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    DESIGNING CHOICE EXPERIMENTS WITH MANY ATTRIBUTES. AN APPLICATION TO SETTING PRIORITIES FOR ORTHOPAEDIC WAITING LISTS

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    Author
    Witt, J; Scott, A; Osborne, RH
    Date
    2009-06-01
    Source Title
    HEALTH ECONOMICS
    Publisher
    WILEY
    University of Melbourne Author/s
    Scott, Anthony; Osborne, Richard; WITT, JULIA
    Affiliation
    Melbourne Institute of Applied Economic and Social Research
    Metadata
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    Document Type
    Journal Article
    Citations
    Witt, J., Scott, A. & Osborne, R. H. (2009). DESIGNING CHOICE EXPERIMENTS WITH MANY ATTRIBUTES. AN APPLICATION TO SETTING PRIORITIES FOR ORTHOPAEDIC WAITING LISTS. HEALTH ECONOMICS, 18 (6), pp.681-696. https://doi.org/10.1002/hec.1396.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/30399
    DOI
    10.1002/hec.1396
    Abstract
    The aim of this paper is to undertake a discrete choice experiment using a 'blocked attribute' design. To date in the health economics literature, most discrete choice experiments have used only a relatively small number of attributes due to concerns about task complexity, non-compensatory decision rules, simplicity of experimental designs, and the costs of surveys. This may lead to omitted variable bias and reduced explanatory power when attributes have been pre-selected from a longer list. There may be situations where it is desirable to include a longer list of attributes, such as attaching weights to quality-of-life instruments to obtain single index scores. We examine this issue in the context of attaching weights to a disease-specific quality-of-life instrument used to prioritise patients on orthopaedic waiting lists in Victorian hospitals. Eleven attributes are allocated across three separate experimental designs and the data pooled for analysis. Pooling is justified given the specific context of the study, including attempts to minimise the effect of unobserved heterogeneity across the three models when designing the study and collecting data. Blocked attribute designs may offer flexibility to researchers when it is not possible or desirable to reduce the number of attributes.
    Keywords
    Applied Economics

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