Inter Rater Agreement Multiple Raters

Many research projects require an evaluation of the reliability of the Inter-Rater (IRR) to demonstrate consistency between the observation assessments of several coders. However, many studies use erroneous statistical methods, do not fully report the information needed to interpret their results, or do not report how ERREURS influence the performance of their subsequent analyses for hypothesis tests. This paper provides an overview of methodological issues related to the evaluation of ERREURS, with an emphasis on the design of studies, the selection of appropriate statistics and the calculation, interpretation and disclosure of some frequently used IRR statistics. Examples of calculations include SPSS and R syntaxes for Cohens Kappa calculation and intra-class correlations for IRR evaluation. The plot of the ranks of spleens, appreciated by the Bavarian latent model, with 95% of credible limits. The common probability of an agreement is the simplest and least robust measure. It is estimated as a percentage of the time advisors agree in a nominal or categorical evaluation system. It ignores the fact that an agreement can only be made on the basis of chance. The question arises as to whether a random agreement should be “corrected” or not; Some suggest that such an adaptation is in any case based on an explicit model of the impact of chance and error on business decisions. [3] Although models of latent characteristics have been criticized because the underlying variable is at an arbitrary and uninterpretable scale [1], other authors have shown how estimated parameters are related to known synthetic statistics such as sensitivity, specificity and forecasts [27], [29]. Models of this type have therefore been used in a number of different applications, including the agreement between advisors [30], [31]. The possible values for Kappa`s statistics range from 1 to 1, 1 indicating a perfect match, 0, indicating a totally random match, and 1, indicating a “perfect” divergence.

Landis and Koch (1977) provide guidelines for interpreting Kappa`s values, values between 0.0 and 0.2 being slightly consistent, 0.21 to 0.40, indicating fair consent, from 0.41 to 0.60, indicating moderate support, from 0.61 to 0.80 and from 0.81 to 1.0, indicating near-perfect or perfect consistency. The use of these qualitative limit values, however, is under discussion and Krippendorff (1980) gives a more conservative interpretation that suggests that conclusions should be reduced for variables with values below 0.67, conclusions for values between 0.67 and 0.80, and final conclusions for values greater than 0.80. However, in practice, Kappa coefficients below Krippendorff`s conservative Cutoff values are often retained in research studies, and Krippendorff proposes these cutoffs on the basis of his own content analysis work, while acknowledging that acceptable estimates of IRR vary according to study methods and the research issue.