The concept of “advisor agreement” is quite simple and, for many years, the reliability of Interraters has been measured as a percentage of match among data collectors. To obtain the measurement agreement, the statistician established a matrix in which the columns represent the different advisors and the lines of the variables for which the raters had collected data (Table 1). The cells in the matrix contained the values captured by the data collectors for each variable. An example of this procedure can be made in Table 1. In this example, there are two advisors (Mark and Susan). They each record their values for variables 1 to 10. To obtain a percentage of approval, the researcher subtracted Susan`s scores from Marks Scores and counted the resulting number of zeroes. Dividing the number of zeros by the number of variables provides a measure of the agreement between advisors. In Table 1, the agreement is 80%. This means that 20% of the data collected in the study is incorrect, because only one of the advisors can be correct if there is disagreement. This statistic is directly interpreted as a percentage of correct data. The value, 1.00 – percent approval can be understood as a percentage of data that is false. That is, if the approval percentage is 82, 1.00-0.82 – 0.18 and 18% is the amount of data that the search data is wrong.

Cedric, I now have support for s.e. and confidence intervals for Cohen kappa and weighted kappa added to the latest version of the software`s actual statistics, namely version 3.8. Note that the unweighted Kappa represents the Cohens Standard Kappa, which should only be considered for nominal variables. For more information, see the corresponding chapter. Bahaman, the Real Statistics Website www.real-statistics.com/reliability/cohens-kappa/ describes Cohens Kappa. If instead you are looking for Cohen`s Kappa, which is gridded as an effect size for mediating analysis, please read the following: lib.ugent.be/fulltxt/RUG01/002/214/023/RUG01-002214023_2015_0001_AC.pdf quantpsy.org/pubs/preacher_kelley_2011.pdf etd.library.vanderbilt.edu/available/etd-05182015-161658/unrestricted/Lachowicz_Thesis_20150526.pdf Charles I`m curious to use weighted kappa in the following scenario. I had two advisors a diagnostic checklist with 12 different criteria. The answer to each criterion was either 1 or 0 (current or absent). If a number of criteria were available, an overall criterion 1 (if not, 0) was coded.

Are dichotomous responses considered orderly in this case? Is the weighted kappa the right statistic for reliability? Marius, as I understand it, you don`t care if we agree between the advisors, but only if each advisor doesn`t change his or her assessment between the two periods. In this case, I do not know of any standard measures of the agreement. Charles Cohen`s Kappa coefficient () is a statistic used to measure reliability between rats (and also the reliability of inter-raters) for qualitative (categorical) elements. [1] It is generally accepted that this is a more robust indicator than a simple percentage of the agreement calculation, since the possibility of a random agreement is taken into account. There are controversies around Cohens Kappa because of the difficulty of interpreting the indications of the agreement. Some researchers have suggested that it is easier, conceptually, to assess differences of opinion between objects. [2] For more details, see Restrictions. Sentence 5 shows that we have an almost complete picture of how the seven weighted kappas are arranged only by comparing the values of , and. Double inequality applies to fourth place in the table, while inequality applies to third place in Table 2.