The rise of best-worst scaling for prioritization: A transdisciplinary literature review

IF 2.8 3区 经济学 Q1 ECONOMICS
Anne L.R. Schuster , Norah L. Crossnohere , Nicola B. Campoamor , Ilene L. Hollin , John F.P. Bridges
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Abstract

Best-worst scaling (BWS) is a theory-driven choice experiment used for the prioritization of a finite number of options. Within the context of prioritization, BWS is also known as MaxDiff, BWS object case, and BWS Case 1. Now used in numerous fields, we conducted a transdisciplinary literature review of all published applications of BWS focused on prioritization to compare norms on the development, design, administration, analysis, and quality of BWS applications across fields. We identified 526 publications published before 2023 in the fields of health (n = 195), agriculture (n = 163), environment (n = 50), business (n = 50), linguistics (n = 24), transportation (n = 24), and other fields (n = 24). The application of BWS has been doubling every four years. BWS is applied globally with greatest frequency in North America (27.0%). Most studies had a clearly stated purpose (94.7%) that was empirical in nature (89.9%) with choices elicited in the present tense (90.9%). Apart from linguistics, most studies: applied at least one instrument development method (94.3%), used BWS to assess importance (63.1%), used ‘most/least’ anchors (85.7%), and conducted heterogeneity analysis (69.0%). Studies predominantly administered surveys online (58.0%) and infrequently included formal sample size calculations (2.9%). BWS designs in linguistics differed significantly from other fields regarding the average number of objects (p < 0.01), average number of tasks (p < 0.01), average number of objects per task (p = 0.03), and average number of tasks presented to participants (p < 0.01). On a 5-point scale, the average PREFS score was 3.0. This review reveals the growing application of BWS for prioritization and promises to foster new transdisciplinary avenues of inquiry.

最佳-最差排序法的兴起:跨学科文献综述
最佳-最差排序(BWS)是一种理论驱动的选择实验,用于对有限数量的选项进行优先排序。在优先级排序中,BWS 也被称为 MaxDiff、BWS object case 和 BWS Case 1。目前,BWS 已被广泛应用于多个领域,我们对所有已发表的 BWS 应用进行了跨学科文献综述,重点关注优先级排序,以比较不同领域的 BWS 应用在开发、设计、管理、分析和质量方面的规范。我们确定了 2023 年之前发表的 526 篇文献,涉及卫生(n = 195)、农业(n = 163)、环境(n = 50)、商业(n = 50)、语言学(n = 24)、交通(n = 24)和其他领域(n = 24)。生物预警系统的应用每四年翻一番。BWS 在全球的应用频率最高的是北美地区(27.0%)。大多数研究都有明确的目的(94.7%),属于实证性质(89.9%),用现在时引出选择(90.9%)。除语言学外,大多数研究还采用了至少一种工具开发方法(94.3%)、使用 BWS 评估重要性(63.1%)、使用 "最多/最少 "锚点(85.7%)以及进行异质性分析(69.0%)。研究主要在网上进行调查(58.0%),很少包括正式的样本量计算(2.9%)。语言学领域的 BWS 设计在对象的平均数量(p <0.01)、任务的平均数量(p <0.01)、每个任务的平均对象数量(p = 0.03)和呈现给参与者的任务的平均数量(p <0.01)方面与其他领域存在显著差异。以 5 分制计算,PREFS 平均分为 3.0 分。这篇综述揭示了 BWS 在优先级排序方面的应用日益广泛,并有望促进新的跨学科研究途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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