Kappa will only address its maximum theoretical value of 1 if the two observers distribute codes in the same way, i.e. if the corresponding totals are the same. Everything else is less than a perfect match. Nevertheless, the maximum value Kappa could achieve helps, as uneven distributions help interpret the actual value received from Kappa. The equation for the maximum is: [16] Kappa is an index that takes into account the observed agreement with a basic agreement. However, investigators must carefully consider whether Kappa`s core agreement is relevant to the research issue. Kappa`s baseline is often called random tuning, which is only partially correct. The basic agreement of Kappa is the agreement that could be expected because of the accidental allocation, given the quantities declared in quantity in the limit amounts of the square emergency table. Kappa – 0 if the observed attribution appears to be random, regardless of the quantitative opinion limited by the limit amounts. However, for many applications, investigators should be more interested in quantitative opinion in marginal amounts than in attribution opinion, as described in the supplementary information on the diagonal of the square emergency table. Kappa`s base is therefore more entertaining than illuminating for many applications.

Take the following example: If you have multiple advisors, calculate the percentage agreement as follows: The fundamental measure of the reliability of inter-advisors is a percentage of correspondence between advisors. In statistics, reliability between advisors (also cited under different similar names, such as the inter-rater agreement. B, inter-rated matching, reliability between observers, etc.) is the degree of agreement between the advisors. This is an assessment of the amount of homogeneity or consensus given in the evaluations of different judges. Some researchers have expressed concern about the tendency to take into account the frequency of observed categories as circumstances, which may make it unreliable for measuring matches in situations such as the diagnosis of rare diseases. In these situations, the S tends to underestimate the agreement on the rare category. [17] This is why the degree of convergence is considered too conservative. [18] Others[19][citation necessary] dispute the assertion that kappa “takes into consideration” the coincidence agreement. To do this effectively, an explicit model of the impact of chance on councillors` decisions would be needed. The so-called random adjustment of Kappa`s statistics assumes that, if they are not entirely sure, the advisors simply guess – a very unrealistic scenario.