The American Sociologist

, Volume 47, Issue 1, pp 47–55 | Cite as

Using Crowdsourcing Websites for Sociological Research: The Case of Amazon Mechanical Turk

  • Daniel B. Shank


Amazon Mechanical Turk, an online marketplace designed for crowdsourcing tasks to other people for compensation, is growing in popularity as a platform for gathering research data within the social sciences. Sociology, compared to some other social sciences, has not been as quick to adopt this form of data collection. Therefore, in this paper I overview the basics of Mechanical Turk research and suggest its pros and cons, both in general and in relation to different sociological data-collection methods and research needs. While Mechanical Turk is currently the most popular crowdsourcing website for research, I present general concepts, patterns, and suggestions that can be applied beyond Mechanical Turk to other crowdsourcing and online research.


Mturk Online research Methods Data collection 


  1. Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research:’s mechanical Turk. Political Analysis, 20(3), 351–368. doi: 10.1093/pan/mpr057.CrossRefGoogle Scholar
  2. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical Turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.CrossRefGoogle Scholar
  3. Chandler, D., & Kapelner, A. (2013). Breaking monotony with meaning: motivation in crowdsourcing markets. Journal of Economic Behavior & Organization, 90, 123–133. doi: 10.1016/j.jebo.2013.03.003.CrossRefGoogle Scholar
  4. Crump, M. J., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon’s mechanical Turk as a tool for experimental behavioral research. PloS One, 8(3), e57410.CrossRefGoogle Scholar
  5. Eriksson, K., & Simpson, B. (2010). Emotional reactions to losing explain gender differences in entering a risky lottery. Judgment and Decision Making, 5(3), 159–163.Google Scholar
  6. Hart, J. (2014). Did hurricane sandy influence the 2012 US presidential election? Social Science Research, 46, 1–8.CrossRefGoogle Scholar
  7. Horton, J. J., Rand, D. G., & Zeckhauser, R. J. (2011). The online laboratory: conducting experiments in a real labor market. Experimental Economics, 14(3), 399–425.CrossRefGoogle Scholar
  8. Hunzaker, M. B. F. (2014). Making sense of misfortune: cultural schemas, victim redefinition, and the perpetuation of stereotypes. Social Psychology Quarterly, 77(2), 166–184. doi: 10.1177/0190272514521219.CrossRefGoogle Scholar
  9. Johnson, M. K., Labouff, J. P., Rowatt, W. C., Patock‐Peckham, J. A., & Carlisle, R. D. (2012). Facets of right-wing authoritarianism mediate the relationship between religious fundamentalism and attitudes toward Arabs and African americans. Journal for the Scientific Study of Religion, 51(1), 128–142.CrossRefGoogle Scholar
  10. Kuwabara, K., & Sheldon, O. (2012). Temporal dynamics of social exchange and the development of solidarity:“testing the waters” versus “taking a leap of faith”. Social Forces, 91(1), 253–273.CrossRefGoogle Scholar
  11. Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s mechanical Turk. Behavior Research Methods, 44(1), 1–23.CrossRefGoogle Scholar
  12. Munsch, C. L., Ridgeway, C. L., & Williams, J. C. (2014). Pluralistic ignorance and the flexibility bias: understanding and mitigating flextime and flexplace bias at work. Work and Occupations, 41(1), 40–62.CrossRefGoogle Scholar
  13. Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867–872.CrossRefGoogle Scholar
  14. Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419.Google Scholar
  15. Rand, D. G. (2012). The promise of mechanical Turk: how online labor markets can help theorists run behavioral experiments. Journal of Theoretical Biology, 299, 172–179. doi: 10.1016/j.jtbi.2011.03.004.CrossRefGoogle Scholar
  16. Ritter, R. S., & Preston, J. L. (2013). Representations of religious words: insights for religious priming research. Journal for the Scientific Study of Religion, 52(3), 494–507.CrossRefGoogle Scholar
  17. Shepherd, H. (2012). Crowdsourcing. Contexts, 11(2), 10–11.CrossRefGoogle Scholar
  18. Simpson, B., Harrell, A., & Willer, R. (2013). Hidden paths from morality to cooperation: moral judgments promote trust and trustworthiness. Social Forces, 91(4), 1529–1548.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Melbourne School of Psychological Sciences, Redmond Barry BuildingThe University of MelbourneParkvilleAustralia

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