Development, Implementation, and Evaluation of a More Efficient Method of Best-Worst Scaling Data Collection. (24th April 2022)

• Record Type: Journal Article
• Authors: Bir, Courtney; Delgado, Michael; Widmar, Nicole
• Is Part Of: Agricultural and resource economics review. Volume 51:Number 1(2022)
• Page Start: 178
• Page End: 201
• Publication Date: 2022-04-24
• Subjects: attribute non-attendance -- best-worst scaling -- data quality -- transitivity; C18 -- Q10; Sustainable agriculture -- Periodicals; Sustainable agriculture -- United States -- Periodicals; Natural resources -- Periodicals; Agriculture -- Economic aspects -- Periodicals; Rural development -- Periodicals; 338.10974
• DOI: 10.1017/age.2021.27
• Languages: English
• ISSNs: 1068-2805

Abstract

Abstract: Discrete choice experiments are used to collect data that facilitates measurement and understanding of consumer preferences. A sample of 750 respondents was employed to evaluate a new method of best-worst scaling data collection. This new method decreased the number of attributes and questions while discerning preferences for a larger set of attributes through self-stated preference "filter" questions. The new best-worst method resulted in overall equivalent rates of transitivity violations and lower incidences of attribute non-attendance than standard best-worst scaling designs. The new method of best-worst scaling data collection can be successfully employed to efficiently evaluate more attributes while improving data quality.