使用亚马逊土耳其机器人在文本中编码心理结构：一种可靠、准确且高效的替代方法。
Coding Psychological Constructs in Text Using Mechanical Turk: A Reliable, Accurate, and Efficient Alternative.
作者信息
Tosti-Kharas Jennifer, Conley Caryn
机构信息
Management, Babson College Babson Park, MA, USA.
Information, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin Austin, TX, USA.
出版信息
Front Psychol. 2016 May 30;7:741. doi: 10.3389/fpsyg.2016.00741. eCollection 2016.
In this paper we evaluate how to effectively use the crowdsourcing service, Amazon's Mechanical Turk (MTurk), to content analyze textual data for use in psychological research. MTurk is a marketplace for discrete tasks completed by workers, typically for small amounts of money. MTurk has been used to aid psychological research in general, and content analysis in particular. In the current study, MTurk workers content analyzed personally-written textual data using coding categories previously developed and validated in psychological research. These codes were evaluated for reliability, accuracy, completion time, and cost. Results indicate that MTurk workers categorized textual data with comparable reliability and accuracy to both previously published studies and expert raters. Further, the coding tasks were performed quickly and cheaply. These data suggest that crowdsourced content analysis can help advance psychological research.
在本文中，我们评估了如何有效地利用众包服务——亚马逊的土耳其机器人（MTurk），对文本数据进行内容分析，以用于心理学研究。MTurk是一个供工人完成离散任务的市场，通常报酬微薄。MTurk总体上已被用于辅助心理学研究，尤其是内容分析。在当前的研究中，MTurk的工人使用先前在心理学研究中开发并验证的编码类别，对个人撰写的文本数据进行内容分析。对这些编码的可靠性、准确性、完成时间和成本进行了评估。结果表明，MTurk的工人对文本数据进行分类的可靠性和准确性，与先前发表的研究以及专家评分者相当。此外，编码任务完成得既快又成本低廉。这些数据表明，众包内容分析有助于推动心理学研究。
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