Decoding HR Patterns: A Step-by-Step Guide to Correlation Matrices with Python 

As I have mentioned many times in my articles (https://medium.com/@gayanehacik), we are all aware of how important data has become in the HR field. 

Using statistical methods, we perform many different analyses and reach meaningful results, which is very effective in accelerating complex decision-making processes. In this article, you will read about one of these methods: how to use the correlation matrix in HR processes. When you reach the end of the article, you can easily create your matrix and colorful map using Python.

If you are ready, here we go!

First, let’s talk about what correlation is. In its simplest form, it is a statistical term that allows you to observe the existence and strength of a linear relationship between two variables. We can list some of its features as follows:

• The correlation coefficient is denoted as “r” and ranges between -1 and 1.
• Positive values indicate a direct linear relationship; negative values indicate a reverse linear relationship.
• If the coefficient is 1, it indicates a very strong connection between these two variables.
• If the coefficient is 0, there is a neutral relationship. That is, there is no linear relationship between the variables.

The point you need to pay attention to is correlation is not causation. The close relationship between A and B does not arise from each other being cause and effect. Close correlation does not imply a cause-effect relationship in all cases.

I leave the mathematical formula below for how it is calculated. If you are interested, you can check the details.

All right, now that we have completed the theory, we can move on to practice.

You will need a data set to create your matrix. In this example, we will use the data set I explain below, but you can use the same codes with your data. Let’s say our data set includes the employee’s performance score, salary, engagement score, and age, and let’s examine whether there is a relationship between these variables using Python on Jupyter Notebook:

# Importing necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Sample HR data
hr_data = [
    {'PerformanceScore': 7.2, 'EngagementScore': 4.5, 'Age': 32, 'Salary': 52000},
    {'PerformanceScore': 6.8, 'EngagementScore': 4.2, 'Age': 45, 'Salary': 48000},
    {'PerformanceScore': 8.5, 'EngagementScore': 4.8, 'Age': 28, 'Salary': 60000},
    {'PerformanceScore': 7.0, 'EngagementScore': 4.0, 'Age': 36, 'Salary': 55000},
    {'PerformanceScore': 9.1, 'EngagementScore': 4.7, 'Age': 50, 'Salary': 65000},
]

# Convert the list of dictionaries to a DataFrame
hr_df = pd.DataFrame(hr_data)

# Generate correlation matrix
correlation_matrix = hr_df.corr()

# Plot the matrix using a heatmap for better visualization
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')

# Add comments
plt.title('HR Data Correlation Matrix')

# Comment on the heatmap
plt.xlabel('Variables')  # X-axis label
plt.ylabel('Variables')  # Y-axis label
plt.show()        

After creating the matrix, we visualize it with a heatmap in a way that we can understand more clearly. If everything goes well, you will see a map like the one below:

• First of all, the squares at the intersections of each row and column express the relationship between the corresponding data.
• Warmer colors (red) indicate stronger positive correlations and cooler colors (blue) indicate stronger negative correlations. The intensity of the color represents the strength of the correlation. For example, darker reds indicate stronger positive correlations. The annotations within the heatmap display the actual correlation coefficients between pairs of variables.
• Let’s say that you want to see if there is a relationship between performance score and salary, you should look at the intersection points of both variables as shown below:

• Here you see that a result of 0.95 is obtained. This is very close to 1, meaning a strong relationship, already shown in red.
• For example, when you want to check the hypothesis that younger employees are less engaged you could find the intersection point of the age and the engagement score variables:

• The absolute value of the correlation coefficient (|-0.13| = 0.13) is relatively close to zero, suggesting a weak correlation. The negative sign indicates a negative correlation. This means that as one variable increases, the other tends to decrease, and vice versa. However, the strength of this relationship is weak. 

There you have it!

These are some simple examples just to give you an idea of what the matrix does and how you could benefit from it. Understanding the correlation matrix can offer valuable insights into relationships between variables in HR data. The heatmap visualization provides a clear and intuitive way to interpret these relationships. Remember, correlation does not imply causation, so it’s crucial to approach the results with a thoughtful mindset.

As you’ve seen in this tutorial, we used a simple HR dataset to demonstrate the creation of a correlation matrix using Python. The heatmap vividly illustrates the strength and direction of relationships between different HR metrics. 

Now, it’s your turn to dive deeper!

I encourage you to try the provided code with your datasets. Whether you are an HR professional or a data enthusiast, modifying the code to fit your specific needs can uncover unique patterns and correlations within your data. Feel free to experiment with different variables or expand the dataset to explore more comprehensive analyses. 

Remember, the beauty of data analysis lies in its versatility. By adapting and extending the code presented here, you can apply these techniques to a wide range of HR scenarios. 

Happy exploring!

Cheers,

Gayane