Tutorial 7: Applied Examples in Economics of Education

Author

Rony Rodriguez-Ramirez

Note

Unfortunately, I haven’t been able to finish this tutorial.

Advanced Analysis and Visualization in the Economics of Education

In this final tutorial, we will explore advanced data analysis and visualization techniques using the Tidyverse, tailored to the field of economics of education. We will cover topics such as regression analysis, creating complex visualizations, and interpreting the results of these analyses. These skills are crucial for conducting in-depth research and effectively communicating your findings. First, load the tidyverse package.

library(tidyverse)

7.1 Regression Analysis in Education Research

Regression analysis is a powerful statistical tool used to examine the relationships between variables. In the context of education economics, it can be used to explore factors affecting student performance, school funding, and other key outcomes.

7.1.1 Simple Linear Regression: Analyzing the Impact of Study Hours on Test Scores

A simple linear regression can be used to assess the relationship between a student’s study hours and their test scores.

# Example data frame
study_data_df <- data.frame(
  StudentID = c(1, 2, 3, 4, 5),
  Study_Hours = c(10, 12, 8, 15, 9),
  Test_Score = c(85, 88, 78, 92, 80)
)

# Performing simple linear regression
study_regression <- lm(Test_Score ~ Study_Hours, data = study_data_df)

# Summary of the regression model
summary(study_regression)

Call:
lm(formula = Test_Score ~ Study_Hours, data = study_data_df)

Residuals:
 1  2  3  4  5 
 2  1 -1 -1 -1 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  63.0000     3.2607  19.321 0.000303 ***
Study_Hours   2.0000     0.2942   6.797 0.006511 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.633 on 3 degrees of freedom
Multiple R-squared:  0.939, Adjusted R-squared:  0.9187 
F-statistic:  46.2 on 1 and 3 DF,  p-value: 0.006511

Explanation:

  • lm() fits a linear regression model to the data, with Test_Score as the dependent variable and Study_Hours as the independent variable.
  • summary() provides detailed statistics about the regression model, including coefficients, R-squared, and p-values.