Analyzing Mathematics Assessment Data in Florida: A Multilevel Approach

Abstract

Understanding mathematics achievement data requires analyzing if and how factors at the school and county level can influence student outcomes. This research applies multilevel modeling to explore the relationships between school characteristics which includes economically disadvantaged student percentages, English Language Learner (ELL) rates, Students with Disabilities (SWD) rates, funding per student, and chronic absenteeism, alongside county level factors of poverty and crime rates. A total of 492 high schools across all 67 Florida counties were analyzed using a two-level hierarchical linear model. The findings reveal that while school level factors significantly predict mathematics achievement, county level variables exhibit limited direct effects. Notably, cross-level interactions suggest that increased school funding in high poverty counties can mitigate disparities and enhance educational outcomes. These results emphasize the critical role of equitable resource distribution and targeted strategies to address systemic inequities.