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Crowdsourcing for quantifying transcripts: An exploratory study

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  • Azzam, Tarek
  • Harman, Elena

Abstract

This exploratory study attempts to demonstrate the potential utility of crowdsourcing as a supplemental technique for quantifying transcribed interviews. Crowdsourcing is the harnessing of the abilities of many people to complete a specific task or a set of tasks. In this study multiple samples of crowdsourced individuals were asked to rate and select supporting quotes from two different transcripts. The findings indicate that the different crowdsourced samples produced nearly identical ratings of the transcripts, and were able to consistently select the same supporting text from the transcripts. These findings suggest that crowdsourcing, with further development, can potentially be used as a mixed method tool to offer a supplemental perspective on transcribed interviews.

Suggested Citation

  • Azzam, Tarek & Harman, Elena, 2016. "Crowdsourcing for quantifying transcripts: An exploratory study," Evaluation and Program Planning, Elsevier, vol. 54(C), pages 63-73.
    • Handle: RePEc:eee:epplan:v:54:y:2016:i:c:p:63-73
    DOI: 10.1016/j.evalprogplan.2015.09.002
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    References listed on IDEAS

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    1. John Horton & David Rand & Richard Zeckhauser, 2011. "The online laboratory: conducting experiments in a real labor market," Experimental Economics, Springer;Economic Science Association, vol. 14(3), pages 399-425, September.
    2. Gabriele Paolacci & Jesse Chandler & Panagiotis G. Ipeirotis, 2010. "Running experiments on Amazon Mechanical Turk," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(5), pages 411-419, August.
    3. Paolacci, Gabriele & Chandler, Jesse & Ipeirotis, Panagiotis G., 2010. "Running experiments on Amazon Mechanical Turk," Judgment and Decision Making, Cambridge University Press, vol. 5(5), pages 411-419, August.
    4. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
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    1. Harman, Elena & Azzam, Tarek, 2018. "Incorporating public values into evaluative criteria: Using crowdsourcing to identify criteria and standards," Evaluation and Program Planning, Elsevier, vol. 71(C), pages 68-82.
    2. Harman, Elena & Azzam, Tarek, 2018. "Towards program theory validation: Crowdsourcing the qualitative analysis of participant experiences," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 183-194.

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