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Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software.

Emily Lancsar1, Denzil G Fiebig2, Arne Risa Hole3

  • 1Centre for Health Economics, Monash Business School, Monash University, 75 Innovation Walk, Clayton, VIC, 3800, Australia. Emily.Lancsar@monash.edu.

Pharmacoeconomics
|April 5, 2017
PubMed
Summary
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This guide offers practical advice for analyzing discrete-choice experiment (DCE) data, covering choice models, estimation, and software. It helps researchers navigate DCE data analysis effectively.

Area of Science:

  • Health Economics
  • Behavioral Economics
  • Econometrics

Background:

  • Discrete-choice experiments (DCEs) are widely used to understand decision-making.
  • Analyzing DCE data requires understanding various choice models and practical considerations.
  • Existing resources may not fully bridge theoretical concepts with practical data analysis steps.

Purpose of the Study:

  • To provide a comprehensive user guide for analyzing DCE data, including best-worst and best-best data.
  • To offer practical guidance on the estimation and post-estimation of choice models.
  • To review standard software available for DCE data analysis.

Main Methods:

  • Theoretical review of key choice models used in DCE analysis.
  • Practical advice on data analysis, including estimation and post-estimation techniques.

Related Experiment Videos

  • Overview and comparison of commonly used software packages for DCE data.

    • Main Results:

    • The choice of modeling approach is contingent upon research questions, study design, and data characteristics.
    • Decisions in analyzing choice data are often interdependent, not strictly sequential.
    • The guide provides a structured approach to navigating DCE data analysis.

    Conclusions:

    • This guide serves as a practical entry point for researchers into DCE data analysis.
    • The theoretical and practical content is applicable across various fields, including health economics and beyond.
    • Effective DCE data analysis requires careful consideration of interdependent analytical choices.