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Incorporating Preferences and Priorities into MCDA: Selecting an Appropriate Scoring and Weighting Technique

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Multi-Criteria Decision Analysis to Support Healthcare Decisions

Abstract

A key component of many multi-criteria decision analyses (MCDAs) is the elicitation of stakeholder preferences in the form or scores and weights. A challenge to the MCDA practitioner is that there is little guidance about how to choose between the many scoring and weighting techniques. This chapter describes and illustrates the four commonly used methods – direct rating (specifically an instance of the use of the Evidence and Value: Impact on Decision Making (EVIDEM) framework), Keeney-Raiffa MCDA, the analytical hierarchy process and discrete choice experiment – and identifies key differences between these techniques in order to support researchers to determine the most appropriate technique in different circumstances. It is concluded that there is no ‘best’ MCDA method, with the pertinence of methods depending on the objective of the analysis.

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Authors and Affiliations

  1. Evidera, London, UK

    Kevin Marsh & Tereza Lanitis

  2. University of Sheffield, Sheffield, UK

    Praveen Thokala

  3. Hochschule Neubrandenburg, Neubrandenburg, Germany

    Axel Mühlbacher

Authors
  1. Kevin Marsh
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  2. Praveen Thokala
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  3. Axel Mühlbacher
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  4. Tereza Lanitis
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Corresponding author

Correspondence to Kevin Marsh .

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Editors and Affiliations

  1. Evidera, London, United Kingdom

    Kevin Marsh

  2. School of Public Health, University of Montreal and LASER Analytica, Montréal, Québec, Canada

    Mireille Goetghebeur

  3. University of Sheffield, Sheffield, United Kingdom

    Praveen Thokala

  4. Radboud University Medical Center, Nijmegen, The Netherlands

    Rob Baltussen

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Marsh, K., Thokala, P., Mühlbacher, A., Lanitis, T. (2017). Incorporating Preferences and Priorities into MCDA: Selecting an Appropriate Scoring and Weighting Technique. In: Marsh, K., Goetghebeur, M., Thokala, P., Baltussen, R. (eds) Multi-Criteria Decision Analysis to Support Healthcare Decisions. Springer, Cham. https://doi.org/10.1007/978-3-319-47540-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-47540-0_4

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