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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.
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.
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.
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]
Jacob Cohen (April 20, 1923 – January 20, 1998) was an American psychologist and statistician best known for his work on statistical power and effect size, which helped to lay foundations for current statistical meta-analysis [1] [2] and the methods of estimation statistics. He gave his name to such measures as Cohen's kappa, Cohen's d, and ...
AgreeStat 360: cloud-based inter-rater reliability analysis, Cohen's kappa, Gwet's AC1/AC2, Krippendorff's alpha, Brennan-Prediger, Fleiss generalized kappa, intraclass correlation coefficients; A useful online tool that allows calculation of the different types of ICC
Cohen's kappa: measures inter-rater agreement for categorical items; Friedman two-way analysis of variance (Repeated Measures) by ranks: tests whether k treatments in randomized block designs have identical effects; Empirical likelihood; Kaplan–Meier: estimates the survival function from lifetime data, modeling censoring
One of the problems with Cohen's Kappa is that it does not always produce the expected answer [1]. For instance, in the following two cases there is much greater agreement between A and B in the first case [why?] than in the second case and we would expect the relative values of Cohen's Kappa to reflect this. However, calculating Cohen's Kappa ...
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