Historically, percentage match (number of chord results/total points) has been 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. Example sas (19.3_agreement_Cohen.sas): two radiologists evaluated 85 patients for liver damage. Assessments were made on an ordinal scale such as: Some researchers have raised concerns about the trend of observed frequency categories, which may make it unreliable for measuring compliance 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. Here, the coverage of quantity and opinion is instructive, while Kappa hides the information. In addition, Kappa poses some challenges in calculating and interpreting, because Kappa is a report. It is possible that the Kappa report returns an indefinite value due to zero in the denominator.

In addition, a report does not reveal its meter or denominator. For researchers, it is more informative to report disagreements in two components, quantity and allocation. These two components more clearly describe the relationship between categories than a single synthetic statistic. If prediction accuracy is the goal, researchers may more easily begin to think about opportunities to improve a forecast using two components of quantity and assignment rather than a Kappa report. [2] In research projects in which they have two or more advisors (also known as “judge” or observer) responsible for measuring a variable on a category scale, it is important to determine whether these advisors agree. Cohens Kappa () is such a measure for the Inter-Rater agreement for categorical scales when there are two advisers (the Greek letter is “kappa” in tiny). Kappa statistics are often used to test the reliability of interreters. The importance of the reliability of reference values lies in the fact that it represents the extent to which the data collected in the study are correct representations of the measured variables. The measurement of the extent to which data collectors assign the same score to the same variables is called the reliability of the interrater.

Although there were many methods for measuring the reliability of Interraters, they were traditionally measured as a percentage of agreement, calculated as the number of chord results divided by the total number of points. In 1960, Jacob Cohen criticized the use of the agreement as a percentage because of its inability to take random agreement into account. He introduced the Cohen-Kappa, which was designed to take into account the possibility that the spleens, due to uncertainty, guessed at least a few variables. Like most correlation statistics, the kappa can be between 1 and 1. While the Kappa is one of the most used statistics to test the reliability of interramas, it has limitations. Judgments about the level of Kappa that should be acceptable for health research are questioned. Cohen`s proposed interpretation may be too lenient for health-related studies, as it implies that a value of up to 0.41 might be acceptable. Kappa and approval percentage are compared, and levels for Kappa and percentage approval that should be requested in health studies. The Cohen-Kappa is a statistical coefficient that represents the degree of accuracy and reliability in a statistical classification.