Question

Explain merit and demerit of spearman rank correlation .

Answer 1

The Spearman rank correlation coefficient, rs , is a nonparametric measure of correlation based on data ranks. It is obtained by ranking the values of the two variables (X and Y) and calculating the Pearson rp on the resulting ranks, not the data itself. Again, PROC CORR will do all of these actual calculations for you.

The Spearman rank correlation coefficient has properties similar to those of the Pearson correlation coefficient, although the Spearman rank correlation coefficient quantifies the degree of linear association between the ranks of X and the ranks of Y. Also, rs does not estimate a natural population parameter (unlike Pearson's rp which estimates ρp ).

An advantage of the Spearman rank correlation coefficient is that the X and Y values can be continuous or ordinal, and approximate normal distributions for X and Y are not required. Similar to the Pearson rp , Fisher's Z transformation can be applied to the Spearman rs to get a statistic, zs , that has an asymptotic normal distribution for calculating an asymptotic confidence interval. Again, PROC CORR will do this as well.

The Spearman rank correlation coefficient is only to be used to describe the relationship between linear data.​ Also, it can be used for data at the ordinal level and it is easier to calculate by hand than the Pearson correlation coefficient.

The Spearman rank correlation coefficient is only to be used to describe the relationship between nonlinear data.​ Also, it can be used for data at the ordinal level and it is easier to calculate by hand than the Pearson correlation coefficient.

The Spearman rank correlation coefficient can be used to describe the relationship between linear or nonlinear data.​ Also, it can be used for data at the ordinal level and it is easier to calculate by hand than the Pearson correlation coefficient.

Answer 2

Merit and Demerit of spearman rank correlation:

Spearman rank correlation is also known as Spearman rho by Charles Spearman. It shows relationship between two variables which can be defined by monotonic functions.

1. Merits of rank correlation function

• It is easy to compute
• It is easy to understand
• It can be computed with any kind of variables be it independent or dependent

2. Demerits of rank correlation function

• Only non-liner data can be computed.
• Regression can’t be computed
• When data has higher values it is difficult to compute.