New interpretations of Cohen's kappa
By Matthijs J. Warrens. 2014
47 citations recorded by the source corpus.
Cohen’s kappa is a widely used association coefficient for summarizing interrater agreement on a nominal scale. Kappa reduces the ratings of the two observers to a single number. With three or more categories it is more informative to summarize the ratings by category coefficients that describe the information for each category separately. Examples of category coefficients are the sensitivity or specificity of a category or the Bloch-Kraemer weighted kappa. However, in many research studies one is often only interested in a single overall number that roughly summarizes the agreement. It is shown that both the overall observed agreement and Cohen’s kappa are weighted averages of various category coefficients and thus can be used to summarize these category coefficients.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/9de7b8099d25126253b33b3fa9bf61d0ff311f6b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment (2021, background): “The Kappa coefficient can be interpreted as a measure of agreement beyond chance compared to the maximum possible beyond chance agreement [30], [31].”
- Five ways to look at Cohen's kappa (2015, background): “However, since kappa has several useful interpretations, it is likely that it continues to be a standard tool for summarizing inter-rater agreement on a nominal scale in the near future [1,3,4,11,12].”