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在众包样本中测量延迟折扣:一项探索性研究。

Measuring delay discounting in a crowdsourced sample: An exploratory study.

机构信息

Department of Psychology, University of Guelph, Guelph, Canada.

Hill/Levene Schools of Business, University of Regina, Regina, Canada.

出版信息

Scand J Psychol. 2019 Dec;60(6):520-527. doi: 10.1111/sjop.12583. Epub 2019 Oct 3.

DOI:10.1111/sjop.12583
PMID:31583709
Abstract

Delay discounting is a measure of preferences for smaller immediate rewards over larger delayed rewards. Discounting has been assessed in many ways; these methods have variably and inconsistently involved measures of different lengths (single vs. multiple items), forced-choice methods, self-report methods, online and laboratory assessments, monetary and non-monetary compensation. The majority of these studies have been conducted in laboratory settings. However, over the past 20 years, behavioral data collection has increasingly shifted online. Usually, these experiments involve completing short tasks for small amounts of money, and are thus qualitatively different than experiments in the lab, which are typically more involved and in a strongly controlled environment. The present study aimed to determine how to best measure future discounting in a crowdsourced sample using three discounting measures (a single shot measure, the 27-item Kirby Monetary Choice Questionnaire, and a one-time Matching Task). We examined associations of these measures with theoretically related variables, and assessed influence of payment on responding. Results indicated that correlations between the discounting tasks and conceptually related measures were smaller than in prior laboratory experiments. Moreover, our results suggest providing monetary compensation may attenuate correlations between discounting measures and related variables. These findings suggest that incentivizing discounting measures changes the nature of measurement in these tasks.

摘要

延迟折扣是一种衡量对较小即时奖励与较大延迟奖励的偏好的方法。折扣已通过多种方式进行评估;这些方法在不同程度上涉及不同长度的测量(单项与多项)、强制选择方法、自我报告方法、在线和实验室评估、货币和非货币补偿。这些研究中的大多数都是在实验室环境中进行的。然而,在过去的 20 年中,行为数据的收集越来越多地转移到了线上。通常,这些实验涉及完成少量金钱的短期任务,因此与实验室中的实验在性质上有所不同,后者通常更加复杂,并且处于高度受控的环境中。本研究旨在确定如何使用三种折扣衡量方法(单次测量、27 项 Kirby 货币选择问卷和一次性匹配任务)在众包样本中最好地衡量未来折扣。我们检查了这些衡量方法与理论相关变量的关联,并评估了支付对反应的影响。结果表明,这些折扣任务与概念上相关的衡量标准之间的相关性小于之前的实验室实验。此外,我们的结果表明,提供货币补偿可能会减弱折扣衡量标准与相关变量之间的相关性。这些发现表明,激励折扣衡量标准会改变这些任务中衡量的性质。

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