The Cognitive Divide: A Multivariate Analysis of Head Start Program Effects on Early Childhood Development

Abstract

This paper presents a comprehensive statistical analysis of the Head Start early childhood intervention dataset using Python 3.11 and open-source data science libraries including pyreadstat, pandas, scipy, statsmodels, pingouin, scikitlearn, seaborn, and matplotlib. The dataset contains cognitive, demographic, and socioeconomic information collected from 969 children at baseline and follow-up. Analyses span descriptive statistics, independent and paired-samples t-tests, one-way and factorial ANOVA, factorial ANCOVA, multiple regression, logistic regression, Monte Carlo simulation, exploratory factor analysis, and non-parametric testing. Results indicate that while raw PPVT follow-up scores did not differ significantly between Head Start and control children in unadjusted comparisons, program assignment was a significant predictor after covariate adjustment. Racial group membership and baseline cognitive ability emerged as strong predictors of follow-up vocabulary performance. These findings underscore the value of multivariate and covariate-adjusted approaches when evaluating the impact of early intervention programs.