Conducting Behavioral Research on Amazon's Mechanical Turk

Abstract

Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this paper is to demonstrate how to use this website for conducting behavioral research and lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments and faster iteration between developing theory and executing experiments. We will discuss how the behavior of workers compares to experts and to laboratory subjects. Then, we illustrate the mechanics of putting a task on Mechanical Turk including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform including techniques for conducting synchronous experiments, methods to ensure high quality work, how to keep data private, and how to maintain code security.

Keywords: crowdsourcing, mechanical turk, methods, online