Percent Of Agreement Statistics

Cohens Kappa`s estimate, which is obtained by pair of coders, is 0.68 (estimates of the pair of kappa codex – 0.62 [codes 1 and 2], 0.61 [codes 2 and 3] and 0.80 [coder 1 and 3], indicating a substantial agreement according to landis and koch (1977). In SPSS, only Kappa seals and Castellans are provided, and Kappa, average on pairs of coders, is 0.56, indicating a moderate agreement (Landis-Koch, 1977). According to the more conservative cutoffs of Krippendorff (1980), Cohen`s kappa estimate might indicate that conclusions on coding fidelity should be discarded, while Siegel-Castellan`s Kappa estimate may indicate that preliminary conclusions will be drawn. Reports on these results should detail the specifics of the chosen kappa variant, provide a qualitative interpretation of the estimate, and describe all the effects of the estimate on statistical performance. For example, the results of this analysis can be reported as follows: until now, the discussion has assumed that the majority was correct and that minority evaluators were wrong in their assessments and that all advisors had deliberately made a rating choice. Jacob Cohen understood that the hypothesis could be false. Indeed, he stated expressly that “in the typical situation, there is no criterion for “correction” of judgments (5). Cohen suggests the possibility that at least for some of the variables, none of thevines were sure of the score to be entered and simply made random assumptions. In this case, the agreement reached is a false agreement. Cohen`s Kappa was designed to address this concern. A final concern about the reliability of advisors was introduced by Jacob Cohen, a leading statistician who, in the 1960s, developed key statistics to measure the reliability of interrater, Cohens Kappa (5). Cohen indicated that there will likely be some degree of match among data collectors if they do not know the correct answer, but if they simply guess.

He assumed that a number of conjectures would be speculated and that insurance statistics should be responsible for this fortuitous agreement. He developed Kappa`s statistics as an understanding of this random agree factor. 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] The weighted Kappa allows for different weightings of differences of opinion[21] and is particularly useful when the codes are sorted. [8]:66 Three matrixes are involved, the matrix of observed scores, the matrix of expected values based on random tuning and the weight matrix.