Hauser David J, Schwarz Norbert
Department of Psychology, University of Michigan, 3233 East Hall, 530 Church Street, Ann Arbor, MI, 48109-1043, USA.
University of Southern California, Los Angeles, CA, USA.
Behav Res Methods. 2016 Mar;48(1):400-7. doi: 10.3758/s13428-015-0578-z.
Participant attentiveness is a concern for many researchers using Amazon's Mechanical Turk (MTurk). Although studies comparing the attentiveness of participants on MTurk versus traditional subject pool samples have provided mixed support for this concern, attention check questions and other methods of ensuring participant attention have become prolific in MTurk studies. Because MTurk is a population that learns, we hypothesized that MTurkers would be more attentive to instructions than are traditional subject pool samples. In three online studies, participants from MTurk and collegiate populations participated in a task that included a measure of attentiveness to instructions (an instructional manipulation check: IMC). In all studies, MTurkers were more attentive to the instructions than were college students, even on novel IMCs (Studies 2 and 3), and MTurkers showed larger effects in response to a minute text manipulation. These results have implications for the sustainable use of MTurk samples for social science research and for the conclusions drawn from research with MTurk and college subject pool samples.
参与者的注意力是许多使用亚马逊土耳其机器人（MTurk）的研究人员所关注的问题。尽管将MTurk上的参与者与传统样本库中的样本的注意力进行比较的研究对此问题的支持不一，但注意力检查问题和其他确保参与者注意力的方法在MTurk研究中已大量出现。由于MTurk群体具有学习能力，我们推测MTurk用户会比传统样本库中的样本更注意指令。在三项在线研究中，来自MTurk和大学群体的参与者参与了一项任务，该任务包括对指令注意力的测量（一种教学操作检查：IMC）。在所有研究中，MTurk用户比大学生更注意指令，即使是在新颖的IMC上（研究2和3），并且MTurk用户对微小的文本操作反应更大。这些结果对社会科学研究中MTurk样本的可持续使用以及从MTurk和大学样本库样本的研究中得出的结论具有启示意义。
