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Health Policy and Planning Advance Access published online on December 26, 2008
Health Policy and Planning, doi:10.1093/heapol/czn047
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Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2008; all rights reserved.

How to do (or not to do) ... Designing a discrete choice experiment for application in a low-income country

Lindsay J Mangham1,*, Kara Hanson1 and Barbara McPake2

1 Health Economics and Finance Programme, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom.
2 Institute for International Health and Development, Queen Margaret University, Queen Margaret University Drive, Musselburgh, Edinburgh, EH21 6UU, United Kingdom.

* Corresponding author. Health Policy Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom. Tel: +44 (0) 20 7927 2148/+44 (0) 7941 776 376. E-mail: lindsay.mangham{at}lshtm.ac.uk

Understanding the preferences of patients and health professionals is useful for health policy and planning. Discrete choice experiments (DCEs) are a quantitative technique for eliciting preferences that can be used in the absence of revealed preference data. The method involves asking individuals to state their preference over hypothetical alternative scenarios, goods or services. Each alternative is described by several attributes and the responses are used to determine whether preferences are significantly influenced by the attributes and also their relative importance. DCEs are widely used in high-income contexts and are increasingly being applied in low- and middle-income countries to consider a range of policy concerns. This paper aims to provide an introduction to DCEs for policy-makers and researchers with little knowledge of the technique. We outline the stages involved in undertaking a DCE, with an emphasis on the design considerations applicable in a low-income setting.

Key Words: Discrete choice experiment, stated preference technique, quantitative, methodology

Accepted for publication 3 December 2008.


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