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Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories. The definition of is =, where p o is the relative observed agreement among raters, and p e is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly selecting each category.
Scott's pi (named after William A Scott) is a statistic for measuring inter-rater reliability for nominal data in communication studies.Textual entities are annotated with categories by different annotators, and various measures are used to assess the extent of agreement between the annotators, one of which is Scott's pi.
In statistics, inter-rater reliability (also called by various similar names, such as inter-rater agreement, inter-rater concordance, inter-observer reliability, inter-coder reliability, and so on) is the degree of agreement among independent observers who rate, code, or assess the same phenomenon.
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Fleiss' kappa is a generalisation of Scott's pi statistic, [2] a statistical measure of inter-rater reliability. [3] It is also related to Cohen's kappa statistic and Youden's J statistic which may be more appropriate in certain instances. [4]
Bennett et al. suggested adjusting inter-rater reliability to accommodate the percentage of rater agreement that might be expected by chance was a better measure than simple agreement between raters. [2] They proposed an index which adjusted the proportion of rater agreement based on the number of categories employed.
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Cicchetti (1994) [19] gives the following often quoted guidelines for interpretation for kappa or ICC inter-rater agreement measures: Less than 0.40—poor. Between 0.40 and 0.59—fair. Between 0.60 and 0.74—good. Between 0.75 and 1.00—excellent. A different guideline is given by Koo and Li (2016): [20] below 0.50: poor; between 0.50 and 0 ...