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] As noted above, correlation does not mean agreement. The correlation refers to the existence of a relationship between two different variables, while the agreement considers the agreement between two measures of a variable. Two sets of observations, strongly correlated, may have a poor agreement; However, if the two sets of values agree, they will certainly be strongly correlated. For example, in the hemoglobin example, the correlation coefficient between the values of the two methods is high, although the agreement is poor [Figure 2]; (r – 0.98). The other way of looking at it is that, although the different points are not close enough to the dotted line (least square line; [2], indicating a good correlation), these are quite far from the running black line that represents the perfect chord line (Figure 2: the black line running).

If there is a good agreement, the dots should fall on or near this line (of the current black line). Concordance limits – average difference observed ± 1.96 × standard deviation of observed differences. Confidence intervals. The forest method or “normal approximation” evaluates the confidence limits on the one hand as follows: CL – SE × zcrit (3.2) CU – in- SE × zcrit (3.3), where SE is here SE (in), as by Eq. (3.1), cl and CU, are the lower and upper trust limits, and zcrit is the z value associated with a trust zone. For a 95% Zcrit confidence range – 1.96; for a 90% confidence range, zcrit – 1,645. For ordination data, where there are more than two categories, it is useful to know whether the evaluations of the various counsellors end slightly or vary by a significant amount. For example, microbiologists can assess bacterial growth on cultured plaques such as: none, occasional, moderate or confluence. In this case, the assessment of a plate given by two critics as “occasional” or “moderate” would mean a lower degree of disparity than the absence of “growth” or “confluence.” Kappa`s weighted statistic takes this difference into account.

It therefore gives a higher value if the evaluators` responses correspond more closely with the maximum scores for perfect match; Conversely, a larger difference in two credit ratings offers a value lower than the weighted kappa. The techniques of assigning weighting to the difference between categories (linear, square) may vary. Statistical methods for evaluating the agreement vary depending on the nature of the variables examined and the number of observers between whom an agreement is sought. These are summarized in Table 2 and explained below. Graham P, Bull B. Approximate standard errors and confidence intervals for positive and negative approval indices. J Clin Epidemiol, 1998, 51(9), 763-771. In Table 2, the share of Category I-specific agreements is 2nii ps (i) – ———.