Is Mechanical Turk the Answer to Our Sampling Woes?

Although we share Bergman and Jean's (2016) concerns about the representativeness of samples in the organizational sciences, we are mindful of the ever changing nature of the job market. New jobs are created from technological innovation while others become obsolete and disappear or are functionally transformed. These shifts in employment patterns produce both opportunities and challenges for organizational researchers addressing the problem of the representativeness in our working population samples. On one hand, it is understood that whatever we do, we will always be playing catch-up with the market. On the other hand, it is possible that we can leverage new technologies in order to react to such changes more quickly. As an example, in Bergman and Jean's commentary, they suggested making use of crowdsourcing websites or Internet panels in order to gain access to undersampled populations. Although we agree there is an opportunity to conduct much research of interest to organizational scholars in these settings, we also would point out that these types of samples come with their own sampling challenges. To illustrate these challenges, we examine sampling issues for Amazon's Mechanical Turk (MTurk), which is currently the most used portal for psychologists and organizational scholars collecting human subjects data online. Specifically, we examine whether MTurk workers are “workers†as defined by Bergman and Jean, whether MTurk samples are WEIRD (Western, educated, industrialized, rich, and democratic; Henrich, Heine, & Norenzayan, 2010), and how researchers may creatively utilize the sample characteristics.