Comparison of Pearson vs Spearman Correlation Coefficients

Pearson and Spearman correlation coefficients are two widely used statistical measures when measuring the relationship between variables. The Pearson correlation coefficient assesses the linear relationship between variables, while the Spearman correlation coefficient evaluates the monotonic relationship.

In this article, we will delve into a comprehensive comparison of these correlation coefficients for correlation analysis. We will explore their calculation methods, interpretability, strengths, and limitations. Understanding the differences between Pearson and Spearman correlation coefficients is crucial for selecting the appropriate measure based on the nature of the data and the research objectives.

Also, we are covering the difference between Pearson and Spearman correlation. We will explore Pearson vs Spearman, highlighting their unique applications, and discuss when to use Pearson correlation vs Spearman in data analysis.

Let’s explore the difference between Pearson vs Spearman Correlation Coefficients!

What is Correlation?

Correlation is a bivariate statistical measure that tells us about the association between the two variables. It describes how one variable behaves if there is some change in the other variable.

If the two variables are increasing or decreasing in parallel then they have a positive correlation between them and if one of the variables is increasing and another one is decreasing then they have a negative correlation with each other. If the change of one variable has no effect on another variable then they have a zero correlation between them.

Importance of Correlation coefficients

Correlation coefficients are like universal translators in the world of machine learning and data science. They help us understand the language between variables – how much, and in what direction, they change together.

Here’s why they’re crucial:

• Finding patterns: Uncovering hidden relationships between features, like what factors influence house prices.
• Picking the best features: Choosing the most relevant data for machine learning models, making them more efficient.
• Understanding models: Seeing how models interpret data and identifying potential issues.

What is Spearman Correlation used for?

Spearman’s correlation, another name for Spearman’s rank correlation coefficient, is a statistical tool that dives into how two variables are connecte. Instead of assuming a straight line relationship, it assesses how much one variable tends to go up or down as the other changes along with it. This change, called a monotonic relationship, can be either a steady increase together or a consistent decrease together. Even if the data doesn’t form a perfect line, Spearman’s correlation can reveal this underlying trend.

Pearson vs Spearman Correlation

What is Pearson Correlation Coefficient?

The Pearson correlation coefficient also known as linear correlation is a statistical measure that quantifies the strength and direction of a linear relationship between two continuous variables. It ranges from -1 to 1, with values close to -1 indicating a strong negative linear relationship, values close to 1 indicating a strong positive linear relationship, and 0 indicating no linear relationship.

What is Spearman Correlation Coefficient?

The Spearman correlation coefficient is a statistical measure that assesses the strength and direction of a monotonic relationship between two variables. It ranks the data rather than relying on their actual values, making it suitable for non-normally distributed or ordinal data. It ranges from -1 to 1, where values close to -1 or 1 indicate a strong monotonic relationship, and 0 indicates no monotonic relationship. Spearman correlation is valuable for detecting and quantifying associations when linear relationships are not assumed or when dealing with ranked or ordinal scale.

Example of Spearman’s Rank Correlation

Spearman’s Rank Correlation:

Let’s say we want to determine the relationship between the study time (in hours) and the exam scores (out of 100) of a group of students. We have the following data for five students:

First, we rank the study time and exam scores separately:

Now, we calculate the differences between the ranks for each pair of data points:

• P=Rank of Study Time−Rank of Exam Score, Di​=Rank of Study Timei​−Rank of Exam Scorei​

Next, we square each (Di)​ value:

The sum of ��2Di2​ is 0+1+0+1+4=60+1+0+1+4=6.

So, the Spearman’s Rank Correlation coefficient (ρ) between study time and exam scores is 0.7, indicating a strong positive correlation.

Practical application of correlation using R?

Determining the association between Girth and Height of Black Cherry Trees (Using the existing dataset “trees” which is already present in r and can be accessed by typing the name of the dataset, list of all the data set can be seen by using the command data() )

Below is the code to compute the correlation:

Loading the Dataset

> data <- trees
> head(data, 3)
  Girth Height Volume
1   8.3     70   10.3
2   8.6     65   10.3
3   8.8     63   10.2

Creating a Scatter Plot Using ggplot2 Library

> library(ggplot2)
> ggplot(data, aes(x = Girth, y = Height)) + geom_point() + 
+   geom_smooth(method = "lm", se =TRUE, color = 'red')

Test for Assumptions of Correlation

Here two assumptions are checked which need to be fulfilled before performing the correlation (Shapiro test, which is test to check the input variable is following the normal distribution or not, is used to check whether the variables i.e. Girth and Height are normally distributed or not)

> shapiro.test(data$Girth)

	Shapiro-Wilk normality test

data:  data$Girth
W = 0.94117, p-value = 0.08893

> shapiro.test(data$Height)

	Shapiro-Wilk normality test

data:  data$Height
W = 0.96545, p-value = 0.4034

p–value is greater than 0.05, so we can assume the normality

Correlation

> cor(data$Girth,data$Height, method = "pearson")
[1] 0.5192801
> cor(data$Girth,data$Height, method = "spearman")
[1] 0.4408387

Testing the Significance of the Correlation

For Pearson

> Pear <- cor.test(data$Girth, data$Height, method = 'pearson')
> Pear

	Pearson's product-moment correlation

data:  data$Girth and data$Height
t = 3.2722, df = 29, p-value = 0.002758
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2021327 0.7378538
sample estimates:
      cor 
0.5192801

For Spearman

> Spear <- cor.test(data$Girth, data$Height, method = 'spearman')
> Spear

	Spearman's rank correlation rho

data:  data$Girth and data$Height
S = 2773.4, p-value = 0.01306
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.4408387

Since the p-value is less than 0.05 (For Pearson it is 0.002758 and for Spearman, it is 0.01306, we can conclude that the Girth and Height of the trees are significantly correlated for both the coefficients with the value of 0.5192801 (Pearson) and 0.4408387 (Spearman).

Pearson vs Spearman Correlation – Final Verdict

As we can see both the correlation coefficients give the positive correlation value for Girth and Height of the trees. Still, the value given by them is slightly different because Pearson correlation coefficients measure the linear relationship between the variables. In contrast, Spearman correlation coefficients measure only monotonic relationships, relationship in which the variables tend to move in the same/opposite direction but not necessarily at a constant rate. In contrast, the rate is constant in a linear relationship.

Hope you like the article! Understanding the differences between Pearson vs Spearman correlation methods is essential for data analysis. Pearson measures linear relationships, while Spearman assesses monotonic relationships. For correlation examples, use Pearson for continuous data and Spearman for ordinal data. The formulas for both correlations vary, influencing when to use each method effectively. Knowing correlation Pearson vs Spearman helps ensure accurate results in your analyses.