazon Mechanical Turk for conducting behavioral research.
• To reduce barriers for researchers new to using online platforms for experiments.
• To provide practical guidance on leveraging MTurk's capabilities.

Main Methods:

• Overview of advantages: large subject pool, low cost, rapid iteration.
• Comparison of MTurk worker behavior to traditional subject pools.
• Step-by-step guide to task implementation: recruitment, execution, and review.

Main Results:

• MTurk provides access to a large, diverse, and stable participant pool.
• Experiments on MTurk can be conducted at a lower cost and with faster turnaround.
• Worker behavior on MTurk is comparable to laboratory subjects in many contexts.

Conclusions:

• Amazon Mechanical Turk is a valuable tool for behavioral researchers across disciplines.
• The platform facilitates efficient subject recruitment and experimental execution.
• Practical solutions are offered for common challenges in online research using MTurk.