Journal
SOCIAL SCIENCE & MEDICINE
Volume 64, Issue 8, Pages 1738-1753Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.socscimed.2006.12.007
Keywords
choice experiments; attribute impact; welfare measurement; partial log likelihood analysis; best worst attribute scaling
Funding
- Medical Research Council [MC_U145079306] Funding Source: researchfish
- MRC [MC_U145079306] Funding Source: UKRI
- Medical Research Council [MC_U145079306] Funding Source: Medline
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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. (c) 2006 Elsevier Ltd. All rights reserved.
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