In statistics, a rank correlation is any of several statistics that measure the relationship between rankings of different ordinal variables or different rankings of the same variable, where a "ranking" is the assignment of the labels "first", "second", "third", etc. to different observations of a particular variable. A rank correlation coefficient measures the degree of similarity between two rankings, and can be used to assess the significance of the relation between them. For example, two common nonparametric methods of significance that use rank correlation are the Mann–Whitney U test and the Wilcoxon signedrank test.
Contents

Context 1

Correlation coefficients 2

General correlation coefficient 3

Kendall's \tau as a particular case 3.1

Spearman's \rho as a particular case 3.2

Rankbiserial correlation 4

Kerby simple difference formula 4.1

Example and interpretation 4.2

References 5
Context
If, for example, one variable is the identity of a college basketball program and another variable is the identity of a college football program, one could test for a relationship between the poll rankings of the two types of program: do colleges with a higherranked basketball program tend to have a higherranked football program? A rank correlation coefficient can measure that relationship, and the measure of significance of the rank correlation coefficient can show whether the measured relationship is small enough to likely be a coincidence.
If there is only one variable, the identity of a college football program, but it is subject to two different poll rankings (say, one by coaches and one by sportswriters), then the similarity of the two different polls' rankings can be measured with a rank correlation coefficient.
Correlation coefficients
Some of the more popular rank correlation statistics include

Spearman's ρ

Kendall's τ

Goodman and Kruskal's γ
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.

−1 if the disagreement between the two rankings is perfect; one ranking is the reverse of the other.
Following Diaconis (1988), a ranking can be seen as a permutation of a set of objects. Thus we can look at observed rankings as data obtained when the sample space is (identified with) a symmetric group. We can then introduce a metric, making the symmetric group into a metric space. Different metrics will correspond to different rank correlations.
General correlation coefficient
Kendall (1944) showed that his \tau (tau) and Spearman's \rho (rho) are particular cases of a general correlation coefficient.
Suppose we have a set of n objects, which are being considered in relation to two properties, represented by x and y, forming the sets of values \{x_i\}_{i\le n} and \{y_i\}_{i\le n}. To any pair of individuals, say the ith and the jth we assign a xscore, denoted by a_{ij}, and a yscore, denoted by b_{ij}. The only requirement made to this functions is antisymmetry, so a_{ij}=a_{ji} and b_{ij}=b_{ji}. Then the generalised correlation coefficient \Gamma is defined by

\Gamma = \frac{\sum_{i,j = 1}^n a_{ij}b_{ij}}{\sqrt{\sum_{i,j = 1}^n a_{ij}^2 \sum_{i,j = 1}^n b_{ij}^2}}
Kendall's \tau as a particular case
If r_i is the rank of the imember according to the xquality, we can define

a_{ij} = \sgn(r_jr_i)
and similarly for b. The sum \sum a_{ij}b_{ij} is twice the amount of concordant pairs minus the discordant pairs (see Kendall tau rank correlation coefficient). The sum \sum a_{ij}^2 is just the number of terms a_{ij}, equal to n(n1), and so for \sum b_{ij}^2. It follows that \Gamma is equal to the Kendall's \tau coefficient.
Spearman's \rho as a particular case
If r_i, s_i are the ranks of the imember according to the x and the yquality respectively, we can simply define

a_{ij} = r_jr_i

b_{ij} = s_js_i
The sums \sum a_{ij}^2 and \sum b_{ij}^2 are equal, since both r_i and s_i range from 1 to n. Then we have:

\Gamma = \frac{\sum (r_jr_i)(s_js_i)}{\sum(r_jr_i)^2}
now

\sum_{i,j = 1}^n (r_jr_i)(s_js_i)= \sum_{i=1}^n \sum_{j=1}^n r_is_i + \sum_{i=1}^n \sum_{j=1}^n r_js_j  \sum_{i=1}^n \sum_{j=1}^n (r_is_j+r_js_i)

=2n\sum_{i=1}^n r_is_i  2 \sum_{i=1}^n r_i \sum_{j=1}^n s_j

=2n\sum_{i=1}^n r_is_i  \frac12 n^2(n+1)^2
since \sum r_i and \sum s_j are both equal to the sum of the first n natural numbers, namely \frac12n(n+1).
We also have

S = \sum_{i=1}^n (r_is_i)^2 = 2 \sum r_i^2  2\sum r_is_i
and hence

\sum(r_jr_i)(s_js_i) = 2n\sum r_i^2  \frac12n^2(n+1)^2  nS
\sum r_i^2 being the sum of squares of the first n naturals equals \frac16n(n+1)(2n+1). Thus, the last equation reduces to

\sum(r_jr_i)(s_js_i) = \frac16n^2(n^21)  nS
Further

\sum(r_jr_i)^2 = 2n\sum r_i^22\sum r_ir_j

= 2n\sum r_i^22(\sum r_i)^2 = \frac16n^2(n^21)
and thus, substituting into the original formula these results we get

\Gamma_R = 1\frac{6\sum d_i^2}{n^3n}
where d_i = x_i  y_i, is the difference between ranks.
which is exactly the Spearman's rank correlation coefficient \rho.
Rankbiserial correlation
Gene Glass (1965) noted that the rankbiserial can be derived from Spearman's \rho. "One can derive a coefficient defined on X, the dichotomous variable, and Y, the ranking variable, which estimates Spearman's rho between X and Y in the same way that biserial r estimates Pearson's r between two normal variables” (p. 91). The rankbiserial correlation had been introduced nine years before by Edward Cureton (1956) as a measure of rank correlation when the ranks are in two groups.
Kerby simple difference formula
Dave Kerby (2014) recommended the rankbiserial as the measure to introduce students to rank correlation, because the general logic can be explained at an introductory level. The rankbiserial is the correlation used with the Mann–Whitney U test, a method commonly covered in introductory college courses on statistics. The data for this test consists of two groups; and for each member of the groups, the outcome is ranked for the study as a whole.
Kerby showed that this rank correlation can be expressed in terms of two concepts: the percent of data that support a stated hypothesis, and the percent of data that do not support it. The Kerby simple difference formula states that the rank correlation can be expressed as the difference between the proportion of favorable evidence (f) minus the proportion of unfavorable evidence (u).

r = f  u
Example and interpretation
To illustrate the computation, suppose a coach trains longdistance runners for one month using two methods. Group A has 5 runners, and Group B has 4 runners. The stated hypothesis is that method A produces faster runners. The race to assess the results finds that the runners from Group A do indeed run faster, with the following ranks: 1, 2, 3, 4, and 6. The slower runners from Group B thus have ranks of 5, 7, 8, and 9.
The analysis is conducted on pairs, defined as a member of one group compared to a member of the other group. For example, the fastest runner in the study is a member of four pairs: (1,5), (1,7), (1,8), and (1,9). All four of these pairs support the hypothesis, because in each pair the runner from Group A is faster than the runner from Group B. There are a total of 20 pairs, and 19 pairs support the hypothesis. The only pair that does not support the hypothesis are the two runners with ranks 5 and 6, because in this pair, the runner from Group B had the faster time. By the Kerby simple difference formula, 95% of the data support the hypothesis (19 of 20 pairs), and 5% do not support (1 of 20 pairs), so the rank correlation is r = .95  .05 = .90.
The maximum value for the correlation is r = 1, which means that 100% of the pairs favor the hypothesis. A correlation of r = 0 indicates that half the pairs favor the hypothesis and half do not; in other words, the sample groups do not differ in ranks, so there is no evidence that they come from two different populations. An effect size of r = 0 can be said to describe no relationship between group membership and the members' ranks.
References

Cureton, E. E. (1956). Rankbiserial correlation. Psychometrika 21, 287290. doi:10.1007/BF02289138

Everitt, B. S. (2002), The Cambridge Dictionary of Statistics, Cambridge: Cambridge University Press,

Diaconis, P. (1988), Group Representations in Probability and Statistics, Lecture NotesMonograph Series, Hayward, CA: Institute of Mathematical Statistics,

Glass, G. V. (1965). A ranking variable analogue of biserial correlation: implications for shortcut item analysis. Journal of Educational Measurement, 2(1), 91–95. DOI: 10.1111/j.17453984.1965.tb00396.x

Kendall, M. G. (1970), Rank Correlation Methods, London: Griffin,

Kerby, D. S. (2014). The simple difference formula: An approach to teaching nonparametric correlation. Innovative Teaching, volume 3, article 1. doi:10.2466/11.CP.3.1. link to pdf
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