The probability that both of them will say yes by chance is: Step 2: Find the probability that the advisors will both say yes by chance. Rater A said yes to 25/50 images, or 50% (0.5). Rater B said yes to 30/50 images or 60% (0.6). The overall probability of the spleens, both of whom say yes to random, is: 0.5 – 0.6 – 0.30. Another factor is the number of codes. As the number of codes increases, kappas become higher. Based on a simulation study, Bakeman and colleagues concluded that for fallible observers, Kappa values were lower when codes were lower. And in accordance with Sim-Wright`s claim on prevalence, kappas were higher than the codes were about equal. Thus Bakeman et al. concluded that no Kappa value could be considered universally acceptable. :357 They also provide a computer program that allows users to calculate values for Kappa that indicate the number of codes, their probability and the accuracy of the observer. If, for example, the codes and observers of the same probability, which are 85% accurate, are 0.49, 0.60, 0.66 and 0.69 if the number of codes 2, 3, 5 and 10 is 2, 3, 5 and 10. Using explicit models of rat decision-making, a valid random correction could perhaps be applied (Uebersax, 1987).
This would require both a theoretically acceptable model and sufficient data to empirically verify the compliance of the observed data with the model. In any event, this becomes an exercise in modelling of the failure agreements (Agresti, 1992; Uebersax, 1993) — instead of calculating a simple index. To calculate pe (the probability of a fortuitous agreement), we note that: It is clear that Se and Sp are very widespread and trustworthy clues, and if it was necessary to correct them on a case-by-case basis, this would have been mentioned a long time ago. There is simply no need and the same principle should apply to the measure of the agreement between two tests or advisors. A case that is sometimes considered a problem with Cohen`s Kappa occurs when comparing the Kappa, which was calculated for two pairs with the two advisors in each pair that have the same percentage of agreement, but one pair gives a similar number of reviews in each class, while the other pair gives a very different number of reviews in each class.  (In the following cases, the B grade has 70 jas and 30 no, in the first case, but these numbers are reversed.) For example, in the following two cases, there is an equal agreement between A and B (60 out of 100 in both cases) with respect to matching in each class, so we expect Cohens Kappa`s relative values to reflect that. However, Cohen Kappa`s calculation for everyone: Theoretically, if one could estimate how many agreements are produced by common councils, their effect could be removed to produce a more accurate measure of the “true” concordance. That`s what the kappa coefficient claims to do, but really doesn`t. First, we should ask ourselves what a chance agreement is.
This is a plausible view: if the spleens are uncertain as to the correct classification, a certain degree of conjecture may arise. The rate may be total (as in “I make a complete assumption here”) or partial (z.B. “My choice is partly based on conjecture”). If two advisors advise them both, they will sometimes agree. The question then is whether such agreements should be included in a statistical index of the agreement. Kappa is an index that takes into account the agreement observed with regard to a basic agreement.