Concordance limits – average difference observed ± 1.96 × standard deviation of observed differences. The following formula is used for correspondence between two advisors. If you have more than two advisors, you must use a formula variant. In SAS, for example, the procedure for Kappa is PROC FREQ, while you need to use the SAS MAGREE macro for multiple debtors. and the general tabular agreement, that is, p-o, for each simulated sample. The po for actual data is considered statistically significant if it represents a certain percentage (for example. B 5%) more than 2000. The values of the p-o. Step 1: Calculate in (the proportional agreement observed): 20 images were rated yes by both. 15 images were judged not by both. So, Po – number in agreement / total – (20 – 15) / 50 – 0.70.
The total number of effective agreements, regardless of the category, corresponds to the sum of Eq. (9) for all categories or C O – TOTAL S (d). (13) J-1 The total number of possible chords is K Oposs – SUM nk (nk – 1). (14) k-1 Eq Division. (13) by Eq. (14) indicates the total percentage of the observed agreement, or O in——-. (15) Oposs It is important to note that in each of the three situations in Table 1, the pass percentages are the same for both reviewers, and if the two reviewers are compared to a typical test of 2 × 2 for the coupled data (McNemar`s test), there would be no difference between their performance; On the other hand, the agreement between the observers is very different in these three situations. The basic idea that must be understood here is that „agreement“ quantifies the agreement between the two examiners for each of the „couples“ of the scores, not the similarity of the total pass percentage between the examiners. If statistical significance is not a useful guide, what is Kappa`s order of magnitude that reflects an appropriate match? The guidelines would be helpful, but other factors than the agreement may influence their magnitude, making it problematic to interpret a certain order of magnitude. As Sim and Wright have noted, two important factors are prevalence (codes are likely or vary in probabilities) and bias (marginal probabilities are similar or different for both observers). Other things are the same, kappas are higher when the codes are equal.