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 / sum – (20 – 15) / 50 – 0.70. Fleiss` Kappa is a generalization of Scott`s statistics[2] a statistical measure of the reliability of inter-advisors. [3] It is also related to Cohen`s kappa statistics and Youdens J`s, which may be more appropriate in some cases. While Scotts pi and Cohens Kappa work for only two advisors, Fleiss` Kappa works for any number of advisors who give categorical ratings for a fixed number of articles. It can be interpreted to mean the extent to which the agreement observed between the advisors exceeds what would be expected if all the advisors issued their ratings at random. It is important to note that while Cohenkappa assumes that the same two advisors have evaluated a number of objects, Fleiss` Kappa explicitly allows that although there is a fixed number of advisors (for example. B three), different objects can be evaluated by different individuals (Fleiss 1971, p.

378). In other words, point 1 is assessed by Councillors A, B and C; But point 2 could be evaluated by DenRatern D, E and F. Historically, percentage match (number of chord results/total points) was used to determine the reliability of Interraters. But a random arrangement on the basis of advice is always a possibility – just as a “correct” answer to a multiple-choice test is possible. Kappa`s statistics take this element into account. Fleiss` kappa (named after Joseph L. Fleiss) is a statistical measure of the reliability of the match between a fixed number of advisors in assigning categorical evaluations to a number of articles or job classification. This contrasts with other kappas like Kappa Cohens, which only work if the agreement between no more than two advisors or intra-rater reliability (for an expert versus himself) is evaluated.

The measure calculates the degree of compliance in the classification above what would be expected by chance. As Marusteri and Bacarea (9) have found, there is never 100% certainty about the results of the research, even if the statistical significance is reached. The statistical results used to test hypotheses about the relationship between independent and dependent variables are meaningless when there are inconsistencies in the evaluation of variables by evaluators. If the agreement is less than 80%, more than 20% of the data analysed is wrong. With a reliability of only 0.50 to 0.60, it is understandable that 40 to 50% of the data analyzed is wrong. If Kappa values are less than 0.60, the confidence intervals around the received kappa are so wide that it can be assumed that about half of the data may be false (10). It is clear that statistical significance does not mean much when there are so many errors in the results tested. 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.

A good example of concern about the importance of Kappa`s results is a paper that compares visual detection of abnormalities in biological samples by humans with automated detection (12). The results showed only a moderate agreement between human and automated advisors for kappa (n-0.555), but the same data showed excellent percentage match of 94.2%.