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Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
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Published on: June 21, 2019

Several methods to investigate relative attribute impact in stated preference experiments.

Emily Lancsar1, Jordan Louviere, Terry Flynn

  • 1Business School (Economics) and Institute for Health and Society, University of Newcastel upon Tyne, UK. Emily.Lancsar@ncl.ac.uk

Social Science & Medicine (1982)
|January 30, 2007
PubMed
Summary

Discrete choice experiments (DCEs) often misinterpret attribute parameters. New methods allow accurate comparison of attribute impact by using a common, comparable scale for utility measurements.

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Area of Science:

  • Behavioral Economics
  • Health Economics
  • Econometrics

Background:

  • Discrete Choice Experiments (DCEs) are increasingly used to understand preferences for products and programs.
  • A common issue in DCE interpretation is confounding attribute parameters with subjective utility scales, preventing direct comparison of attribute impact.
  • Current health economics literature frequently misinterprets parameter size and significance as relative attribute weight.

Purpose of the Study:

  • To address the fundamental issue of confounding in DCE attribute parameter interpretation.
  • To present and empirically demonstrate methods for comparing the relative impact of attributes on choices.
  • To establish commensurable measurement units for accurate attribute impact analysis in DCEs.

Main Methods:

  • Introduced five methods for comparing attribute impact: partial log-likelihood analysis, marginal rate of substitution, Hicksian welfare measures, probability analysis, and best-worst attribute scaling.
  • Empirically demonstrated the application of these five methods.
  • Discussed the advantages, disadvantages, and appropriate use cases for each method.

Main Results:

  • Attribute parameters in DCEs are confounded with subjective utility scales and cannot directly represent relative attribute impact.
  • The presented methods provide a means to establish commensurable units for comparing attribute impact.
  • Each method offers distinct advantages for analyzing attribute importance under different model specifications.

Conclusions:

  • Accurate comparison of attribute impact in DCEs requires methods that yield commensurable measurement units.
  • The five presented methods offer valid approaches to overcome the confounding issue in DCE analysis.
  • Selecting the appropriate method depends on the specific research question and model characteristics.