{"title":"R packages and tutorial for case 1 best–worst scaling","authors":"Hideo Aizaki , James Fogarty","doi":"10.1016/j.jocm.2022.100394","DOIUrl":null,"url":null,"abstract":"<div><p>Case 1 best–worst scaling (BWS1) has been used in a wide variety of research fields. BWS1 is attractive, relative to discrete choice experiments, because individual’s preferences for items can be easily measured. Despite the relative ease of implementation, BWS1 analysis still requires the use of software packages. When used in conjunction with other packages, the new and revised functions in the package <strong>support.BWS</strong> allow BWS1 analysis to be conducted using either the counting approach or the modeling approach. Additionally, a new function that simulates responses to BWS1 questions allows discipline specific BWS1 examples to be created for teaching purposes. To make it easier for novice users to implement BWS1 analysis with R, the package <strong>RcmdrPlugin.BWS1</strong>, that integrates with R Commander has been developed. A free web tutorial for BWS1 in R has also been developed. This paper explains the features of the latest version of <strong>support.BWS</strong>, along with the new package <strong>RcmdrPlugin.BWS1</strong>, and illustrates how these packages work.</p></div>","PeriodicalId":46863,"journal":{"name":"Journal of Choice Modelling","volume":"46 ","pages":"Article 100394"},"PeriodicalIF":2.8000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Choice Modelling","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755534522000513","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 6
Abstract
Case 1 best–worst scaling (BWS1) has been used in a wide variety of research fields. BWS1 is attractive, relative to discrete choice experiments, because individual’s preferences for items can be easily measured. Despite the relative ease of implementation, BWS1 analysis still requires the use of software packages. When used in conjunction with other packages, the new and revised functions in the package support.BWS allow BWS1 analysis to be conducted using either the counting approach or the modeling approach. Additionally, a new function that simulates responses to BWS1 questions allows discipline specific BWS1 examples to be created for teaching purposes. To make it easier for novice users to implement BWS1 analysis with R, the package RcmdrPlugin.BWS1, that integrates with R Commander has been developed. A free web tutorial for BWS1 in R has also been developed. This paper explains the features of the latest version of support.BWS, along with the new package RcmdrPlugin.BWS1, and illustrates how these packages work.