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Several methods to investigate relative attribute impact in stated preference experiments

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

    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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    Bibliographic Info

    Article provided by Elsevier in its journal Social Science & Medicine.

    Volume (Year): 64 (2007)
    Issue (Month): 8 (April)
    Pages: 1738-1753

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    Handle: RePEc:eee:socmed:v:64:y:2007:i:8:p:1738-1753

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    Related research

    Keywords: Choice experiments Attribute impact Welfare measurement Partial log likelihood analysis Best worst attribute scaling;

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    Cited by:
    1. 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.
    2. 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, vol. 12(3), pages 193-203, June.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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