An agreement does not always mean a contract, because it may lack an essential element of the contract, such as counterparty.B. The same principle should logically apply to the assessment of the concordance between two advisers or tests. In this case, we have the opportunity to calculate the shares of the specific positive agreement (PA) and the specific negative agreement (NA) that are closely compatible with Se and Sp. By verifying that the PA and NPA are acceptable, extreme base interest rates are protected from unwarranted capitalization when assessing the amount of the missed agreement. 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. So why isn`t a similar random correction taken into account in the case of Se? The answer is probably that if you enjoy yourself, you also enjoy the sp. generally. The combined use of the two indices avoids the possibility that an extreme marginal division could lead to a poor diagnostic test. If a test and gold standard is independent or lowly associated and base interest rates are extreme — the usual situation where a random correction becomes a potential problem — both sp will be high. The overall probability of a random agreement is the probability that they have agreed on a yes or no vote, i.e. the weighted Kappa allows differences of opinion to be weighted differently and is particularly useful when the codes are sorted. :66 Three matrixes are involved, the matrix of observed scores, the matrix of expected values based on random tuning and the weight matrix.