The correspondence between the measurements refers to the degree of correspondence between two (or more) measures. Statistical methods used to verify compliance are used to assess the variability of inter-variability or to decide whether one variable measurement technique can replace another. In this article, we examine statistical measures of compliance for different types of data and discuss the differences between them and those for assessing correlation. 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 the maximum is: [16] 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. 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. It can be seen that there is a fairness to a good agreement between counsellors with respect to the assessment of participants as having “depression,” “personality disorder,” “schizophrenia” and “others”; But there is a misreprescement in the diagnosis of “neurosis.” Weighted Kappa partly compensates for a problem with unweighted kappa, namely that it is not suited to the degree of disagreement. Disagreement is weighted as a decreasing priority by the upper left (origin) of the table. StatsDirect uses the following definitions of weight (1 is the default setting): It is important to note that in each of the three situations in Table 1, the pass percentages are the same for both reviewers, and if the two reviewers are compared to a standard test of 2 × 2 for the coupled data (McNemar test), there would be no difference between their performance; On the other hand, the agreement between the observers is very different in these three situations. The basic idea that must be understood here is that “agreement” quantifies the agreement between the two examiners for each of the “couples” of the scores, not the similarity of the total pass percentage between the examiners.

Methods for assessing the consistency between observers based on the nature of the variables measured and the number of Gwets AC1 observers are the statistics of choice for two advisors (Gwet, 2008). The Gwet agreement coefficient can be used in more contexts than kappa or pi, as it does not depend on the acceptance of independence between councillors. If you have only two categories, scotts is more reliable than kappa (with confidence intervals using the Eliasziw Thunder method (1992) for the Inter-Rater Agreement (Zwick, 1988). For the three situations described in Table 1, the use of the McNemar test (designed to compare coupled categorical data) would not make a difference. However, this cannot be construed as evidence of an agreement. The McNemar test compares the total proportions; Therefore, any situation in which the total share of the two examiners in Pass/Fail (for example. B situations 1, 2 and 3 in Table 1) would result in a lack of differences.