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Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data

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  • N. Flynn, Terry
  • J. Peters, Tim
  • Coast, Joanna

Abstract

Older people's valuation of health-related aspects of quality of life may be altered by response shift, where they lower expectations of aspects of well-being that are believed to naturally deteriorate with age. Policy-makers may wish to adjust estimated preferences if these reflect past inequities in health funding rather than the true production possibilities. Response shift might be quantified by changing the context of the choice task. The ICECAP-O valuation exercise achieved this by asking a binary choice holistic decision of respondents, in addition to the case 2 best-worst choice task among the five attributes. Answers to the former are more likely to be subject to response shift since they involve traditional trade-offs. Answers to the latter reflect only ‘relative disutility’ of various impairments.

Suggested Citation

  • N. Flynn, Terry & J. Peters, Tim & Coast, Joanna, 2013. "Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data," Journal of choice modelling, Elsevier, vol. 6(C), pages 34-43.
    • Handle: RePEc:eee:eejocm:v:6:y:2013:i:c:p:34-43
    DOI: 10.1016/j.jocm.2013.04.004
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    References listed on IDEAS

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    1. Flynn, Terry Nicholas & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2010. "Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters," Social Science & Medicine, Elsevier, vol. 70(12), pages 1957-1965, June.
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    Cited by:

    1. Yoo, Hong Il & Doiron, Denise, 2013. "The use of alternative preference elicitation methods in complex discrete choice experiments," Journal of Health Economics, Elsevier, vol. 32(6), pages 1166-1179.
    2. Denise Doiron & Hong Il Yoo, 2020. "Stated preferences over job characteristics: A panel study," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(1), pages 43-82, February.
    3. Jennifer A Whitty & Ruth Walker & Xanthe Golenko & Julie Ratcliffe, 2014. "A Think Aloud Study Comparing the Validity and Acceptability of Discrete Choice and Best Worst Scaling Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    4. Terry N. Flynn & Elisabeth Huynh & Tim J. Peters & Hareth Al‐Janabi & Sam Clemens & Alison Moody & Joanna Coast, 2015. "Scoring the Icecap‐a Capability Instrument. Estimation of a UK General Population Tariff," Health Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 258-269, March.
    5. Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    6. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    7. Nicolas Krucien & Verity Watson & Mandy Ryan, 2017. "Is Best–Worst Scaling Suitable for Health State Valuation? A Comparison with Discrete Choice Experiments," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1-16, December.
    8. Balbontin, C. & Ortúzar, J. de D. & Swait, J.D., 2015. "A joint best–worst scaling and stated choice model considering observed and unobserved heterogeneity: An application to residential location choice," Journal of choice modelling, Elsevier, vol. 16(C), pages 1-14.
    9. Lipovetsky, Stan & Conklin, Michael, 2014. "Best-Worst Scaling in analytical closed-form solution," Journal of choice modelling, Elsevier, vol. 10(C), pages 60-68.
    10. Qinxin Guo & Junyi Shen, 2019. "An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice," Discussion Paper Series DP2019-14, Research Institute for Economics & Business Administration, Kobe University.

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