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Empirical Investigation of Ranking vs Best–Worst Scaling Generated Preferences for Attributes of Quality of Life: One and the Same or Differentiable?

  • Julie RatcliffeEmail author
  • Billingsley Kaambwa
  • Claire Hutchinson
  • Emily Lancsar
Original Research Article

Abstract

Objectives

The objective of this study was to investigate the degree of inconsistency in quality-of-life attribute preference orderings generated via successive best–worst scaling (a form of ranking whereby the respondent chooses the best and worst attributes from a choice set, these attributes are then eliminated and the best and worst attributes from the reduced choice set are then chosen and this process is continued until all presented attributes are eliminated) and conventional ranking methods (whereby the respondent chooses the best, second best and third best from a choice set until all presented attributes are eliminated).

Methods

An on-line survey was developed for administration to two general population samples comprising younger people (aged 18–64 years) and older people (aged 65 years and above). Data were analysed in STATA through an empirical examination of the relative level of choice inconsistency (randomness in responses or the variability in choice outcomes not explained by attributes and their associated preference weights) for successive best–worst in comparison with the conventional ranking method for the younger and older person samples.

Results

For the younger person sample, both methods were found to be similarly consistent. In contrast, for the older person sample, ranking performed relatively worse than best–worst scaling with more inconsistent responses (tau = 0.515, p < 0.01).

Conclusions

These findings lend some support to the hypothesis initially propagated by the developers of best–worst scaling that it is a comparatively easier choice task for respondents to undertake than a traditional ranking task.

Notes

Acknowledgements

The authors thank Dr. Nicolas Krucien, Dr. Yuanyuan Gu and Associate Prof. Gang Chen for their helpful comments on a previous version of this paper.

Author Contributions

JR, BK and EL conceived the study. All authors designed the study. JR and EL oversaw the data analysis. BK and CH performed the data analysis. JR led and BK, EL and CH contributed to drafting the article. All authors read and approved the final article.

Compliance with Ethical Standards

Funding

This study was funded in part by an Australian Research Council Linkage Grant (LP110200079).

Conflict of interest

Julie Ratcliffe, Billingsley Kaambwa, Claire Hutchinson and Emily Lancsar have no conflicts of interest that are directly relevant to the content of this article.

Ethics approval

This study was approved by the Flinders University Social and Behavioural Research Ethics Committee (Project No.: 6682).

Supplementary material

40271_2019_406_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (DOCX 20 kb)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Health and Social Care Economics Group, College of Nursing and Health SciencesFlinders UniversityAdelaideAustralia
  2. 2.Health Economics Unit, College of Medicine and Public HealthFlinders UniversityAdelaideAustralia
  3. 3.Department of Health Services Research and Policy, Research School of Population HealthAustralian National UniversityCanberraAustralia

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