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 agree, 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, there is a similar number of evaluations in each class. , in the first case, note 70 votes in for and 30 against, but these numbers are reversed in the second case.) 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. Cohens Kappa computational value for each: Kappa is a form of correlation coefficient. Correlation coefficients cannot be interpreted directly, but a square correlation coefficient, called the Determination Coefficient (COD), is directly interpretable. The COD is explained as the amount of variation in the dependent variable that can be explained by the independent variable. Whereas the true COD is calculated only on the pearson r, an estimate of the variance taken into account for each correlation statistic can be obtained by squaring the correlated value. The squaring of the Kappa is conceptually translated into accuracy (i.e. the reversal of the error) in the data because of the congruence between the data collectors. Figure 2 shows an estimate of the amount of correct and false data in research data sets based on the degree of congruence, as measured by the percentage agreement or kappa. On the other hand, if there are more than 12 codes, the expected Kappa value increment becomes flat. As a result, the percentage of the agreement could serve the purpose of measuring the amount of the agreement. In addition, the increment of the sensitivity performance metric apartment values also reaches the asymptote of more than 12 codes. To address this problem, most clinical trials now express the Interobserver agreement using Kappa`s statistics, which normally have values between 0 and 1.
(The appendix at the end of this chapter shows how statistics are calculated.) A value of 0 indicates that the match observed is accurate with that expected at random, and a value of 1 indicates a perfect match. According to the agreement, a value of 0 to 0.2 indicates a slight agreement; 0.2 to 0.4 fair agreement; 0.4 to 0.6 moderate agreement; 0.6 to 0.8 essential agreement; and from 0.8 to 1.0 almost perfect match.† Rarely physical characters have values below 0 (theoretically as low as -1), suggesting that the observed chord was worse than the random chord. Many situations in the health sector rely on multiple people to collect research or clinical laboratory data. The question of consistency or consistency between data-gathering individuals arises immediately because of variability among human observers. Well-designed research studies must therefore include methods to measure the consistency between different data collectors. Study projects generally include the training of data collectors and the extent to which they record the same values for the same phenomena. Perfect match is rarely achieved and confidence in the study results depends in part on the amount of disagreements or errors introduced in the study due to inconsistencies between the data collectors. The extent of the match between the data collectors is called „the reliability of the Interrater.” The increase in the number of codes leads to a gradually smaller increase in Kappa. If the number of codes is less than five, and especially if K-2, The lower Kappa values are acceptable, but the variability in prevalence must also be taken into account. For only two codes, The highest value of Kappa is .80 observers accurately .95, and the lowest value is the cappa .02 of observers accurately .80.