Examination Of Early Child Longitudinal Study

Description of the Project:

This project explores child longitudinal study data that outlines data points on fall/spring reading, math, and general knowledge scores of individual students by income. The data shows the scores for fall 1998 and spring 1999 measurements, evaluating kindergarten students over the span of several months. The analysis is guided by the following questions:

Research question 1: Does income group significantly predict differences in spring general knowledge scores among kindergarten students after controlling baseline reading and math abilities? To answer the research question on whether the income group significantly predicts differences in spring general knowledge scores among kindergarten students after controlling baseline reading and math abilities the one-way ANCOVA has been employed.

Research question 2: Does income group predict spring math scores while controlling for baseline fall math scores? The one-way ANCOVA has been conducted to respond to the research question whether the income group predicts spring math scores while controlling for baseline fall math scores.

Submitted files:

The following files have been submitted for this project:

• "ANCOVA_Analysis_with_Python.ipynb" is a Python notebook that contains the code for EDA and ANCOVA Test.
• "Report_on_the_Analysis_of_Covariance_Vizualizations_and_Interpretations.docx" is a report with the major findings and interpretations.

Prerequsites:

To run the notebook file the following prerequsites should be considered:

• Access to the Python 3.13.1 or latest version
• Libraries such as numpy, pandas, matplotlib.pyplot, seaborn , plotline should be imported for the EDA.
• Libraries such as scipy.stats, statsmodels.api, statsmodels.formula.api, and pingouin should be imported for the statistical analysis.