How to Use EDA to Study the Relationship Between Salary and Job Satisfaction

Exploratory Data Analysis (EDA) is an essential first step in understanding any dataset. It involves using various statistical and visualization techniques to summarize the main characteristics of the data. In the context of studying the relationship between salary and job satisfaction, EDA helps in identifying patterns, outliers, and potential correlations between these two variables. This process can guide further statistical analysis and modeling.

Here’s a structured approach to using EDA for studying the relationship between salary and job satisfaction:

1. Understand the Data

Before diving into EDA, it’s important to know the dataset. The data should contain at least two key variables:

• Salary: This could be presented as annual income, monthly salary, or any other form of monetary compensation.

• Job Satisfaction: Typically, this would be a rating scale (e.g., 1–5, 1–10), or it could be categorized as low, medium, or high satisfaction.

You may also have additional variables that could influence the relationship, such as job title, years of experience, department, or location. These could be useful for segmenting the data.

2. Data Cleaning

Before performing any analysis, it’s crucial to clean the data:

• Check for missing values: If there are missing values in salary or job satisfaction, you can either remove those rows or impute the missing data.

• Check for inconsistencies: For instance, ensure salary figures are within reasonable ranges, and job satisfaction ratings follow the expected scale.

• Remove duplicates: Ensure there are no duplicate records that could skew your analysis.

3. Univariate Analysis

Start by analyzing the distribution of each variable individually. This will give you insights into the general nature of the data.

Salary:

• Summary Statistics: Calculate the mean, median, minimum, maximum, and standard deviation to understand the central tendency and spread of the salary.

• Histogram: Plot a histogram to observe the distribution of salaries. Is it skewed to the right (high salaries are less frequent) or left (low salaries are more common)?

• Boxplot: This will highlight any outliers in the salary data.

Job Satisfaction:

• Summary Statistics: Calculate the central tendency and spread for job satisfaction. If it’s on a scale, the mean and mode are especially useful.

• Histogram or Bar Chart: Visualize the distribution of job satisfaction ratings. Are most employees satisfied, dissatisfied, or neutral?

• Boxplot: This will reveal any potential outliers in job satisfaction.

4. Bivariate Analysis (Salary vs. Job Satisfaction)

Now, you want to explore the relationship between salary and job satisfaction. This step involves analyzing how changes in one variable relate to changes in the other.

Scatter Plot:

• A scatter plot is an effective way to visualize the relationship between salary (on the x-axis) and job satisfaction (on the y-axis). Look for any visible patterns or trends.

• If there’s a positive correlation, the points may tend to move upwards from left to right (higher salary correlates with higher job satisfaction). If it’s negative, the points would slope downwards (higher salary correlates with lower job satisfaction).

Correlation Coefficient:

• Calculate the Pearson correlation coefficient between salary and job satisfaction. This will quantify the strength and direction of the relationship. A positive value indicates a positive relationship, and a negative value indicates an inverse relationship.

• Keep in mind that correlation doesn’t imply causation; it only shows the degree of linear association.

Boxplot (Grouped by Salary):

• If salary is categorized into different bands (e.g., low, medium, high), you can create a boxplot of job satisfaction scores for each salary band. This can help identify whether higher salary ranges tend to be associated with higher satisfaction scores.

Heatmaps (Optional):

• If your data has additional categorical variables (such as job title, department, etc.), you can use a heatmap to show how these variables interact with salary and job satisfaction.

5. Multivariate Analysis

To gain deeper insights into the relationship between salary and job satisfaction, consider introducing other variables. For example:

• Department: You may find that job satisfaction varies significantly across departments, even within the same salary range.

• Years of Experience: An analysis of how salary and job satisfaction vary with experience could uncover additional trends.

• Location: Geographic location might influence both salary and job satisfaction, so segmenting by this variable could provide valuable insights.

You can create:

• Pair Plots: If you have more variables, pair plots can help you visualize how each variable interacts with salary and job satisfaction.

• Facet Grids: These can be used to create subplots for different segments of the data (e.g., salary ranges, departments, etc.) to study their impact on job satisfaction.

6. Identify Outliers

Outliers can have a significant effect on the relationship between salary and job satisfaction. Identifying outliers through boxplots or scatter plots allows you to investigate if these data points should be removed or if they represent an important aspect of the dataset.

For instance, if someone has an extremely high salary but a low job satisfaction score, this could be worth investigating further, as it might suggest a unique but interesting pattern.

7. Trend Analysis and Hypothesis Testing

If you notice any patterns or trends during your EDA, you can proceed with hypothesis testing to validate your findings. For example:

• Does higher salary lead to higher job satisfaction? You can test this hypothesis using statistical tests such as t-tests or ANOVA, especially if you’ve categorized the salary into different groups.

• Does salary affect job satisfaction differently across departments? In this case, an analysis of variance (ANOVA) could help.

8. Visualizing the Relationship

Effective visualizations are key to communicating your findings clearly:

• Line or Bar Plots: If salary and job satisfaction are categorical, use bar charts to show the mean job satisfaction score for different salary groups.

• Regression Line: For a more statistical approach, you can fit a regression line (simple linear regression) to your scatter plot to see the linear relationship between salary and job satisfaction.

9. Conclusion and Insights

Finally, summarize the key insights you’ve gained from your analysis. For example:

• Is there a positive or negative relationship between salary and job satisfaction?

• Are there any specific salary ranges that are strongly associated with higher job satisfaction?

• Are there any additional variables (e.g., department, experience, location) that significantly impact the relationship?

By conducting a thorough EDA, you’ve explored and understood the nuances of the data, which will lay the groundwork for more advanced modeling and decision-making. This approach ensures that any conclusions drawn are backed by solid data insights.