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Several methods to investigate relative attribute impact in stated preference experiments


  • Lancsar, Emily
  • Louviere, Jordan
  • Flynn, Terry


There is growing use of discrete choice experiments (DCEs) to investigate preferences for products and programs and for the attributes that make up such products and programs. However, a fundamental issue overlooked in the interpretation of many choice experiments is that attribute parameters estimated from DCE response data are confounded with the underlying subjective scale of the utilities, and strictly speaking cannot be interpreted as the relative "weight" or "impact" of the attributes, as is frequently done in the health economics literature. As such, relative attribute impact cannot be compared using attribute parameter size and significance. Instead, to investigate the relative impact of each attribute requires commensurable measurement units; that is, a common, comparable scale. We present and demonstrate empirically a menu of five methods that allow such comparisons: (1) partial log-likelihood analysis; (2) the marginal rate of substitution for non-linear models; (3) Hicksian welfare measures; (4) probability analysis; and (5) best-worst attribute scaling. We discuss the advantages and disadvantages of each method and suggest circumstances in which each is appropriate.

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  • Lancsar, Emily & Louviere, Jordan & Flynn, Terry, 2007. "Several methods to investigate relative attribute impact in stated preference experiments," Social Science & Medicine, Elsevier, vol. 64(8), pages 1738-1753, April.
  • Handle: RePEc:eee:socmed:v:64:y:2007:i:8:p:1738-1753

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    References listed on IDEAS

    1. Small, Kenneth A & Rosen, Harvey S, 1981. "Applied Welfare Economics with Discrete Choice Models," Econometrica, Econometric Society, vol. 49(1), pages 105-130, January.
    2. 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.
    3. Dorte Gyrd-Hansen & Jes Søgaard, 2001. "Analysing public preferences for cancer screening programmes," Health Economics, John Wiley & Sons, Ltd., vol. 10(7), pages 617-634.
    4. Scott, Anthony, 2001. "Eliciting GPs' preferences for pecuniary and non-pecuniary job characteristics," Journal of Health Economics, Elsevier, vol. 20(3), pages 329-347, May.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, March.
    6. Emily Lancsar, 2002. "Deriving welfare measures from stated preference discrete choice modelling experiments, CHERE Discussion Paper No 48," Discussion Papers 48, CHERE, University of Technology, Sydney.
    7. Ryan, Mandy & Wordsworth, Sarah, 2000. "Sensitivity of Willingness to Pay Estimates to the Level of Attributes in Discrete Choice Experiments," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(5), pages 504-524, November.
    8. Jane Hall & Patricia Kenny & Madeleine King & Jordan Louviere & Rosalie Viney & Angela Yeoh, 2002. "Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 457-465.
    9. Emily Lancsar & Elizabeth Savage, 2004. "Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory," Health Economics, John Wiley & Sons, Ltd., vol. 13(9), pages 901-907.
    10. Ryan, Mandy, 1999. "Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation," Social Science & Medicine, Elsevier, vol. 48(4), pages 535-546, February.
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    Cited by:

    1. Andriy Danyliv & Milena Pavlova & Irena Gryga & Wim Groot, 2015. "Preferences for physician services in Ukraine: a discrete choice experiment," International Journal of Health Planning and Management, Wiley Blackwell, vol. 30(4), pages 346-365, October.
    2. Vermeulen, Bart & Goos, Peter & Vandebroek, Martina, 2010. "Obtaining more information from conjoint experiments by best-worst choices," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1426-1433, June.
    3. Chiara Seghieri & Alessandro Mengoni & Sabina Nuti, 2014. "Applying discrete choice modelling in a priority setting: an investigation of public preferences for primary care models," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(7), pages 773-785, September.
    4. Axel Mühlbacher & Uwe Junker & Christin Juhnke & Edgar Stemmler & Thomas Kohlmann & Friedhelm Leverkus & Matthias Nübling, 2015. "Chronic pain patients’ treatment preferences: a discrete-choice experiment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(6), pages 613-628, July.
    5. Richard Norman & Gisselle Gallego, 2008. "Equity weights for economic evaluation: An Australian Discrete Choice Experiment, CHERE Working Paper 2008/5," Working Papers 2008/5, CHERE, University of Technology, Sydney.
    6. repec:spr:patien:v:10:y:2017:i:6:d:10.1007_s40271-017-0247-7 is not listed on IDEAS
    7. Álvarez, Begoña & Rodríguez-Míguez, Eva, 2011. "Patients' self-interested preferences: Empirical evidence from a priority setting experiment," Social Science & Medicine, Elsevier, vol. 72(8), pages 1317-1324, April.
    8. Soto, José R. & Adams, Damian C. & Escobedo, Francisco J., 2016. "Landowner attitudes and willingness to accept compensation from forest carbon offsets: Application of best–worst choice modeling in Florida USA," Forest Policy and Economics, Elsevier, vol. 63(C), pages 35-42.
    9. Pedersen, Line Bjørnskov & Hess, Stephane & Kjær, Trine, 2016. "Asymmetric information and user orientation in general practice: Exploring the agency relationship in a best–worst scaling study," Journal of Health Economics, Elsevier, vol. 50(C), pages 115-130.
    10. Axel Mühlbacher & F. Reed Johnson, 2016. "Choice Experiments to Quantify Preferences for Health and Healthcare: State of the Practice," Applied Health Economics and Health Policy, Springer, vol. 14(3), pages 253-266, June.
    11. Riera, Pere & Giergiczny, Marek & Peñuelas, Josep & Mahieu, Pierre-Alexandre, 2012. "A choice modelling case study on climate change involving two-way interactions," Journal of Forest Economics, Elsevier, vol. 18(4), pages 345-354.
    12. Abiiro, Gilbert Abotisem & Torbica, Aleksandra & Kwalamasa, Kassim & De Allegri, Manuela, 2014. "Eliciting community preferences for complementary micro health insurance: A discrete choice experiment in rural Malawi," Social Science & Medicine, Elsevier, vol. 120(C), pages 160-168.
    13. Chris Skedgel & Dean Regier, 2015. "Constant-Sum Paired Comparisons for Eliciting Stated Preferences: A Tutorial," The Patient: Patient-Centered Outcomes Research, Springer;Johns Hopkins Bloomberg School of Public Health, vol. 8(2), pages 155-163, April.
    14. Pfarr, Christian, 2012. "Meltzer-Richard and social mobility hypothesis: revisiting the income-redistribution nexus using German choice data," MPRA Paper 43325, University Library of Munich, Germany.
    15. Axel Mühlbacher & Matthias Nübling, 2011. "Analysis of physicians’ perspectives versus patients’ preferences: direct assessment and discrete choice experiments in the therapy of multiple myeloma," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(3), pages 193-203, June.
    16. Mehdi Ammi & Christine Peyron, 2016. "Heterogeneity in general practitioners’ preferences for quality improvement programs: a choice experiment and policy simulation in France," Health Economics Review, Springer, vol. 6(1), pages 1-11, December.
    17. repec:spr:pharme:v:35:y:2017:i:7:d:10.1007_s40273-017-0506-4 is not listed on IDEAS
    18. 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.
    19. Dellaert, Benedict G.C. & Arentze, Theo & Horeni, Oliver & Timmermans, Harry J.P., 2017. "Deriving attribute utilities from mental representations of complex decisions," Journal of choice modelling, Elsevier, vol. 22(C), pages 24-38.
    20. Jacob L. Orquin & Martin P. Bagger & Simone Mueller Loose, 2013. "Learning affects top down and bottom up modulation of eye movements in decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(6), pages 700-716, November.
    21. Richard Norman & Paula Cronin & Rosalie Viney, 2012. "Deriving utility weights for the EQ-5D-5L using a discrete choice experiment. CHERE Working Paper 2012/01," Working Papers 2012/01, CHERE, University of Technology, Sydney.
    22. Petra Baji & Manuel García-Goñi & László Gulácsi & Emmanouil Mentzakis & Francesco Paolucci, 2016. "Comparative analysis of decision maker preferences for equity/efficiency attributes in reimbursement decisions in three European countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(7), pages 791-799, September.
    23. 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.


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