The agreement and the pre-agreement actually observed constitute a random agreement. Subsequent extensions of the approach included versions that could deal with “under-credits” and ordinal scales.  These extensions converge with the intra-class correlation family (ICC), which allows us to estimate reliability for each level of measurement, from the notion (kappa) to the ordinal (or ICC) at the interval (ICC or ordinal kappa) and the ratio (ICC). There are also variations that may consider the agreement by the evaluators on a number of points (for example.B. two people agree on the rates of depression for all points of the same semi-structured interview for a case?) as well as cases of raters x (for example. B how do two or more evaluators agree on whether 30 cases have a diagnosis of depression, yes/no a nominal variable). This is calculated by ignoring that pe is estimated from the data and treating in as an estimated probability of binomial distribution, while asymptomatic normality is used (i.e. assuming that the number of items is large and that this in is not close to 0 or 1). S E – Display style SE_ -kappa (and CI in general) can also be enjoyed with bootstrap methods. 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 dissent is 14/16 or 0.875.
The disagreement is due to the quantity, because the assignment is optimal. Kappa is 0.01. Another approach to concordance (useful when there are only two advisors and the scale is continuous) is to calculate the differences between the observations of the two advisors.