Crowdsourcing a normative natural language dataset: A comparison of Mechanical Turk and in-lab data collection

Citation:

Saunders DR, Bex PJ, Woods RL. Crowdsourcing a normative natural language dataset: A comparison of Mechanical Turk and in-lab data collection. Journal of Medical Internet Research. 2013;15(5):e100.

Abstract:

Background: Crowdsourcing has become a valuable method for collecting medical research data. This approach, recruiting through open calls on the web, is particularly useful for assembling large normative datasets. However, it is not known how natural language datasets collected over the web differ from those collected under controlled laboratory conditions. Objective: To compare the natural language responses obtained through crowdsourcingfrom a crowdsourced sample with responses collected in a conventional laboratory setting from participants recruited according to specific age and gender criteria. Methods: We collected natural language descriptions of 200 half-minute movie clips, from Amazon.com Mechanical Turk workers (crowdsourced) and 60 participants recruited from the community (lab-sourced). Crowdsourced participants responded to as many clips as they wanted, and typed their responses, whereas lab-sourced participants gave spoken responses to 40 clips, and their responses were transcribed. The content of the responses was evaluated using a take-one-out procedure, which compared responses to other responses to the same clip and to other clips, with a comparison of the average number of shared words. Results: In contrast to the 13 months of recruiting that was required to collect normative data from 60 lab-sourced participants (with specific demographic characteristics), only 34 days were needed to collect normative data from 99 crowdsourced participants (contributing a median of 22 responses). The majority of crowdsourced workers were female, and the median age was 35y, lower than the lab-sourced median of 62y but similar to the median age of the {U.S.} population. The responses contributed by the crowdsourced participants were longer on average, 33 words compared to 28 words (P {\textless} .001), and they used a less varied vocabulary. However, there was strong similarity in the words used to describe a particular clip between the two datasets, as a cross-dataset count of shared words showed (P {\textless} .001). Within both datasets, responses contained substantial relevant content, with more words in common with responses to the same clip than to other clips (P {\textless} .001). There was evidence that responses from female and older crowdsourced participants had more shared words (P = .004 and .01 respectively), whereas younger participants had higher numbers of shared words in the lab-sourced population (P = .01). Conclusions: Crowdsourcing is an effective approach to quickly and economically collect a large, reliable dataset of normative natural language responses.