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Kendall's W (also known as Kendall's coefficient of concordance) is a non-parametric statistic for rank correlation. It is a normalization of the statistic of the Friedman test, and can be used for assessing agreement among raters and in particular inter-rater reliability. Kendall's W ranges from 0 (no agreement) to 1 (complete agreement).
The Kendall rank coefficient is often used as a test statistic in a statistical hypothesis test to establish whether two variables may be regarded as statistically dependent. This test is non-parametric , as it does not rely on any assumptions on the distributions of X or Y or the distribution of ( X , Y ).
Kendall's W is a normalization of the Friedman statistic between and . The Wilcoxon signed-rank test is a nonparametric test of nonindependent data from only two groups. The Skillings–Mack test is a general Friedman-type statistic that can be used in almost any block design with an arbitrary missing-data structure.
Kendall's τ; Goodman and Kruskal's γ; Somers' D; An increasing rank correlation coefficient implies increasing agreement between rankings. The coefficient is inside the interval [−1, 1] and assumes the value: 1 if the agreement between the two rankings is perfect; the two rankings are the same. 0 if the rankings are completely independent.
The concordance correlation coefficient is nearly identical to some of the measures called intra-class correlations.Comparisons of the concordance correlation coefficient with an "ordinary" intraclass correlation on different data sets found only small differences between the two correlations, in one case on the third decimal. [2]
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Kendall's tau: measures statistical dependence between two variables; Kendall's W: a measure between 0 and 1 of inter-rater agreement. Kolmogorov–Smirnov test: tests whether a sample is drawn from a given distribution, or whether two samples are drawn from the same distribution.
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