The basic measure for Inter-Rater`s reliability is a percentage agreement between advisors. If advisors tend to accept, the differences between the evaluators` 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. In this competition, the judges agreed on 3 out of 5 points. The approval percentage is 3/5 – 60%. A serious error in this type of reliability between boards is that the random agreement does not take into account and overestimates the level of agreement. This is the main reason why the percentage of consent should not be used for scientific work (i.e. doctoral theses or scientific publications). Second, the researcher must indicate whether a good error should be characterized by absolute agreement or absolute consistency in the ratings. While it is important for advisors to provide partitions similar to absolute value, absolute match should be used, while if the spleens provide similar points in the ranking, consistency should be used. For example, consider a coder that typically provides low notes (z.B 1-5 on an 8-point Likert scale) and another coder that typically provides high grades (for example.

B 4-8 on the same scale). Absolute approval of these ratings would be expected to be low, given that there were large differences in actual rating values; However, it is possible that the consistency of these ratings would be high if the rankings of these ratings were similar between the two coders. Kappa is a way to measure agreements or reliability and to correct the frequency with which ratings might consent to chance. Cohens Kappa,[5] who works for two councillors, and Fleiss` Kappa,[6] an adaptation that works for any fixed number of councillors, improve the common likelihood that they would take into account the amount of agreement that could be expected by chance. The original versions suffered from the same problem as the probability of joints, as they treat the data as nominal and assume that the evaluations have no natural nature; if the data does have a rank (ordinal measurement value), this information is not fully taken into account in the measurements. As the observed agreement is greater than the entry agreement, we get a positive kappa. Reliability assessment between rating agencies (ACCORD, also known as the Inter-Rater Agreement) is often necessary for research projects that collect data through evaluations of trained or untrained coders. However, many studies use false statistical analyses to calculate ERREURS, misinterpret the results of IRR analyses, or disrepresent the implications that IRR estimates have on statistical performance for subsequent analyses. Krippendorffs Alpha[16][17] is a versatile statistic that evaluates the agreement between observers who categorize, evaluate or measure a certain number of objects against the values of a variable. It generalizes several specialized agreement coefficients by accepting any number of observers applicable to nominal, ordinal, interval and proportional levels of measurement, capable of processing missing and corrected data for small sample sizes. Use the boarding school agreement to evaluate the agreement between two classifications (nominal or ordinal scales).

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.