Kappa will only address its maximum theoretical value of 1 if the two observers distribute codes in the same way, i.e. if the corresponding totals are the same. Everything else is less than a perfect match. Nevertheless, the maximum value Kappa could achieve helps, as uneven distributions help interpret the actual value received from Kappa. The equation for maximum is:[16] The pioneer paper introduced by Kappa as a new technique was published in 1960 by Jacob Cohen in the journal Educational and Psychological Measurement. [5] Cohen`s Kappa coefficient () is a statistic used to measure reliability between rats (as well as the reliability of intra-rater services and services) for qualitative (category) elements. [1] It is generally accepted that this is a more robust indicator than a simple percentage of the agreement calculation, since the possibility of a random agreement is taken into account. There are controversies around Cohens Kappa because of the difficulty of interpreting the indications of the agreement. Some researchers have suggested that it is easier, conceptually, to assess differences of opinion between objects. [2] For more details, see Restrictions. Cohens Kappa measures the agreement between two advisors who classify each of the N elements into exclusion categories C.

The definition of “textstyle” is as follows: where is the relative correspondence observed among advisors (identical to accuracy), and pe is the hypothetical probability of a random agreement, the observed data being used to calculate the probabilities of each observer who sees each category at random. If the advisors are in complete agreement, it`s the option ” 1″ “textstyle” “kappa – 1.” If there is no agreement between advisors who are not expected at random (as indicated by pe), the “textstyle” option is given by the name “. The statistics may be negative,[6] which implies that there is no effective agreement between the two advisers or that the agreement is worse than by chance. 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. [12]: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.