Using Mechanical Turk Data in IS Research: Risks, Rewards, and Recommendations

Abstract

With the increasing use of crowdsourced data in behavioral research fields, it is important to examine their appropriateness and desirability for IS research. Extending recent work in the IS literature, this tutorial discusses the risks and rewards of using data gathered on Amazon’s Mechanical Turk. We examine the characteristics of MTurk workers and the resulting method biases that may be exacerbated in MTurk data. Based on this analysis, we present a 2x2 matrix to illustrate the categories of IS research questions that are and are not amenable to MTurk data. We suggest that MTurk data is more appropriate for generalizing studies that examine diverse cognition than for contextualizing studies or those involving shared cognition. Finally, we offer a set of practical recommendations for researchers who wish to collect data on MTurk.