The resulting estimate of Cohen`s kappa, average per pair of coders, is 0.68 (pair of kappa coders = 0.62 [coders 1 and 2], 0.61 [coders 2 and 3] and 0.80 [coders 1 and 3], indicating an essential correspondence according to Landis and Koch (1977). In the SPSS, only Kappa is provided by Siegel and Castellan, and Kappa per pairs of encoders is averaged by 0.56, indicating moderate convergence (Landis & Koch, 1977). According to Krippendorffs (1980), a more conservative cutoffs, Cohen Kappa`s estimate could indicate that conclusions about coding accuracy should be discarded, while Siegel & Castellan`s Kappa estimate could indicate that preliminary conclusions will be drawn. The report on these results should describe in detail the particularities of the selected Kappa variant, provide a qualitative interpretation of the estimate, and describe any effects of the estimate on statistical relevance. For example, the results of this analysis can be reported as follows: Cohen (1968) offers an alternative weighted kappa that allows researchers to punish disagreements in a differentiated way depending on the extent of the disagreement. Cohen`s weighted kappa is typically used for categorical data with an ordinal structure, for example.B. in an evaluation system categorizing the high, medium, or low presence of a particular attribute. In this case, a subject considered high by one programmer and a low subject by another should lead to a lower IRR estimate than one subject considered high by one programmer and another as average. Norman and Streiner (2008) show that the use of a weighted kappa with square weights for ordinal scales is identical to a two-sided mixed consistency CCI and that both can be replaced. This interchangeability has a particular advantage when three or more encoders are used in a study, as CICs may contain three or more encoders, while weighted kappa can only absorb two encoders (Norman &Streiner, 2008).
Variations between evaluators in measurement methods and variability in the interpretation of measurement results are two examples of sources of error in evaluation measures. The reliability of ambiguous or difficult measurement scenarios requires clear guidelines for rendering evaluations. Many research designs require an assessment of interconceptional reliability (IRR) in order to demonstrate consistency between observational assessments of multiple programmers….