Project Team
Students
Muhammad Adam Danial bin Mohd Nasir
Information Sciences and Technology
Penn State Harrisburg
Faculty Mentors
Rukayya Ibrahim
Penn State Harrisburg
Science, Engineering and Technology
Gregory Jenkins
Penn State University Park
Meteorology
Project
Project Video
Project Abstract
In the complex landscape of American education, disparities in academic achievement and educational opportunities persist across socioeconomic lines. These disparities may be further nuanced by the varying availability and accessibility of tertiary education opportunities across different socioeconomic strata. This research employs Hierarchical Linear Modeling (HLM) to delve into these intricate dynamics, focusing on Pennsylvania’s education landscape as a microcosm of the broader national context.
Leveraging diverse datasets, including postsecondary admission rate, county-level social vulnerability index, income distribution, violent crime rates, racial demographics, and tertiary education admission rates, this study performs a comprehensive multilevel analysis. The assembled dataset encompasses 218,331 students across 734 schools in 67 counties of Pennsylvania, representing a wide spectrum of SES and academic achievement scenarios.
Initial analyses pointed towards a pronounced SES-academic achievement divide, with schools in high-SES regions surprisingly reporting markedly low postsecondary admission rates. This counterintuitive relationship sparked curiosity. One might naturally assume that schools in higher SES regions would yield higher rates of post-secondary admissions given the associated benefits such as access to better resources, both material and intellectual.
However, our data suggested otherwise, indicating that the reality of academic achievement as it relates to SES is more nuanced than conventional wisdom might suggest. In the face of such complexity, it became apparent that traditional analytical methods might fail to capture the full extent and nature of the relationships involved. Given the multilevel nature of the data, HLM was utilized to account for the nested data structure and accurately estimate the relationships under scrutiny.
Anticipated findings from this study hold the potential to illuminate the multifaceted interplay between SES, academic achievement, and tertiary education opportunities. The insights could carry substantial implications for policy-making and interventions aimed at mitigating SES-driven educational disparities and promoting academic success across all socioeconomic strata.
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