Example 5. For the table assigned to the category, the kappa intraclass [26, page 276] can be defined as the letter originally used by Scott [9]. Bloch and Kraemer [26] have shown that this coefficient can be used as part of an agreement. The intra-classical kappa meets the classic definition of reliability [15, 18]. We can write (14) as, therefore, . The prevalence of the code does not matter much with the increase in the code number. If the number of codes 6 is greater than or equal to 6, the variability of prevalence is not significant and the standard deviation of Kappa values obtained accurately by observers .80, 85, 90 and 85 is less than 0.01. 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. Lemma 16 shows that we can either take the average of randomly corrected versions of coefficients, or a weighted average of coefficients, and then correct the total coefficient for concordance due to chance. The result will be the same. The coefficient (45) contains two quantities to indicate, namely the expectation and the sum of the differences. With, for fixed, and in (6a) and (6b), (8) and (9), (13a) and (13b), or (15a) and (15b) we get identity (46) shows that all coefficients treated in section 2 belong to a given family of linear transformations. An example of a coefficient that does not belong to this family is the phi coefficient (50).
For more examples, see [22]. Suppose you analyze data for a group of 50 people applying for a grant. Each grant proposal was read by two readers, and each reader said “yes” or “no” to the proposal. Suppose the data for the disagreement count were as follows, where A and B are drives, the data on the main diagonal of the matrix (a and d) count the number of chords and the non-diagonal data (b and c) count the number of disagreements: Lemma 15 shows that if we apply to intraclass kappas in example 5 , we get Scotts. In most applications, Kappa`s size is generally more interested than the statistical significance of Kappa. The following classifications have been proposed to interpret the strength of the agreement on the basis of Cohen`s Kappa value (Altman 1999, Landis JR (1977) The weighted Kappa allows for different weightings of differences of opinion[21] and is particularly useful when codes are ordered. [8]:66 Three matrixes are involved, the matrix of observed scores, the matrix of expected values based on random tuning and the weight matrix.