How to Visualize the Impact of Public Education Spending on Student Achievement Using EDA

Exploratory Data Analysis (EDA) is an essential process in understanding the relationships and patterns within data before diving into more complex modeling or analysis. In the case of visualizing the impact of public education spending on student achievement, EDA allows us to uncover insights and guide further investigations. Below is a comprehensive approach to using EDA for this purpose.

1. Define Key Metrics for Education Spending and Student Achievement

Before starting any analysis, it’s important to define what exactly you’re measuring. For the sake of this EDA, let’s assume we have the following key metrics:

• Public Education Spending: This could be represented by figures such as government expenditure per student, total school funding, or per-pupil expenditure by district, state, or country.

• Student Achievement: Common measures of student achievement include standardized test scores (e.g., SAT, ACT, state-level assessments), graduation rates, or even long-term outcomes like college enrollment rates.

Both of these variables should be quantifiable and available at a consistent level (e.g., district, state, national).

2. Collect and Prepare the Data

Gather relevant data for both education spending and student achievement. Common sources include:

• National Center for Education Statistics (NCES): Offers data on education spending at various levels.

• OECD Education Database: Provides global education spending data.

• State and Local Education Departments: For more granular data at the state or district level.

Clean and preprocess the data, ensuring that:

• There are no missing values (or they are handled appropriately).

• The data is normalized if comparing different regions or years (e.g., inflation-adjusted spending).

• Outliers are identified, as they may skew visualizations or analysis.

3. Initial Data Exploration and Summary Statistics

Start by exploring the dataset using summary statistics and basic visualizations. The goal is to understand the distribution, range, and basic trends in both education spending and student achievement.

• Descriptive Statistics: Mean, median, standard deviation, minimum, and maximum of both education spending and achievement metrics.

• Correlation: Calculate the correlation coefficient between education spending and student achievement to get a sense of their linear relationship.

• Histograms: Plot histograms for both spending and achievement to understand their distributions. Are the values normally distributed? Are there any skewness or outliers?

• Box Plots: Use box plots to visualize the spread of data and identify potential outliers in both metrics.

Example:

python
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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Sample dataset loading
df = pd.read_csv('education_data.csv')

# Descriptive statistics
print(df[['spending_per_student', 'test_scores']].describe())

# Histograms
df[['spending_per_student', 'test_scores']].hist(figsize=(12, 6))
plt.show()

# Box plots
sns.boxplot(x='spending_per_student', data=df)
plt.show()

4. Visualizing Relationships Between Spending and Achievement

After understanding the basic characteristics of the data, we can start looking at relationships between public education spending and student achievement. This is where EDA truly shines, as you can visualize how one variable may influence the other.

• Scatter Plots: Scatter plots are ideal for visualizing the relationship between two continuous variables. Plot education spending against student achievement (test scores) to see if there is a visible pattern.

  If the data is too noisy, consider adding a trend line using linear regression or a smoother (e.g., Loess).

  Example:

  python
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  sns.scatterplot(x='spending_per_student', y='test_scores', data=df)
  plt.title('Education Spending vs. Student Achievement')
  plt.show()
  

• Facet Grid: If you have data across different regions (states, districts), you can use facet grids to plot scatter plots for different regions or categories.

  Example:

  python
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  g = sns.FacetGrid(df, col="region", height=4)
  g.map(sns.scatterplot, 'spending_per_student', 'test_scores')
  plt.show()
  

• Heatmaps: If you have multiple variables, a heatmap can help visualize correlations between various metrics. For example, you could visualize the correlation between different types of spending (e.g., teacher salaries, infrastructure) and student achievement.

  Example:

  python
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  correlation_matrix = df.corr()
  sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')
  plt.show()
  

5. Exploring Trends Over Time

If your data spans multiple years or academic cycles, you can visualize trends in both education spending and student achievement over time. This can help you spot any long-term patterns or changes in the relationship between spending and achievement.

• Line Plots: Plot the average spending and achievement over time to see if they are increasing or decreasing.

• Change Over Time: You could also calculate the year-over-year percentage change in both variables and plot them to assess how spending and achievement evolve in relation to each other.

Example:

python
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df['year'] = pd.to_datetime(df['year'], format='%Y')
df_grouped = df.groupby('year').agg({'spending_per_student': 'mean', 'test_scores': 'mean'}).reset_index()

plt.figure(figsize=(12, 6))
plt.plot(df_grouped['year'], df_grouped['spending_per_student'], label='Spending Per Student', color='blue')
plt.plot(df_grouped['year'], df_grouped['test_scores'], label='Test Scores', color='red')
plt.legend()
plt.title('Education Spending and Student Achievement Over Time')
plt.show()

6. Breakdown by Categories or Groups

It’s important to break down the data further to identify patterns in specific subgroups. For example:

• Geographic Differences: Are there differences in the relationship between education spending and achievement between states or countries?

• School Type: Do public vs. private schools show different trends?

• Demographic Factors: How do spending and achievement correlate across different demographics, such as income or ethnicity?

Use violin plots, bar charts, or pair plots to visualize these relationships in various subgroups.

Example (facet by region):

python
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sns.violinplot(x='region', y='test_scores', data=df)
plt.title('Test Scores by Region')
plt.show()

7. Modeling or Statistical Testing (Optional)

While EDA is primarily focused on exploration and visualization, you may want to go a step further and apply statistical tests or regression models to confirm your findings.

• Linear Regression: Fit a simple linear regression model to quantify the relationship between education spending and achievement.

• Statistical Tests: You can apply t-tests or ANOVA to test if differences in spending lead to statistically significant differences in achievement, depending on your groups.

Conclusion

EDA is an effective first step in understanding the complex relationship between public education spending and student achievement. By visualizing trends, relationships, and patterns within the data, you can generate insights that guide further analysis, whether through statistical testing, machine learning, or policy recommendations.