Quantifying the agreement by other means inevitably implies a model on how ratings are carried out and why advisors agree or disagree. This model is either explicit, as for latent structural models, or implicitly, as for the Kappa coefficient. In this context, two fundamental principles are obvious: there are a number of statistics that can be used to determine reliability between advisors. 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. Pearson`s „R-Displaystyle,“ Kendall format or Spearman`s „Displaystyle“ can measure the pair correlation between advisors using an orderly scale. Pearson believes that the scale of evaluation is continuous; Kendall and Spearman`s statistics only assume it`s ordinal. If more than two clicks are observed, an average match level for the group can be calculated as the average value of the R-Displaystyle r values, or „Displaystyle“ of any pair of debtors. μ j and σ 2 j are the average and variance of the Jth.
Note that C b depends in part on „bias“ if the interest is to estimate the difference between the means of the two tests, i.e. μ 1 x μ 2. C b is also called the „bia correction factor.“ 9 The CCC can therefore be designed as the product of a consistency measure (i.e. the Pearson correlation coefficient) and a distortion measure. In other words, the CCC quantifies not only how the observations fall on the regression line (by B), but also how close this regression line is to the 45-degree line of perfect concordance (above C b). 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. Consider a situation in which we wish to assess the consistency between hemoglobin measurements (g/dL) with a hemoglobinometer on the hospital bed and the formal photometric laboratory technique in ten people [Table 3]. The Bland-Altman diagram for these data shows the difference between the two methods for each person [Figure 1]. The average difference between values is 1.07 g/dL (with a standard deviation of 0.36 g/dL) and 95% agreements are 0.35 to 1.79. This implies that the hemoglobin level of a given person, as measured by photometry, could vary from 0.35 g/dL greater than 1.79 g/dL (this is the case for 95% of people; for 5% of people, differences could be outside these limits).
This of course means that the two techniques cannot be used as a substitute for each other. It is important that there is no single test for what constitutes acceptable limits of the agreement; it is a clinical decision and depends on the measured variable. Dispersal diagram with correlation between hemoglobin measurements from two data methods in Table 3 and Figure 1. The dotted line is a trend line (the line of the smallest squares) by the observed values, and the correlation coefficient is 0.98. However, the individual points are far from the line of the perfect chord (solid black line) Note that cohenkappa measures agree only between two advisors.