The reliability of the interrater is the level of correspondence between councillors or judges. If everyone agrees, IRR is 1 (or 100%) and if not everyone agrees, IRR is 0 (0%). There are several methods of calculating IRR, from the simple (z.B. percent) to the most complex (z.B. Cohens Kappa). What you choose depends largely on the type of data you have and the number of advisors in your model. In statistics, reliability between advisors (also cited under different similar names, such as the inter-rater agreement. B, inter-rated matching, reliability between observers, etc.) is the degree of agreement between the advisors. This is an assessment of the amount of homogeneity or consensus given in the evaluations of different judges. Higher levels of CCI suggest better irregage, an ICC estimate of 1 indicating perfect matching, and random matching of 0. Negative CCI estimates indicate systematic discrepancies and some ICCs may be less than $1 for three or more codes. Cicchetti (1994) proposes cutoffs often cited for qualitative ratings of agreements based on ICC values, ERRORS are bad for ICC values below 40, fair for values between .40 and .59, good for values between 0.60 and 0.74 and excellent for values between 0.75 and 1.0. SpSS and R require that the data be structured for each variable of interest with separate variables for each code, as shown in Table 3 for the depression variable.
If additional variables were evaluated by each coder, each variable would have additional columns for each coder (z.B Rater1_Anxiety, Rater2_Anxiety, etc.) and kappa would have to be calculated separately for each variable. Datasets formatted with reviews of different coders listed in a column can be reformatted using the VARSTOCASES command in SPSS (see Lacroix-Giguére tutorial, 2006) or the “Reforming in R” function. Subsequent extensions of the approach included versions that could deal with “under-credits” and ordinal scales.  These extensions converge with the intra-class correlation family (ICC), which allows us to estimate reliability for each level of measurement, from the notion (kappa) to the ordinal (or ICC) at the interval (ICC or ordinal kappa) and the ratio (ICC). There are also variations that may consider the agreement by the evaluators on a number of points (for example.B. two people agree on the rates of depression for all points of the same semi-structured interview for a case?) as well as cases of raters x (for example. B how do two or more evaluators agree on whether 30 cases have a diagnosis of depression, yes/no a nominal variable). Step 3: For each pair, put a “1” for the chord and “0” for the chord. For example, participant 4, Judge 1/Judge 2 disagrees (0), Judge 1/Judge 3 disagrees (0) and Judge 2 /Judge 3 agreed (1). Intraclass correlation analysis (CCI) is one of the most commonly used statistics to assess ERREURS for ordination, interval and reporting variables. CCI is suitable for studies involving two or more coders and can be used if all subjects are evaluated by multiple coders in one study or if a single subset of subjects is evaluated by multiple coders and the rest is evaluated by a coder. ICCs are suitable for completely cross-concepts or when a new group of coders is randomly selected for each participant.
Unlike Cohens Kappa (1960), which quantifies IRRs on an all-or-nothing basis, ICCs take into account the magnitude of discrepancies in the calculation of IRR estimates, with larger differences of opinion resulting in smaller ICCs than smaller differences of opinion.