Whilst the Kendall rank correlation coefficient is used to determine the association between just two variables measured in (or transformed to) ranks, the Kendall coefficient of concordance (W) is used to determine the association between k such variables. It is most commonly used to assess agreement among raters. The coefficient bears a linear relationship to the average Spearman rank correlation coefficient between all possible pairs of raters.
Ranking of quality of strawberries 
Farms 
Raters 
A  B  C  D  ΣR 
1 2 3 4 5 6 7 8 
8 4 2 3 5 1 6 7 
7 3 1 4 5 2 6 8 
8 2 5 6 7 1 4 3 
8 3 4 2 5 1 6 7 
31 12 12 15 22 5 22 25 
We will take an example of four people (raters) ranking the quality of samples of strawberries grown on eight different farms from best (1) to worst (8).
Procedure
 If there are any tied observations, assign the average of the ranks they would have been assigned had no ties occurred.
 Find the sum of ranks (R_{j}) for each item being ranked.
 Sum these R_{j} and divide by the number of items being ranked (N)
to give the mean value of the R_{j}.
 Calculate the sum of squares of the deviations of each R_{j}
from the mean using SS = Σ[ R_{j} − ( ΣR_{j}/N)]^{2}
 Compute the Kendall coefficient of concordance (W) using:

Algebraically speaking 
W 
= 
SS 

(n^{3}−n) k^{2}/12 
where
 SS is the sum of squares of the deviations of each R_{j} from the mean
 n is the number of items being ranked,
 k is the number of raters.

 For n > 7, the quantity k(N−1)W is distributed as χ^{2} with N − 1 degrees of freedom.