Mechanical Turk:大枚をはたくことなく、リサーチを迅速に拡張

ユーザーエクスペリエンスの研究者は、時間や予算やターゲットユーザーの不足といった制約にしばしば直面するが、AmazonのMechanical Turk (メカニカルターク) をうまく利用して迅速かつ拡張可能なリサーチを実施することにより、これらの課題を克服できるかもしれない。また、Mechanical Turkは研究者が多様な参加者 (世界各地のユーザー、さまざまなレベルの認知および身体能力を持つユーザーなど) にリーチするのにも役立つ。この記事の著者らは、ユーザー調査におけるMechanical Turkの使用方法についての詳細な説明とともに、潜在的課題の具体的な解決法を提供する。


Volz, J., Mitchell, C. (2017). Mechanical Turk:大枚をはたくことなく、リサーチを迅速に拡張. User Experience Magazine, 17(5).
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2 Responses

  1. Connie Godsey-Bell より:

    This is a very interesting use of Mechanical Turk. Can you give more detail on why you can leverage it without as high a regard for SME as before? Thanks.

    • Jake Volz より:

      Hi Connie,

      The main reason for the change is that, while we were scaling our panels, we began trying new platforms that source LinkedIn- and Facebook-verified participants for remote (and even in-person) studies. Since these platforms can collect personal information of the participants, they become substantially more effective to screen SME with. This is especially true for our group, as much of our initial recruiting criteria can be seen on a LinkedIn profile. Another reason is that our internal databases of verified participants have scaled nicely over time, which has decreased some of our demand through external channels.

      It is rare that we’ll rely on Mechanical Turk for SME participants now, but we still sometimes include participants from multiple sources (including Mechanical Turk) in our remote studies requiring SME participants.