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.

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

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.

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.

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.

Aspect | Pearson Correlation Coefficient | Spearman Correlation Coefficient |
---|---|---|

Purpose | Measures linear relationships | Measures monotonic relationships |

Assumptions | Variables are normally distributed, linear relationship | Variables have monotonic relationship, no assumptions on distribution |

Calculation Method | Based on covariance and standard deviations | Based on ranked data and rank order |

Range of Values | -1 to 1 | -1 to 1 |

Interpretation | Strength and direction of linear relationship | Strength and direction of monotonic relationship |

Sensitivity to Outliers | Sensitive to outliers | Less sensitive to outliers |

Data Types | Appropriate for interval and ratio data | Appropriate for ordinal variables and non-normally distributed data |

Sample Size | The Pearson correlation coefficient isn’t the most efficient choice for small sample sizes. | This method works well with smaller samples and doesn’t require normality assumptions. |

Usage | Assessing linear associations, parametric tests | Assessing monotonic associations, non-parametric tests |

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.

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.

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:

Student | Study Time (hours) | Exam Score |
---|---|---|

A | 10 | 75 |

B | 8 | 60 |

C | 12 | 85 |

D | 6 | 55 |

E | 9 | 70 |

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

Student | Study Time (hours) | Rank (Study Time) | Exam Score | Rank (Exam Score) |
---|---|---|---|---|

A | 10 | 3 | 75 | 3 |

B | 8 | 4 | 60 | 5 |

C | 12 | 1 | 85 | 1 |

D | 6 | 5 | 55 | 6 |

E | 9 | 2 | 70 | 4 |

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 Time*i*−Rank of Exam Score*i*

Student | Di |
---|---|

A | 0 |

B | -1 |

C | 0 |

D | -1 |

E | -2 |

Next, we square each (*Di)* value:

Student | 2Di2 |
---|---|

A | 0 |

B | 1 |

C | 0 |

D | 1 |

E | 4 |

The sum of ��2*D**i*2 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.

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:

```
> 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
```

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

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

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

```
> 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
```

```
> 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).

As we can see both the correlation coefficients give the positive correlation value for Girth and Height of the trees but the value given by them is slightly different because Pearson correlation coefficients measure the linear relationship between the variables while 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 whereas the rate is constant in a linear relationship.

At the end of this article , I want to say that hope you like the article of pearson and spearman correlation. You get a clear information of pearson vs spearman correlation and its difference .Also,

A. The Pearson and Spearman correlation measures the strength and direction of the relationship between variables. Pearson correlation assesses linear relationships, while Spearman correlation evaluates monotonic relationships.

A. Spearman correlation is useful when the relationship between variables is not strictly linear but can be described by a monotonic function. It is commonly used when dealing with ordinal or non-normally distributed data.

It is inaccurate to say that Spearman correlations are inherently more powerful than Pearson correlations. The choice between the two depends on the specific characteristics and assumptions of the data and the research question being addressed.

A. Spearman correlation is not always higher than Pearson correlation. The magnitude and direction of the correlation can differ between the two measures, especially when the relationship between variables is nonlinear or influenced by outliers. The choice between the two should be based on the data and the research objectives.

A. Kendall’s tau and Spearman’s rank are similar correlation coefficients for non-normal data. Here’s the key difference:**Kendall’s tau:** More robust to outliers, better for small samples (uses concordant/discordant pairs).**Spearman’s rank:** Might give slightly higher values, but more sensitive to outliers (uses rank differences).

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Thanks a lot. This is really useful.