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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.
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 ...
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.
The most commonly used measure of agreement between observers is Cohen's kappa. The value of kappa is not always easy to interpret and it may perform poorly if the values are asymmetrically distributed. It also requires that the data be independent. The delta statistic may be of use when faced with the potential difficulties.
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 ...
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From November 2010 to December 2012, if you bought shares in companies when Erroll B. Davis, Jr. joined the board, and sold them when he left, you would have a -15.7 percent return on your investment, compared to a 19.2 percent return from the S&P 500.