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在三个在线离散选择实验调查中,kaizen任务的执行:证据综合
Benjamin Matthew Craig1, Maksat Jumamyradov2, Oliver Rivero-Arias3,4
1Department of Economics, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, CMC206A33620, USA. bcraig@usf.edu.
The patient
|July 20, 2024
概括
在离散选择实验中的Kaizen任务比传统方法收集更多的偏好数据. 关键的改进包括热身任务,保留,多级别属性和随机的属性顺序,以获得更好的结果.
科学领域:
- 行为经济学是一种行为经济学.
- 卫生经济学 卫生经济学
- 心理测量 心理测量 心理测量
背景情况:
- 凯森是一种持续改进的方法,在离散选择实验 (DCE) 中增强了偏好诱导.
- 与传统的选择一个或对对比任务相比,Kaizen任务收集了更丰富的偏好数据.
- 这篇综合文章检查了三个在线DCE的kaizen任务性能:COVID-19疫苗接种 (美国),儿童手术结果报告 (英国) 和EQ-5D-Y-3L估值 (美国).
研究的目的:
- 在线离散选择实验中评估kaizen任务的性能.
- 分析响应行为,参数估计 (条件逻辑,泽梅洛-布拉德利-特里) 和标准错误.
- 在未来的DCE中推出实施kaizen任务的最佳实践.
主要方法:
- 使用kaizen任务进行的三个在线离散选择实验的证据综合.
- 分析响应行为,包括位置效应.
- 估计和比较条件逻辑和泽梅洛-布拉德利-特里 (ZBT) 模型参数.
主要成果:
- 在COVID-19疫苗接种和EQ-5D-Y-3L研究中,包括持久减半了定位行为.
- 儿童手术结果报告研究显示,有限的显著积极估计,可能是由于固定的属性顺序.
- EQ-5D-Y-3L估计值非常显著,与对对比概率有很强的相关性.
结论:
- 未来的DCE应该包括热身任务,保留和多层次的改进.
- 建议在受访者层面随机化属性顺序.
- 较小的样本大小可能足以用于使用kaizen任务的DCE,而不是仅使用选择一个任务的DCE.
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