# Advantages of Spearman’s correlation coefficient analysis

Spearman rank correlation coefficient is a variant of Spearman rank correlation, which is not as strict as the product-difference correlation coefficient in terms of data conditions, as long as the observations of the two variables are paired rank-rated information, or rank information obtained by the transformation of the observations of the continuous variable, regardless of the overall distribution pattern of the two variables and the size of the sample capacity, it is possible to use the Spearman rank correlation to carry out the study. However, the Spearman rank correlation coefficient has higher data requirements and is suitable for discrete and ordered data, but not for continuous data. In addition, the calculation of the Spearman rank correlation coefficient is more complicated and requires the use of a computer or specialized statistical software.

### Difference between pearson correlation coefficient and spearman correlation coefficient

Difference:

Spearman correlation coefficient is also used between two ordinal measurements, not pearson correlation coefficient.

The pearson correlation is usually used to calculate the correlation between equidistant and isoparametric data, or continuous data, which are not limited to integer values.

For example, the correlation of two test scores before and after is suitable for the use of pearson correlation.

Spearman correlation is specifically used to calculate the relationship between the grade data, this type of data is characterized by the data have successive grades but the specific score difference between two consecutive grades may not be equal.

### How to understand the Spearman rank correlation coefficient?

The Spearman rank correlation coefficient is a measure of the correlation between two variables X and Y. It is a measure of the correlation between two variables.

The formula is:

Interestingly, it does not operate directly on the values of the dimensions of the variables, but rather on the ordering of the values of the dimensions, known as rank.

Obviously, if the two variables are monotonically identical, ρ=1 when the difference di between the ranks of the dimensions are both 0, and ρ=-1 when they are monotonically opposite.

Example of calculating the Spearman correlation coefficient between IQ values and the number of hours of TV watched per week:

The Spearman rank correlation coefficient is named after CharlesSpearman and often denotes its value by the Greek letter ρ (rho). The Spearman rank correlation coefficient is used to estimate the correlation between two variables X and Y, where the correlation between the variables can be described using a monotonic function.

If there are not the same two elements in both sets from which the two variables take their values, then ρ between the two variables can be +1 or -1 when one of the variables can be expressed as a good monotonic function of the other (i.e., when the two variables are trending in the same direction).