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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]
Cohen's kappa, [5] which works for two raters, and Fleiss' kappa, [6] an adaptation that works for any fixed number of raters, improve upon the joint probability in that they take into account the amount of agreement that could be expected to occur through chance.
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.
Indeed, Cohen's kappa explicitly ignores all systematic, average disagreement between the annotators prior to comparing the annotators. So Cohen's kappa assesses only the level of randomly varying disagreements between the annotators, not systematic, average disagreements. Scott's pi is extended to more than two annotators by Fleiss' kappa.
Krippendorff's alpha coefficient, [1] named after academic Klaus Krippendorff, is a statistical measure of the agreement achieved when coding a set of units of analysis.. Since the 1970s, alpha has been used in content analysis where textual units are categorized by trained readers, in counseling and survey research where experts code open-ended interview data into analyzable terms, in ...
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Fleiss' kappa, like F-score, assumes that both variables are drawn from the same distribution and thus have the same expected prevalence, while Cohen's kappa assumes that the variables are drawn from distinct distributions and referenced to a model of expectation that assumes prevalences are independent. [8]