How to Measure Agreement, Consensus, and Polarization in Ordinal Data

Clem Aeppli and Didier Ruedin
Additional contact information
Didier Ruedin: University of Neuchâtel

No syzbr, SocArXiv from Center for Open Science

Abstract: Different measures exist to capture agreement, consensus, concentration, dispersion, and polarization in ordinal data. We compare consensus scores across specific situations for a better understanding of how different measures work in practice: constructed cases, simulated data where we know the underlying distribution, and empirical data. Although researchers have solved the ‘problem’ of measuring agreement, consensus, and polarization several times, we highlight similarities and equivalence across some existing approaches, while others differ substantially. The choice of method can lead to substantively different conclusions, and we recommend that researchers use a combination of measures and use graphics to examine the distribution qualitatively.

Date: 2022-10-24
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/635798e80ecb4222512ebc86/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:syzbr

DOI: 10.31219/osf.io/syzbr

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().