Spearman's Rank Correlation Coefficient (DP IB Applications & Interpretation (AI)): Revision Note

Spearman’s rank

What is Spearman’s rank correlation coefficient?

  • Spearman's rank correlation coefficient is a measure of how well the relationship between two variables can be described using a monotonic function

    • A function is monotonic if it is either always increasing or always decreasing

      • i.e. if the y values are always increasing or always decreasing as the x values increase

    • This can be used as a way to measure correlation in linear models

    • Though Spearman's Rank correlation coefficient can also be used to assess a non-linear relationship

  • Each data is ranked, from biggest to smallest or from smallest to biggest

    • For n data values, they are ranked from 1 to n

    • It doesn't matter whether variables are ranked from biggest to smallest or smallest to biggest, but they must be ranked in the same order for both variables

  • Spearman’s rank of a sample is denoted by r subscript s

    • rs can take any value in the interval negative 1 less or equal than r subscript s less or equal than 1

    • A positive value of rs describes a degree of agreement between the rankings

    • A negative value of rs describes a degree of disagreement between the rankings

    • rs = 0 means the data shows no monotonic behaviour

    • rs = 1 means the rankings are in complete agreement: the data is strictly increasing

      • An increase in one variable always means an increase in the other

    • rs = -1 means the rankings are in complete disagreement: the data is strictly decreasing

      • An increase in one variable always means a decrease in the other

    • The closer to 1 or -1 the stronger the correlation of the rankings

Six scatter plots showing different Spearman's rank correlation values: a linear and a non-linear example of perfect positive (r_s=1), weak positive (r_s=0.3), a linear and a non-linear example of perfect negative (r_s=-1), and strong negative (r_s=-0.7).

How do I calculate Spearman’s rank correlation coefficient (PMCC)?

  • Rank each set of data independently

    • 1 to n for the x-values

    • 1 to n for the y-values

  • If some values are equal then give each the average of the ranks they would occupy

    • For example: if the 3rd, 4th and 5th highest values are equal then give each the ranking of 4

      • fraction numerator 3 plus 4 plus 5 over denominator 3 end fraction equals 4

  • Calculate the PMCC of the rankings using your GDC

    • This value is Spearman's rank correlation coefficient

Worked Example

The table below shows the scores of eight students for a maths test and an English test.

Maths left parenthesis x right parenthesis

7

18

37

52

61

68

75

82

English left parenthesis y right parenthesis

5

3

9

12

17

41

49

97

a) Write down the value of Pearson’s product-moment correlation coefficient, r.

4-2-2-ib-ai-sl-correlation-coefficients-a-we-solution

b) Find the value of Spearman’s rank correlation coefficient, r subscript s.

4-2-2-ib-ai-sl-correlation-coefficients-b-we-solution

c) Comment on the values of the two correlation coefficients.

4-2-2-ib-ai-sl-new-we-c

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Dan Finlay

Author: Dan Finlay

Expertise: Maths Subject Lead

Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.

Roger B

Reviewer: Roger B

Expertise: Maths Content Creator

Roger's teaching experience stretches all the way back to 1992, and in that time he has taught students at all levels between Year 7 and university undergraduate. Having conducted and published postgraduate research into the mathematical theory behind quantum computing, he is more than confident in dealing with mathematics at any level the exam boards might throw at you.