Georgia Southern University, Department of Psychology, USA.
National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Bioanalytics Branch, USA.
Behav Processes. 2022 May;198:104628. doi: 10.1016/j.beproc.2022.104628. Epub 2022 Mar 27.
In this paper, we introduce discrete choice experiments (DCEs) and provide foundational knowledge on the topic. DCEs are one of the most popular methods within econometrics to study the distribution of choices within a population. DCEs are particularly useful when studying the effects of categorical variables on choice. Procedurally, a DCE involves recruiting a large sample of individuals exposed to a set of choice arrays. The factors that are suspected to affect choice are varied systematically across the choice arrays. Most commonly, DCE data are analyzed with a multinomial logit statistical model with a goal of determining the relative utility of each relevant factor. We also discuss DCEs in comparison with behavioral choice models, such as those based on the matching law, and we show an example of a DCE to illustrate how a DCE can be used to understand choice with behavioral, social, and organizational factors.
在本文中，我们介绍离散选择实验（DCE）并提供相关主题的基础知识。DCE 是计量经济学中研究群体内选择分布最常用的方法之一。当研究类别变量对选择的影响时，DCE 特别有用。从程序上讲，DCE 涉及招募大量接触一组选择数组的个体。在选择数组中系统地改变被怀疑影响选择的因素。通常，使用多项逻辑回归统计模型分析 DCE 数据，目的是确定每个相关因素的相对效用。我们还讨论了 DCE 与行为选择模型的比较，例如基于匹配律的模型，并展示了一个 DCE 的示例，说明如何使用 DCE 来理解行为、社会和组织因素的选择。
