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

A new working paper with Clem Aeppli on SocArXiv. We look at different measures to capture agreement, consensus, polarization, whatever you want to call it — in ordinal data. Using simulations and an empirical example, we show commonalities and differences between measures. The paper ends with recommendations for researchers wanting to measure consensus, agreement — whatever — in ordinal data.

Aeppli, Clem, and Didier Ruedin. 2022. ‘How to Measure Agreement, Consensus, and Polarization in Ordinal Data’. SocArXiv. https://doi.org/10.31235/osf.io/syzbr.

Ruedin, Didier. 2022. ‘Agrmt: Agreement A’. R. CRAN. http://agrmt.r-forge.r-project.org/.

## Calculating Agreement, Consensus, Polarization in R

I have just uploaded a new version of the R package agrmt to R-Forge. The package implements various measures to enumerate the degree of agreement, consensus, or polarization among respondents. Apart from van der Eijk’s Agreement “A”, there are a range of other measures proposed in the literature.

## Measuring Consensus

I have mentioned Cees van der Eijk’s measure of agreement before, and Leik’s measure of ordinal consensus. Unsurprisingly, others have come across this issue, discontent with the widespread use of standard deviations (inappropriate as this can be). Tastle & Wierman (2007) take a quite different approach, taking the Shannon entropy as the starting point. I have added this to my R package agrmt on R-Forge, and will push it through to CRAN once the documentation is up to scratch. It’s interesting how many different approaches are developed to address the same problem; clearly the different solutions have not spread wide enough to prevent doubling the effort.

Tastle, W., and M. Wierman. 2007. Consensus and dissention: A measure of ordinal dispersion. International Journal of Approximate Reasoning 45 (3): 531-545.