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在日本利用众包服务进行在线行为研究。

Conducting Online Behavioral Research Using Crowdsourcing Services in Japan.

作者信息

Majima Yoshimasa, Nishiyama Kaoru, Nishihara Aki, Hata Ryosuke

机构信息

Department of Psychology for Well-Being, Hokusei Gakuen University Sapporo, Japan.

Department of Foreign Language Education, Hokusei Gakuen University Sapporo, Japan.

出版信息

Front Psychol. 2017 Mar 14;8:378. doi: 10.3389/fpsyg.2017.00378. eCollection 2017.

Abstract

Recent research on human behavior has often collected empirical data from the online labor market, through a process known as crowdsourcing. As well as the United States and the major European countries, there are several crowdsourcing services in Japan. For research purpose, Amazon's Mechanical Turk (MTurk) is the widely used platform among those services. Previous validation studies have shown many commonalities between MTurk workers and participants from traditional samples based on not only personality but also performance on reasoning tasks. The present study aims to extend these findings to non-MTurk (i.e., Japanese) crowdsourcing samples in which workers have different ethnic backgrounds from those of MTurk. We conducted three surveys ( = 426, 453, 167, respectively) designed to compare Japanese crowdsourcing workers and university students in terms of their demographics, personality traits, reasoning skills, and attention to instructions. The results generally align with previous studies and suggest that non-MTurk participants are also eligible for behavioral research. Furthermore, small screen devices are found to impair participants' attention to instructions. Several recommendations concerning this sample are presented.

摘要

最近关于人类行为的研究经常通过一种叫做众包的过程,从在线劳动力市场收集实证数据。除了美国和欧洲主要国家外,日本也有几种众包服务。出于研究目的,亚马逊的土耳其机器人(MTurk)是这些服务中使用广泛的平台。先前的验证研究表明,MTurk工人与传统样本的参与者之间不仅在个性方面,而且在推理任务表现上都有许多共性。本研究旨在将这些发现扩展到非MTurk(即日本)众包样本,其中工人的种族背景与MTurk不同。我们进行了三项调查(样本量分别为426、453、167),旨在比较日本众包工人和大学生在人口统计学、人格特质、推理能力以及对说明的关注度方面的差异。结果总体上与先前的研究一致,并表明非MTurk参与者也适合进行行为研究。此外,发现小屏幕设备会削弱参与者对说明的关注度。本文还针对该样本提出了一些建议。

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