Spearman's rank correlation coefficient

In statistics, Spearman's rank correlation coefficient or Spearman's ρ is a number ranging from -1 to 1 that indicates how strongly two sets of ranks are correlated. It could be used in a situation where one only has ranked data, such as a tally of gold, silver, and bronze medals. If a statistician wanted to know whether people who are high ranking in sprinting are also high ranking in long-distance running, they would use a Spearman rank correlation coefficient.
The coefficient is named after Charles Spearman and often denoted by the Greek letter [rho (letter)|] or as. It is a nonparametric measure of rank correlation. It assesses how well the relationship between two variables can be described using a monotonic function.
The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.
Intuitively, the Spearman correlation between two variables will be high when observations have a similar rank between the two variables, and low when observations have a dissimilar rank between the two variables.
Spearman's coefficient is appropriate for both continuous and discrete ordinal variables. Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient.

Applications

The coefficient can be used to determine how well data fits a model, like when determining the similarity of text documents.

Definition and calculation

The Spearman correlation coefficient is defined as the Pearson correlation coefficient between the rank variables.
For a sample of size the pairs of raw scores are converted to ranks and is computed as
where
Only when all ranks are distinct integers, it can be computed using the popular formula
where
Consider a bivariate sample with corresponding rank pairs
Then the Spearman correlation coefficient of is
where, as usual,
and
We shall show that can be expressed purely in terms of provided we assume that there be no ties within each sample.
Under this assumption, we have that can be viewed as random variables distributed like a uniformly distributed discrete random variable on Hence
and
where
and thus
Observe now that
Putting this all together thus yields
Identical values are usually each assigned fractional ranks equal to the average of their positions in the ascending order of the values, which is equivalent to averaging over all possible permutations.
If ties are present in the data set, the simplified formula above yields incorrect results: Only if in both variables all ranks are distinct, then .
The first equation — normalizing by the standard deviation — may be used even when ranks are normalized to because it is insensitive both to translation and linear scaling.
The simplified method should also not be used in cases where the data set is truncated; that is, when the Spearman's correlation coefficient is desired for the top X records, the user should use the Pearson correlation coefficient formula given above.

Related quantities

There are several other numerical measures that quantify the extent of statistical dependence between pairs of observations. The most common of these is the Pearson product-moment correlation coefficient, which is a similar correlation method to Spearman's rank, that measures the "linear" relationships between the raw numbers rather than between their ranks.
An alternative name for the Spearman rank correlation is the "grade correlation"; in this, the "rank" of an observation is replaced by the "grade". In continuous distributions, the grade of an observation is, by convention, always one half less than the rank, and hence the grade and rank correlations are the same in this case. More generally, the "grade" of an observation is proportional to an estimate of the fraction of a population less than a given value, with the half-observation adjustment at observed values. Thus this corresponds to one possible treatment of tied ranks. While unusual, the term "grade correlation" is still in use.

Interpretation

The sign of the Spearman correlation indicates the direction of association between X and Y. If Y tends to increase when X increases, the Spearman correlation coefficient is positive. If Y tends to decrease when X increases, the Spearman correlation coefficient is negative. A Spearman correlation of zero indicates that there is no tendency for Y to either increase or decrease when X increases. The Spearman correlation increases in magnitude as X and Y become closer to being perfectly monotonic functions of each other. When X and Y are perfectly monotonically related, the Spearman correlation coefficient becomes 1. A perfectly monotonic increasing relationship implies that for any two pairs of data values and, that and always have the same sign. A perfectly monotonic decreasing relationship implies that these differences always have opposite signs.
The Spearman correlation coefficient is often described as being "nonparametric". This can have two meanings. First, a perfect Spearman correlation results when X and Y are related by any monotonic function. Contrast this with the Pearson correlation, which only gives a perfect value when X and Y are related by a linear function. The other sense in which the Spearman correlation is nonparametric is that its exact sampling distribution can be obtained without requiring knowledge of the joint probability distribution of X and Y.
The strength of correlations also are an important consideration for interpretation of correlation analyses but descriptions of the strength of correlation are not universally accepted. A small correlation coefficient calculated for a very large sample may be statistically significant without providing a quantitative relation between variables. Similarly, a large correlation coefficient calculated with a small sample size may indicate that a quantitative equation between variables may be possible even though the correlation coefficient is not statistically significant. Akoglu noted the need for consistent descriptions of strength and provides different correlation-strength descriptors for psychology, politics, and medicine that have different descriptors and thresholds. Because there is a general lack of consensus on correlation strength, Granato defined operational definitions for the absolute values of correlation coefficients as weak, moderate, semi-strong, and strong for use in hydrologic analyses of stormwater treatment statistics. Similarly, Schober and others note that "...cutoff points are arbitrary and inconsistent and should be used judiciously..." Despite their warning Schober and others provide a table indicating that correlations less than or equal to 0.1 are negligible; correlations greater than 0.1 and less than or equal to 0.39 are weak; correlations greater than 0.39 and less than or equal to 0.69 are moderate; correlations greater than 0.69 and less than or equal to 0.89 are strong; and correlations greater than 0.89 are very strong. Descriptions of strength indicate the potential to develop quantitative relations among variables but these descriptors, as with the correlation coefficients themselves, do not indicate causation.

Example

In this example, the arbitrary raw data in the table below is used to calculate the correlation between the IQ of a person with the number of hours spent in front of TV per week .

IQ,	Hours of TV per week,
106	7
100	27
86	2
101	50
99	28
103	29
97	20
113	12
112	6
110	17

Firstly, evaluate. To do so use the following steps, reflected in the table below.

Sort the data by the first column. Create a new column and assign it the ranked values 1, 2, 3,..., n.
Next, sort the augmented data by the second column. Create a fourth column and similarly assign it the ranked values 1, 2, 3,..., n.
Create a fifth column to hold the differences between the two rank columns.
Create one final column to hold the value of column squared.

IQ,	Hours of TV per week,	rank	rank
86	2	1	1	0	0
97	20	2	6	−4	16
99	28	3	8	−5	25
100	27	4	7	−3	9
101	50	5	10	−5	25
103	29	6	9	−3	9
106	7	7	3	4	16
110	17	8	5	3	9
112	6	9	2	7	49
113	12	10	4	6	36

With found, add them to find. The value of n is 10. These values can now be substituted back into the equation
to give
which evaluates to with a p-value = 0.627188.
That the value is close to zero shows that the correlation between IQ and hours spent watching TV is very low, although the negative value suggests that the longer the time spent watching television the lower the IQ. In the case of ties in the original values, this formula should not be used; instead, the Pearson correlation coefficient should be calculated on the ranks.

Confidence intervals

Confidence intervals for Spearman's ρ can be easily obtained using the Jackknife Euclidean likelihood approach in de Carvalho and Marques. The confidence interval with level is based on a Wilks' theorem given in the latter paper, and is given by
where is the quantile of a chi-square distribution with one degree of freedom, and the are jackknife pseudo-values. This approach is implemented in the R package .

Determining significance

One approach to test whether an observed value of ρ is significantly different from zero is to calculate the probability that it would be greater than or equal to the observed r, given the null hypothesis, by using a permutation test. An advantage of this approach is that it automatically takes into account the number of tied data values in the sample and the way they are treated in computing the rank correlation.
Another approach parallels the use of the Fisher transformation in the case of the Pearson product-moment correlation coefficient. That is, confidence intervals and hypothesis tests relating to the population value ρ can be carried out using the Fisher transformation:
If F is the Fisher transformation of r, the sample Spearman rank correlation coefficient, and n is the sample size, then
is a z-score for r, which approximately follows a standard normal distribution under the null hypothesis of statistical independence.
One can also test for significance using
which is distributed approximately as Student's t-distribution with degrees of freedom under the null hypothesis. A justification for this result relies on a permutation argument.
A generalization of the Spearman coefficient is useful in the situation where there are three or more conditions, a number of subjects are all observed in each of them, and it is predicted that the observations will have a particular order. For example, a number of subjects might each be given three trials at the same task, and it is predicted that performance will improve from trial to trial. A test of the significance of the trend between conditions in this situation was developed by E. B. Page and is usually referred to as Page's trend test for ordered alternatives.

Correspondence analysis based on Spearman's ''ρ''

Classic correspondence analysis is a statistical method that gives a score to every value of two nominal variables. In this way the Pearson correlation coefficient between them is maximized.
There exists an equivalent of this method, called grade correspondence analysis, which maximizes Spearman's ρ or Kendall's τ.

Approximating Spearman's ''ρ'' from a stream

There are two existing approaches to approximating the Spearman's rank correlation coefficient from streaming data. The first approach
involves coarsening the joint distribution of. For continuous values: cutpoints are selected for and respectively, discretizing
these random variables. Default cutpoints are added at and. A count matrix of size, denoted, is then constructed where stores the number of observations that
fall into the two-dimensional cell indexed by. For streaming data, when a new observation arrives, the appropriate element is incremented. The Spearman's rank
correlation can then be computed, based on the count matrix, using linear algebra operations. Note that for discrete random
variables, no discretization procedure is necessary. This method is applicable to stationary streaming data as well as large data sets. For non-stationary streaming data, where the Spearman's rank correlation coefficient may change over time, the same procedure can be applied, but to a moving window of observations. When using a moving window, memory requirements grow linearly with chosen window size.
The second approach to approximating the Spearman's rank correlation coefficient from streaming data involves the use of Hermite series based estimators. These estimators, based on Hermite polynomials,
allow sequential estimation of the probability density function and cumulative distribution function in univariate and bivariate cases. Bivariate Hermite series density
estimators and univariate Hermite series based cumulative distribution function estimators are plugged into a large sample version of the
Spearman's rank correlation coefficient estimator, to give a sequential Spearman's correlation estimator. This estimator is phrased in
terms of linear algebra operations for computational efficiency. These algorithms are only applicable to continuous random variable data, but have
certain advantages over the count matrix approach in this setting. The first advantage is improved accuracy when applied to large numbers of observations. The second advantage is that the Spearman's rank correlation coefficient can be
computed on non-stationary streams without relying on a moving window. Instead, the Hermite series based estimator uses an exponential weighting scheme to track time-varying Spearman's rank correlation from streaming data,
which has constant memory requirements with respect to "effective" moving window size. A software implementation of these Hermite series based algorithms exists and is discussed in Software implementations.

Software implementations

R's statistics base-package implements the test in its "stats" package. The package computes confidence intervals. The package computes fast batch estimates of the Spearman correlation along with sequential estimates.
Stata implementation: spearman varlist calculates all pairwise correlation coefficients for all variables in varlist.
MATLAB implementation: = corr where r is the Spearman's rank correlation coefficient, p is the p-value, and x and y are vectors.
Python has many different implementations of the spearman correlation statistic: it can be computed with the function of the scipy.stats module, as well as with the DataFrame.corr method from the library, and the corr function from the statistical package .