Observer Agreement Score

The correspondence between the measurements refers to the degree of correspondence between two (or more) measures. Statistical methods used to verify compliance are used to assess the variability of inter-variability or to decide whether one variable measurement technique can replace another. In this article, we examine statistical measures of compliance for different types of data and discuss the differences between them and those for assessing correlation. As mentioned above, correlation is not synonymous with agreement. The correlation refers to the existence of a relationship between two different variables, while the agreement considers the agreement between two measures of a variable. Two sets of observations, strongly correlated, may have a poor agreement; However, if the two sets of values agree, they will certainly be strongly correlated. For example, in the hemoglobin example, the correlation coefficient between the values of the two methods is high, although the agreement is poor [Figure 2]; (r – 0.98). The other way of looking at it is that, although the different points are not close enough to the dotted line (least square line; [2], indicating a good correlation), these are quite far from the running black line that represents the perfect chord line (Figure 2: the black line running). If there is a good agreement, the dots should fall on or near this line (of the current black line).

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. ( observed agreement [Po] – expected agreement [Pe]) / (agreement 1 expected [Pe]). There are a number of statistics that can be used to determine the reliability of interramas. Different statistics are adapted to different types of measurement. Some options are the common probability of an agreement, Cohens Kappa, Scott`s pi and the Fleiss`Kappa associated with it, inter-rate correlation, correlation coefficient, intra-class correlation and Krippendorff alpha. For ordination data, where there are more than two categories, it is useful to know whether the evaluations of the various counsellors end slightly or vary by a significant amount.