If you have multiple advisors, calculate the percentage agreement as follows: As you can probably say, calculating percentage agreements for more than a handful of advisors can quickly become difficult. For example, if you had 6 judges, you would have 16 pairs of pairs to calculate for each participant (use our combination calculator to find out how many pairs you would get for multiple judges). Kappa is an index that takes into account the agreement observed with regard to a basic agreement. However, investigators must carefully consider whether Kappa`s core agreement is relevant to the research issue. Kappa`s baseline is often called random tuning, which is only partially correct. The basic agreement of Kappa is the agreement that could be expected because of the accidental allocation, given the quantities declared in quantity in the limit amounts of the square emergency table. Kappa – 0 if the observed attribution appears to be random, regardless of the quantitative opinion limited by the limit amounts. However, for many applications, investigators should be more interested in quantitative opinion in marginal amounts than in attribution opinion, as described in the supplementary information on the diagonal of the square emergency table. Kappa`s base is therefore more entertaining than illuminating for many applications. Let`s take the following example: If advisors tend to accept, the differences between the spleen`s observations will be close to zero. If one advisor is generally higher or lower than the other by a consistent amount, the distortion differs from zero.

If advisors tend to disagree, but without a consistent model of one assessment above each other, the average will be close to zero. Confidence limits (generally 95%) It is possible to calculate for bias and for each of the limits of the agreement. Z 1 – α / 2 – 1,965 “Z_ -1-alpha /2 – 1.965” is the normal standard sub-cent, if α – 5% “display style” “alpha – 5%” and S E – po ( 1 x po) N ( 1 {2} p_ p_ p_ SE_ The common probability of a deal is the simplest and least robust measure.