Agreement Between Two Groups


Agreement or support of a group, idea, plan, etc. general agreement that something is true, reasonable or cannot be changed, a trade agreement in which people trust each other without a written contract Readers are referred to the following documents that contain measures of the agreement: Schouten, H.J.A. (1982). Measure of the interobserver agreement in pairs when all subjects are evaluated by the same observers. Statistica Neerlandica, 36, 45-61. Two methods are available to assess the consistency between continuously measuring a variable on observers, instruments, dates, etc. One of them, the intraclass coefficient correlation coefficient (CCI), provides a single measure of the magnitude of the match and the other, the Bland-Altman diagram, also provides a quantitative estimate of the narrowness of the values of two measures. an implicit agreement between citizens and the government on the rights and duties of each group conferring legitimacy on a government. It is important to note that in each of the three situations in Table 1, the pass percentages are the same for both reviewers, and if the two reviewers are compared to a 2-× 2 test for mated data (McNemar`s test), there would be no difference between their performance; On the other hand, the agreement between the observers is very different in these three situations. The basic idea that must be understood here is that “agreement” quantifies the agreement between the two examiners for each of the “couples” of the scores, not the similarity of the total pass percentage between the examiners. Cohens – can also be used if the same counsellor evaluates the same patients at two times (for example. B to 2 weeks apart) or, in the example above, re-evaluated the same response sheets after 2 weeks. Its limitations are: (i) it does not take into account the magnitude of the differences, so it is unsuitable for ordinal data, (ii) it cannot be used if there are more than two advisors, and (iii) it does not distinguish between agreement for positive and negative results – which can be important in clinical situations (for example.

B misdiagnosing a disease or falsely excluding them can have different consequences). Think of two ophthalmologists who measure the pressure of the ophthalmometer with a tonometer. Each patient therefore has two measures – one of each observer. CCI provides an estimate of the overall agreement between these values. It is akin to a “variance analysis” in that it considers the differences in intermediate pairs expressed as a percentage of the overall variance of the observations (i.e. the overall variability in the “2n” observations, which would be the sum of the differences between pairs and sub-pairs). CCI can take a value of 0 to 1, 0 not agreeing and 1 indicating a perfect match. Qureshi et al.

compared the degree of prostatic adenocarcinoma assessed by seven pathologists using a standard system (Gleason score). [3] The agreement between each pathologist and the initial relationship and between the pairs of pathologists was determined with Cohen`s Kappa.

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