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Fifty Days an MTurk Worker: The Social and Motivational Context for Amazon Mechanical Turk Workers

Published online by Cambridge University Press:  28 July 2015

Gordon B. Schmidt
Gordon B. Schmidt*
Affiliation:
Indiana University Purdue University Fort Wayne
*
Correspondence concerning this article should be addressed to Gordon B. Schmidt, Organizational Leadership & Supervision, Indiana University Purdue University Fort Wayne, OLS Neff 288D, 2101 Coliseum Boulevard, Fort Wayne, IN 46805. E-mail: [email protected]
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Extract

The focal article of Landers and Behrend (2015) persuasively argues that universally condemning potential convenience data sources outside of traditional industrial–organizational (I-O) samples such as college students and organization samples is misguided. This author agrees that instead we need to consider the context, strengths, and weaknesses of more recently recognized potential data sources. This commentary will focus on understanding the context of one particular potential data source, Amazon's Mechanical Turk (MTurk; https://www.mturk.com/). While some existing research has looked at the demographic characteristics of Amazon MTurk workers and how those workers’ answers compared with more traditional samples (Casler, Bickel, & Hackett, 2013; Goodman, Cryder, & Cheema, 2013; Paolacci & Chandler, 2014), for this commentary I decided to take a primarily different tack. For the space of approximately 50 days, I acted as an MTurk worker on the site and participated in online communities at which MTurk workers congregate. The purposes of this were to experience the MTurk worker environment firsthand and observe how MTurk workers interact with each other and the site. This was done in the spirit of participant-observer research. Stanton and Rogelberg (2002) argue that online communities might be a particularly fruitful avenue for such participant-observer research within the field of I-O psychology. I am quick to note here that I don't see my efforts here as anywhere near as extensive as much of the participant-observer work of the past, and I did my time on MTurk in the spirit of such work rather than as a match for their methodological and analytical rigor. The observations I make in this commentary will be couched in my own experiences as well as the existing literature base on Amazon MTurk.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 2 , June 2015 , pp. 165 - 171
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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References

Behrend, T. S., Sharek, D. J., Meade, A. W., & Wiebe, E. N. (2011). The viability of crowdsourcing for survey research. Behavior Research Methods, 43, 800–813.Google Scholar
Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon's MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29, 2156–2160.Google Scholar
Chandler, J., Mueller, P., & Paolacci, G. (2014). Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers. Behavior Research Methods, 46, 112–130.Google Scholar
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26, 213–224.Google Scholar
Ipeirotis, P. (2010). Demographics of Mechanical Turk (CeDER Working Paper-10–01). New York: New York University. Retrieved from http://hdl.handle.net/2451/29585.Google Scholar
Landers, R. N., & Behrend, T. S. (2015). An inconvenient truth: Arbitrary distinctions between organizational, Mechanical Turk, and other convenience samples. Industrial and Organizational Psychology: Perspectives on Science and Practice.Google Scholar
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23, 184–188.Google Scholar
Stanton, J. M., & Rogelberg, S. G. (2002). Beyond online surveys: Internet research opportunities for industrial–organizational psychology. In Rogelberg, S. G. (Ed.), Handbook of research methods in industrial and organizational psychology. Oxford, United Kingdom: Blackwell.Google Scholar