Spearman's rank correlation coefficient

In mathematics and statistics, **Spearman's rank correlation coefficient** is a measure of correlation. It is named after its maker, Charles Spearman. It is written in short as the Greek letter _rho_ () or sometimes as . It is a number that shows how closely two sets of data are linked. It only can be used for data which can be put in order, such as highest to lowest.

The general formula for  is .

For example, if you have data for how expensive different computers are, and data for how fast the computers are, you could see if they are linked, and how closely they are linked, using .

### Step one

To work out  you first have to _rank_ each piece of data. We are going to use the example from the intro of computers and their speed.

So, the computer with the lowest price would be rank **1**. The one higher than that would have **2**. Then, it goes up until it is all ranked. You have to do this to both sets of data.

|align="center" style="background:#f0f0f0;"|**PC**|align="center" style="background:#f0f0f0;"|**Price ($)**|align="center" style="background:#f0f0f0;"|****|align="center" style="background:#f0f0f0;"|**Speed (GHz)**|align="center" style="background:#f0f0f0;"|****|
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|A|200|1|1.80|2||||||
|B|275|2|1.60|1||||||
|C|300|3|2.20|4||||||
|D|350|4|2.10|3||||||
|E|600|5|4.00|5||||||

### Step two

Next, we have to find the _difference_ between the two ranks. Then, you multiply the difference by itself, which is called squaring. The difference is called , and the number you get when you square  is called .

|align="center" style="background:#f0f0f0;"|****|align="center" style="background:#f0f0f0;"|****|align="center" style="background:#f0f0f0;"|****|align="center" style="background:#f0f0f0;"|****|
| --- | --- | --- | --- | --- | --- | --- | --- |
|1|2|-1|1|||||
|2|1|1|1|||||
|3|4|-1|1|||||
|4|3|1|1|||||
|5|5|0|0|||||

### Step three

Count how much data we have. This data has ranks 1 to 5, so we have 5 pieces of data. This number is called .

### Step four

Finally, use everything we have worked out so far in this formula: .

means that we take the total of all the numbers that were in the column . This is because  means total.

So,  is  which is 4. The formula says multiply it by 6, which is 24.

is  which is 120.

So, to find out , we simply do .

Therefore, Spearman's rank correlation coefficient is 0.8 for this set of data.

## What the numbers mean

always gives an answer between −1 and 1. The numbers between are like a scale, where −1 is a very strong link, 0 is no link, and 1 is also a very strong link. The difference between 1 and −1 is that 1 is a positive correlation, and −1 is a negative correlation.  A graph of data with a  value of −1 would look like the graph shown except the line and points would be going from top left to bottom right.

For example, for the data that we did above,  was 0.8. So this means that there is a positive correlation. Because it is close to 1, it means that the link is strong between the two sets of data. So, we can say that those two sets of data are _linked_, and go _up_ together. If it was −0.8, we could say it was _linked_ and as one goes up, the other goes down.

## If two numbers are the same

Sometimes, when ranking data, there are two or more numbers that are the same. When this happens in , we take the mean or average of the ranks that are the same. These are called _tied_ ranks. To do this, we rank the tied numbers as if they were not tied. Then, we add up all the ranks that they would have, and divide it by how many there are.  For example, say we were ranking how well different people did in a spelling test.

|align="center" style="background:#f0f0f0;"|**Test score**|align="center" style="background:#f0f0f0;"|**Rank**|align="center" style="background:#f0f0f0;"|**Rank (with tied)**|
| --- | --- | --- | --- | --- | --- |
|4|1|1||||
|6|2|||||
|6|3|||||
|6|4|||||
|8|5|||||
|8|6|||||

These numbers are used in exactly the same way as normal ranks.

## Related pages

- Correlation