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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?

Author

Listed:
  • Julie Ratcliffe

    (Flinders University)

  • Billingsley Kaambwa

    (Flinders University)

  • Claire Hutchinson

    (Flinders University)

  • Emily Lancsar

    (Australian National University)

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

Suggested Citation

  • Julie Ratcliffe & Billingsley Kaambwa & Claire Hutchinson & Emily Lancsar, 2020. "Empirical Investigation of Ranking vs Best–Worst Scaling Generated Preferences for Attributes of Quality of Life: One and the Same or Differentiable?," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 13(3), pages 307-315, June.
    • Handle: RePEc:spr:patien:v:13:y:2020:i:3:d:10.1007_s40271-019-00406-6
    DOI: 10.1007/s40271-019-00406-6
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    References listed on IDEAS

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    1. Lancsar, Emily & Louviere, Jordan & Donaldson, Cam & Currie, Gillian & Burgess, Leonie, 2013. "Best worst discrete choice experiments in health: Methods and an application," Social Science & Medicine, Elsevier, vol. 76(C), pages 74-82.
    2. Wiseman, V. & Mooney, G. & Berry, G. & Tang, K. C., 2003. "Involving the general public in priority setting: experiences from Australia," Social Science & Medicine, Elsevier, vol. 56(5), pages 1001-1012, March.
    3. Vikas Soekhai & Esther W. Bekker-Grob & Alan R. Ellis & Caroline M. Vass, 2019. "Discrete Choice Experiments in Health Economics: Past, Present and Future," PharmacoEconomics, Springer, vol. 37(2), pages 201-226, February.
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    5. Gu, Yuanyuan & Lancsar, Emily & Ghijben, Peter & Butler, James RG & Donaldson, Cam, 2015. "Attributes and weights in health care priority setting: A systematic review of what counts and to what extent," Social Science & Medicine, Elsevier, vol. 146(C), pages 41-52.
    6. Caroline M. Vass & Stuart Wright & Michael Burton & Katherine Payne, 2018. "Scale Heterogeneity in Healthcare Discrete Choice Experiments: A Primer," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(2), pages 167-173, April.
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