Now, if we apply our formula for Cohen`s Kappa, we get the disagreement rate to be 14/16 or 0.875. The disagreement is due to the quantity, because the allocation is optimal. Kappa is 0.01. A similar statistic, called Pi, was proposed by Scott (1955). Cohens Kappa and Scotts Pi differ as to how pe is calculated. If you have multiple evaluators, calculate the percentage of agreement as follows: the relative concordance observed between the evaluators is (identical to the accuracy) and pe is the hypothetical probability of a random agreement, with the observed data being used to calculate the probabilities that each observer will see each category by chance. If the evaluators completely match, then κ = 1 {textstyle kappa =1}. If there is no match between the evaluators other than what is expected by chance (as indicated by pe), κ = 0 {textstyle kappa =0}. It is possible that the statistics are negative[6], implying that there is no effective agreement between the two evaluators or that the agreement is worse than chance. Kappa = ((Observed agreement) – (Expected agreement)) /(A+B+C+D) – (Expected agreement) To calculate the percentage of agreement, you need to calculate the percentage of the difference between two digits.

This value can be useful if you want to see the difference between two numbers as a percentage. Scientists can use the percentage of agreement between two numbers to display the percentage of the relationship between the different results. To calculate the percentage difference, you need to take the difference in the values, divide them by the average of the two values, and then multiply this number by 100. where k=number of codes and w i j {displaystyle w_{ij}} , x i i j {displaystyle x_{ij} } and m i j {displaystyle m_{ij} are elements of weighting, observed and expected matrices. If the diagonal cells contain weights of 0 and all the off-diagonal cells contain weights of 1, this formula produces the same kappa value as the calculation shown above. Some researchers have raised concerns about the tendency of κ to take as data the frequencies of the observed categories, which may make them unreliable for measuring concordance in situations such as the diagnosis of rare diseases. . . .