If you have multiple reviewers, calculate the percentage agreement as follows: As you can probably see, calculating percentage chords can quickly prove to be a good one for more than a handful of reviewers. For example, if you had 6 judges, you would have 16 pair combinations to calculate for each participant (use our combination calculator to find out how many couples you would get for multiple judges). In this competition, the judges agreed on 3 points out of 5. The percentage of concordance is 3/5 = 60%. Inter-board reliability is the degree of adequacy between evaluators or judges. If everyone agrees, IRR is 1 (or 100%) and if everyone does not agree, IRR is 0 (0%). There are several methods of calculating the IRR, from the simple (e.g.B. percentage overset) to the most complex (e.g.B Cohen`s Kappa). What you choose depends largely on the type of data you have and the number of evaluators in your model. The basic measure of reliability among evaluators is a percentage of agreement between evaluators.

Intraclass correlation (ICC) is one of the most frequently used statistics to assess IRR for ordinal, intermittent and relational variables. CICs are suitable for studies involving two or more coders and can be used if all subjects are evaluated in a study by several programmers or if only a portion of subjects are evaluated by several programmers and the rest by one encoder. CICs are suitable for completely cross-design or if a new set of encoders is randomly selected for each participant. Unlike Cohens (1960) kappa, who quantifies the IRR on the basis of an all-or-nothing agreement, CICs take into account the size of the disagreement in calculating irr estimates, with larger disagreements leading to smaller ONES. The IRR was assessed with a mixed, coherent, medium CCI (McGraw & Wong, 1996) to assess the degree of consistency of programmers in their assessments of empathy beyond subjects. The resulting CCI was in the excellent domain, CCI = 0.96 (Cicchetti, 1994), indicating that programmers had a high degree of agreement and empathy was similarly assessed by programmers. The high CPI indicates that a minimal amount of measurement errors has been introduced by independent coders and that, therefore, the statistical relevance for subsequent analyses is not significantly reduced. Empathy ratings were therefore considered appropriate for use in hypothesis testing in this study. Different variants of CCI should be selected depending on the nature of the study and the nature of the agreement the researcher wishes to collect. Four main factors determine the suitable ICC variant based on its own design (McGraw & Wong, 1996; Shrout &Fleiss, 1979) and here briefly censored. Possible values for Kappa statistics range from ?1 to 1, with 1 being synonymous with perfect match, 0 with totally random match, and ?1 with “perfect” disagreement. Landis and Koch (1977) provide guidance for the interpretation of Kappa values, if values from 0.0 to 0.2 correspond slightly, from 0.21 to 0.40 a fair match, from 0.41 to 0.60 a moderate match, from 0.61 to 0.80 an essential match and from 0.81 to 1.0, a near-perfect or perfect match.

However, the use of these qualitative cutoffs is discussed and Krippendorff (1980) gives a more conservative interpretation which suggests that conclusions for variables with values below 0.67 should be drawn provisionally for values between 0.67 and 0.80 and that definitive conclusions should be drawn for values above 0.80. . . . .