Paths to Mathematics Success: A Path Analysis of Educational Expenditures to Math Outcomes of Latine Students
This quantitative critical study employed multiple linear path analysis models with difference scores to investigate the effects of funding adjustments on mathematics learning rates for Latine students. The theoretical framework integrated the Education Production Function with the QuantCrit frameworks to untangle the complex web of relationships associating educational funding adjustments and the academic outcomes of Latine students. The study specifically examined these relationships across U.S. districts stratified by poverty level (20%). To strengthen the causal inference and enhance control over confounding variables, the research design incorporated several key methodological features: (1) the use of difference scores to control for baseline academic attainment; (2) a cohort comparison framework to mitigate national contextual confounding variables; (3) a Funding Effort approach to account for regional economic differences; and (4) a five-year temporal separation between cohorts to better capture the lagged effect of funding associated changes, as observed in regression discontinuity studies. As the studied cohorts straddled the COVID-19 pandemic, the findings specifically illuminate the educational experience of Latine students during a period of systemic educational disruption. The study combined data from the Stanford Education Data Archive, Rutgers School of Finance’s District Cost Database, National Center for Education Statistics, and Bureau of Economic Analysis. The study assessed both the direct effects of funding changes and their indirect effects mediated by strategic expenditures. Results indicated that funding’s impact was exclusively context-dependent, manifesting significantly only in high-poverty districts (>20%). In these districts, funding-driven reductions in student-to-teacher ratios were predictive of stronger math learning rates for Latine students. Counterintuitively, increases in pupil support services were predictive of slower math learning rates; a relationship likely confounded by the severe and simultaneous disruptions of the pandemic. This study demonstrates the value of using difference scores and path analysis to help disentangle the causal effects in educational funding research, and the need to expand and improve longitudinal data systems. Future research should investigate the intersection of poverty and the concentration of ethnic student groups as related to school funding and related strategic expenditure choices on the academic outcomes of traditionally underserved populations.
Class sizes, Equitable school funding, Latine, Math growth, Professional development, Student services, Education
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