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

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.