Konishi S. Normalizing transformations and variants for intraclass correlations. Ann Inst Stat Math. 37(1):87–94. In statistics, the correlation coefficient measures the agreement between two variables, for example. B for assessing reproducibility or reliability between boards. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing the reliability of advisors. Psychol Bull.
86(2):420. To see this, match the variances of the variables ui (vi) (population) and (population). The Pearson correlation is an estimate of the correlation of the following product time: Vineyard R, Patel YC. Simulated intraclass correlation coefficients and their transformations z. J Stat Comput Simul. 1981; 13(1):13–26. Like the Pearson correlation, the Spearman-Rho in (4) is a sample-based statistic. This spearman rho sample is an estimate of the following population Spearman rho: Olkin I, Pratt JW.
Unbiased estimate of certain correlation coefficients. Ann Math Stat. 29:201-11. doi.org/10.1214/aoms/1177706717. Note that the Spearman s rho sample (4) is called spearman s rho in the literature. Contrary to the Pearson correlation, there is no formal name for the population of Spearman s rho in (7). In general, the absence of a formal name for the population-wide version is not confusing, as it is generally clear what is used in a discussion. Like all statistics, the population version of a statistic is called a parameter in the statistical Lingo. Statistics and parameters are used for different purposes. For example, only the parameter can be used to specify statistical hypotheses such as .B the zero hypothesis, H:--0, to test whether the Spearman-Rho 0 is of the population. The values reported of Spearman rho by studies are always the spearman rho sample. Unlike the Pearson correlation, it also applies to non-linear relationships, which helps address the aforementioned Pearson correlation restriction.
By comparing two methods of measurement, it is interesting not only to estimate both the bias and the limits of the agreement between the two methods (interdeccis agreement), but also to evaluate these characteristics for each method itself. It is quite possible that the agreement between two methods is bad simply because one method has broad convergence limits, while the other is narrow. In this case, the method with narrow limits of compliance would be statistically superior, while practical or other considerations could alter that assessment. In any event, what represents narrow or broad boundaries of the agreement or a large or small bias is a practical assessment. Correspondence and correlation are widely used concepts in the medical literature. Both are used to indicate the strength of the association between variables of interest, but they are conceptually separate and therefore require the use of different statistics. Subsequent extensions of the approach included versions that could deal with "under-credits" and ordinal scales.  These extensions converge with the intra-class correlation family (ICC), which allows us to estimate reliability for each level of measurement, from the notion (kappa) to the ordinal (or ICC) at the interval (ICC or ordinal kappa) and the ratio (ICC). There are also variations that may consider the agreement by the evaluators on a number of points (for example.B. two people agree on the rates of depression for all points of the same semi-structured interview for a case?) as well as cases of raters x (for example.
B how do two or more evaluators agree on whether 30 cases have a diagnosis of depression, yes/no a nominal variable).