Another factor is the number of codes. As the number of codes increases, kappas become higher. Based on a simulation study, Bakeman and colleagues concluded that for fallible observers, Kappa values were lower when codes were lower. And in accordance with Sim-Wright`s claim on prevalence, kappas were higher than the codes were about equal. Thus Bakeman et al. concluded that no Kappa value could be considered universally acceptable. :357 They also provide a computer program that allows users to calculate values for Kappa that indicate the number of codes, their probability and the accuracy of the observer. If, for example, the codes and observers of the same probability, which are 85% accurate, are 0.49, 0.60, 0.66 and 0.69 if the number of codes 2, 3, 5 and 10 is 2, 3, 5 and 10. The prevalence of characteristics was calculated on the basis of the number of positive cases judged by the two counsellors, and then as a percentage of the total number of cases and the reliability between the counsellors (Tables 3, 4 and 5). In calculating the prevalence of Avoidant in the VU-MN pair (Table 3), the number of cases where counsel agreed to each other was 5, which was calculated as a percentage of the total number of cases (19), resulting in a prevalence rate of 26.32%. Table 6 shows a summary of the comparison between Cohens Kappa and Gwets AC1 based on the prevalence rate for each.

When the prevalence rate was higher, Cohen`s Kappa and compliance height were also; On the other hand, the values of Gwets AC1 did not change dramatically with the prevalence compared to Cohens Kappa, but remained close to the percentage of the agreement. Kappa measures the agreement. A perfect match is when all counts will fall on the main diagonal of the table, and the probability of a chord will be equal to 1. e (K) – the probability of a random agreement – A 1 N ∗ B 1 N – A 2 N ∗ B 2 N In table 3 x 3, there are two options that would not allow an agreement to be reached (which indicates an account): another hypothesis of interest is to assess whether two different reviewers agree with each other or whether two different evaluation systems are identical. This has important applications in medicine, where two physicians may be asked to evaluate the same group of patients for further treatment. Keep in mind that these guidelines may not be sufficient for health-related research and testing. Objects such as X-ray measurements and test results are often evaluated subjectively. While interratist approval of .4 might be acceptable for a general investigation, it is usually too low for something like cancer screening. Therefore, you generally want to have a higher level for the acceptable reliability of interrater it when it comes to health.

Percentage of match on multiple data collectors (fictitious data). The concept of “advisor agreement” is quite simple and, for many years, the reliability of Interraters has been measured as a percentage of match among data collectors. To obtain the measurement agreement, the statistician established a matrix in which the columns represent the different advisors and the lines of the variables for which the raters had collected data (Table 1). The cells in the matrix contained the values captured by the data collectors for each variable. An example of this procedure can be made in Table 1. In this example, there are two advisors (Mark and Susan). They each record their values for variables 1 to 10. To obtain a percentage of approval, the researcher subtracted Susan`s scores from Marks Scores and counted the resulting number of zeroes. Dividing the number of zeros by the number of variables provides a measure of the agreement between advisors.