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Crowdsourcing for quantifying transcripts: An exploratory study.

Tarek Azzam¹, Elena Harman¹

¹Claremont Graduate University, United States.

Evaluation and Program Planning

|November 1, 2015

Summary

Crowdsourcing shows promise for analyzing transcribed interviews. Multiple crowdsourced groups consistently rated transcripts and identified key quotes, suggesting its utility as a supplemental mixed-methods tool.

Keywords:

Crowdsourcing MTurk Mechanical Turk Qualitative analysis Stability Transcript coding Transcript rating

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Area of Science:

Qualitative Research Methods
Human-Computer Interaction
Social Sciences

Background:

Transcribed interviews are valuable qualitative data.
Quantifying qualitative data can be time-consuming.
Crowdsourcing offers a scalable approach to task completion.

Purpose of the Study:

To explore the utility of crowdsourcing for quantifying transcribed interviews.
To assess the reliability of crowdsourced ratings and quote selection.
To evaluate crowdsourcing as a supplemental mixed-methods tool.

Main Methods:

Multiple crowdsourced samples rated two transcribed interviews.
Crowdsourced individuals selected supporting quotes from transcripts.
Inter-rater reliability was assessed for ratings and quote selection.

Main Results:

Crowdsourced samples demonstrated high consistency in transcript ratings.
Consistent selection of supporting quotes across different crowdsourced groups.
Crowdsourcing provided a reliable supplemental perspective on interview data.

Conclusions:

Crowdsourcing is a potentially valuable technique for quantifying qualitative data.
Further development could integrate crowdsourcing as a mixed-methods tool.
This approach offers a scalable and consistent method for interview analysis.