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Let’s Measure Agreement, Consensus, and Polarization in Ordinal Data

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  • Aeppli, Clem
  • Ruedin, Didier

    (University of Neuchâtel)

Abstract

There are different measures to capture agreement, consensus, concentration, dispersion, and polarization in ordinal data. To better understand how different measures work in practice, we compare consensus scores across specific situations: constructed cases, simulated data where we know the underlying distribution, and empirical data. Researchers have addressed the ‘problem’ of measuring agreement, consensus, and polarization in ordinal data in many ways across many subfields; we highlight the similarities as well as the distinctive features of these approaches. In many cases, the different measures are highly correlated, but the choice of method can lead to substantially different conclusions in situations that are difficult for non-mathematical users to predict. Unless researchers prioritize specific properties, we recommend a combination of measures and graphics to qualitatively examine the distribution.

Suggested Citation

  • Aeppli, Clem & Ruedin, Didier, 2022. "Let’s Measure Agreement, Consensus, and Polarization in Ordinal Data," SocArXiv syzbr_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:syzbr_v1
    DOI: 10.31219/osf.io/syzbr_v1
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