Analyzing Chilean College Admissions Data: Insights on Demographics, Income, and Education

Introduction

In this analysis, we delve into a dataset concerning Chilean college admissions, focusing on both academic and demographic variables. Our objective is to extract meaningful insights from the data, addressing questions related to demographics, income, and educational outcomes. To achieve this, we’ll follow a structured approach, including data preprocessing, computation of relevant metrics, and creating insightful visualizations.

Separating Demographics Data

The first step in our analysis is to segregate the demographic data into distinct columns. This involves extracting information such as date of birth (DOB), family size, the number of family members working, and income level.

Creating a Table for Demographics

To facilitate our analysis, we’ll create a dedicated table for demographics, ensuring that data is organized and easily accessible for subsequent steps.

Filtering Incomplete or Erroneous Data

Data quality is crucial. We’ll apply filters to remove observations with incomplete or inaccurate demographic information. For example, we may filter out records with missing DOB or family size, ensuring the reliability of our analysis.

Computing Current Age

We calculate the current age of each student using their DOB. This involves parsing the date format provided in the dataset and subtracting it from the current date.

Computing Median Monthly Income per Family Member:

To understand income distribution, we compute the median monthly income per family member. For the highest income level, we consider $4000/month as the approximate value.

Analyzing Median Income in Relation to Parental Education:

We create a two-dimensional pivot table to explore the relationship between the median monthly income and the level of education of both parents (father and mother). This helps us identify patterns and correlations.

Examining the Effect of Family Size on NEM Scores

We include the variable “number of family members” in the NEM scores table and create a pivot table to investigate how family size impacts NEM scores, shedding light on potential socioeconomic factors.

Analyzing the Impact of Parental Education on Academic Performance

We calculate the average between Math (MATE) and Language (LYC) scores and analyze the effects of the level of education of the mother and father. This allows us to identify whether parental education plays a significant role in students’ academic performance and which factor has a more substantial effect.

Investigating the Relationship Between Income and Test Scores:

We explore how the level of income is related to test scores. By plotting income levels against scores, we can assess whether higher income is correlated with better academic performance.

Exploring the Association Between High School Type and Income

Lastly, we examine the relationship between the type of high school attended and income levels. This analysis helps us understand whether certain high school types are associated with higher income levels.

Conclusion

This comprehensive analysis of the Chilean college admissions dataset provides valuable insights into the interplay between demographics, income, and educational outcomes. By meticulously preprocessing data, computing relevant metrics, and leveraging pivot tables, we have uncovered correlations and patterns that can inform educational policies and interventions. Understanding the intricate relationships among these variables is crucial for creating a fairer and more equitable educational system in Chile.