IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v70y2010i12p1957-1965.html
   My bibliography  Save this article

Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters

Author

Listed:
  • Flynn, Terry Nicholas
  • Louviere, Jordan J.
  • Peters, Tim J.
  • Coast, Joanna

Abstract

Health services researchers are increasingly using discrete choice experiments (DCEs) to model a latent variable, be it health, health-related quality of life or utility. Unfortunately it is not widely recognised that failure to model variance heterogeneity correctly leads to bias in the point estimates. This paper compares variance heterogeneity latent class models with traditional multinomial logistic (MNL) regression models. Using the ICECAP-O quality of life instrument which was designed to provide a set of preference-based general quality of life tariffs for the UK population aged 65+, it demonstrates that there is both mean and variance heterogeneity in preferences for quality of life, which covariate-adjusted MNL is incapable of separating. Two policy-relevant mean groups were found: one group that particularly disliked impairments to independence was dominated by females living alone (typically widows). Males who live alone (often widowers) did not display a preference for independence, but instead showed a strong aversion to social isolation, as did older people (of either sex) who lived with a spouse. Approximately 6-10% of respondents can be classified into a third group that often misunderstood the task. Having a qualification of any type and higher quality of life was associated with smaller random component variances. This illustrates how better understanding of random utility theory enables richer inferences to be drawn from discrete choice experiments. The methods have relevance for all health studies using discrete choice tasks to make inferences about a latent scale, particular QALY valuation exercises that use DCEs, best-worst scaling and ranking tasks.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:socmed:v:70:y:2010:i:12:p:1957-1965
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277-9536(10)00235-2
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emily Lancsar & Jordan Louviere, 2008. "Conducting Discrete Choice Experiments to Inform Healthcare Decision Making," PharmacoEconomics, Springer, vol. 26(8), pages 661-677, August.
    2. Grewal, Ini & Lewis, Jane & Flynn, Terry & Brown, Jackie & Bond, John & Coast, Joanna, 2006. "Developing attributes for a generic quality of life measure for older people: Preferences or capabilities?," Social Science & Medicine, Elsevier, vol. 62(8), pages 1891-1901, April.
    3. Flynn, Terry N. & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2007. "Best-worst scaling: What it can do for health care research and how to do it," Journal of Health Economics, Elsevier, vol. 26(1), pages 171-189, January.
    4. van der Pol, Marjon & Cairns, John, 2008. "Comparison of two methods of eliciting time preference for future health states," Social Science & Medicine, Elsevier, vol. 67(5), pages 883-889, September.
    5. Dolan, Paul, 2008. "Developing methods that really do value the ‘Q’ in the QALY," Health Economics, Policy and Law, Cambridge University Press, vol. 3(01), pages 69-77, January.
    6. Wiktor Adamowicz & David Bunch & Trudy Cameron & Benedict Dellaert & Michael Hanneman & Michael Keane & Jordan Louviere & Robert Meyer & Thomas Steenburgh & Joffre Swait, 2008. "Behavioral frontiers in choice modeling," Marketing Letters, Springer, vol. 19(3), pages 215-228, December.
    7. Hole, Arne Risa, 2008. "Modelling heterogeneity in patients' preferences for the attributes of a general practitioner appointment," Journal of Health Economics, Elsevier, vol. 27(4), pages 1078-1094, July.
    8. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    9. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    10. Coast, Joanna & Flynn, Terry N. & Natarajan, Lucy & Sproston, Kerry & Lewis, Jane & Louviere, Jordan J. & Peters, Tim J., 2008. "Valuing the ICECAP capability index for older people," Social Science & Medicine, Elsevier, vol. 67(5), pages 874-882, September.
    11. Jordan Louviere, 2006. "What You Don’t Know Might Hurt You: Some Unresolved Issues in the Design and Analysis of Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 34(1), pages 173-188, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Potoglou, Dimitris & Burge, Peter & Flynn, Terry & Netten, Ann & Malley, Juliette & Forder, Julien & Brazier, John E., 2011. "Best-worst scaling vs. discrete choice experiments: An empirical comparison using social care data," Social Science & Medicine, Elsevier, vol. 72(10), pages 1717-1727, May.
    2. Glenk, Klaus & Hall, Clare & Liebe, Ulf & Meyerhoff, Jürgen, 2012. "Preferences of Scotch malt whisky consumers for changes in pesticide use and origin of barley," Food Policy, Elsevier, vol. 37(6), pages 719-731.
    3. Ratcliffe, Julie & Huynh, Elisabeth & Chen, Gang & Stevens, Katherine & Swait, Joffre & Brazier, John & Sawyer, Michael & Roberts, Rachel & Flynn, Terry, 2016. "Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm," Social Science & Medicine, Elsevier, vol. 157(C), pages 48-59.
    4. Kassie, Girma T. & Abdulai, Awudu & Greene, William H. & Shiferaw, Bekele & Abate, Tsedeke & Tarekegne, Amsal & Sutcliffe, Chloe, 2017. "Modeling Preference and Willingness to Pay for Drought Tolerance (DT) in Maize in Rural Zimbabwe," World Development, Elsevier, vol. 94(C), pages 465-477.
    5. repec:eee:socmed:v:189:y:2017:i:c:p:114-128 is not listed on IDEAS
    6. 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.
    7. Anna Bartczak & Jürgen Meyerhoff, 2012. "Valuing the chances of survival of two distinct Eurasian lynx populations in Poland – do people want to keep doors open?," Working Papers 2012-14, Faculty of Economic Sciences, University of Warsaw.
    8. Carmelo J. León & Jorge E. Araña, 2012. "The Dynamics of Preference Elicitation after an Environmental Disaster: Stability and Emotional Load," Land Economics, University of Wisconsin Press, vol. 88(2), pages 362-381.
    9. Li, Jinhu & Scott, Anthony & McGrail, Matthew & Humphreys, John & Witt, Julia, 2014. "Retaining rural doctors: Doctors' preferences for rural medical workforce incentives," Social Science & Medicine, Elsevier, vol. 121(C), pages 56-64.
    10. Sivey, Peter & Scott, Anthony & Witt, Julia & Joyce, Catherine & Humphreys, John, 2012. "Junior doctors’ preferences for specialty choice," Journal of Health Economics, Elsevier, vol. 31(6), pages 813-823.
    11. Marti, Joachim & Buckell, John & Maclean, J. Catherine & Sindelar, Jody L., 2017. "To 'Vape' or Smoke? A Discrete Choice Experiment among Adult Smokers," IZA Discussion Papers 10490, Institute for the Study of Labor (IZA).
    12. Joachim Marti & John Buckell & Johanna Catherine Maclean & Jody L. Sindelar, 2016. "To ‘Vape’ or Smoke? A Discrete Choice Experiment Among U.S. Adult Smokers," NBER Working Papers 22079, National Bureau of Economic Research, Inc.
    13. Mara Thiene & Riccardo Scarpa & Jordan Louviere, 2015. "Addressing Preference Heterogeneity, Multiple Scales and Attribute Attendance with a Correlated Finite Mixing Model of Tap Water Choice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 637-656, November.
    14. Thiene, Mara & Meyerhoff, Jürgen & De Salvo, Maria, 2012. "Scale and taste heterogeneity for forest biodiversity: Models of serial nonparticipation and their effects," Journal of Forest Economics, Elsevier, vol. 18(4), pages 355-369.
    15. 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.
    16. Marti, Joachim, 2012. "A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking," Social Science & Medicine, Elsevier, vol. 75(1), pages 87-97.
    17. James Laurenceson & Paul F. Burke & Edward Wei, 2015. "The Australian Public's Preferences Over Foreign Investment in Agriculture," Agenda - A Journal of Policy Analysis and Reform, Australian National University, College of Business and Economics, School of Economics, vol. 22(1), pages 45-62.
    18. Alessandro Mengoni & Chiara Seghieri & Sabina Nuti, 2013. "The application of discrete choice experiments in health economics: a systematic review of the literature," Working Papers 201301, Scuola Superiore Sant'Anna of Pisa, Istituto di Management.
    19. repec:spr:pharme:v:36:y:2018:i:2:d:10.1007_s40273-017-0575-4 is not listed on IDEAS
    20. Emily Lancsar & Peter Burge, 2014. "Choice modelling research in health economics," Chapters,in: Handbook of Choice Modelling, chapter 28, pages 675-687 Edward Elgar Publishing.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:70:y:2010:i:12:p:1957-1965. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.