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A Convenient Solution: Using MTurk To Sample From Hard-To-Reach Populations

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  • Smith, Nicholas A.
  • Sabat, Isaac E.
  • Martinez, Larry R.
  • Weaver, Kayla
  • Xu, Shi

Abstract

We agree with Landers and Behrend's (2015) proposition that Amazon's Mechanical Turk (MTurk) may provide great opportunities for organizational research samples. However, some groups are characteristically difficult to recruit because they are stigmatized or socially disenfranchised (Birman, 2005; Miller, Forte, Wilson, & Greene, 2006; Sullivan & Cain, 2004; see Campbell, Adams, & Patterson, 2008, for a review). These groups may include individuals who have not previously been the focus of much organizational research, such as those of low socioeconomic status; individuals with disabilities; lesbian, gay, bisexual, or transgender (LGBT) individuals; or victims of workplace harassment. As Landers and Behrend (2015) point out, there is an overrepresentation of research using “Western, educated, industrialized, rich, and democratic†participants. It is important to extend research beyond these samples to examine workplace phenomena that are specific to special populations. We contribute to this argument by noting the particular usefulness that MTurk can provide for sampling from hard-to-reach populations, which we characterize as groups that are in the numerical minority in terms of nationwide representation. To clarify, we focus our discussion on populations that are traditionally hard to reach in the context of contemporary organizational research within the United States.

Suggested Citation

  • Smith, Nicholas A. & Sabat, Isaac E. & Martinez, Larry R. & Weaver, Kayla & Xu, Shi, 2015. "A Convenient Solution: Using MTurk To Sample From Hard-To-Reach Populations," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(2), pages 220-228, June.
    • Handle: RePEc:cup:inorps:v:8:y:2015:i:02:p:220-228_00
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    Cited by:

    1. Laurie A. Garrow & Ziran Chen & Mohammad Ilbeigi & Virginie Lurkin, 2020. "A new twist on the gig economy: conducting surveys on Amazon Mechanical Turk," Transportation, Springer, vol. 47(1), pages 23-42, February.
    2. Ajda Merkuž & Katarina Katja MiheliÄ, 2023. "Thriving While Working Remotely: The Role of Family-Work Affect, Exploration, and Ambidextrous Leadership," SAGE Open, , vol. 13(1), pages 21582440231, February.
    3. Miloslava Plachkinova & Philip Menard, 2022. "An Examination of Gain- and Loss-Framed Messaging on Smart Home Security Training Programs," Information Systems Frontiers, Springer, vol. 24(5), pages 1395-1416, October.
    4. Maher Said & Emma R. Zajdela & Amanda Stathopoulos, 2021. "Accelerating the Adoption of Disruptive Technologies: The Impact of COVID-19 on Intentions to Use Autonomous Vehicles," Papers 2108.01615, arXiv.org, revised Dec 2021.
    5. Deena A. Isom & Hunter M. Boehme & Toniqua C. Mikell & Stephen Chicoine & Marion Renner, 2021. "Status Threat, Social Concerns, and Conservative Media: A Look at White America and the Alt-Right," Societies, MDPI, vol. 11(3), pages 1-20, July.
    6. Hudik, Marek & Fang, Eddy S., 2020. "Money or in-kind gift? Evidence from red packets in China," Journal of Institutional Economics, Cambridge University Press, vol. 16(5), pages 731-746, October.

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