Title

A Multi-level Quantitative Analysis on Factors Affecting Special Education Compliance

Year Issued

2022

Abstract

The Individuals with Disabilities Education Act is a federal law that requires that students with disabilities are provided with a Free and Appropriate Public Education. The cost of ensuring FAPE can be quite high for students that require more specialized supports; ...
Show moreThe Individuals with Disabilities Education Act is a federal law that requires that students with disabilities are provided with a Free and Appropriate Public Education. The cost of ensuring FAPE can be quite high for students that require more specialized supports; however, in not meeting that standard, districts may be liable for compensatory education. Much of the responsibility to fund special education, especially in Pennsylvania, falls on the schools to raise through local taxes. This has put schools in very challenging circumstances in regard to their budget. States across the country have conducted a Costing Out Analysis to review the necessary spending levels reviewed for education. Many studies discuss the need for higher funding allocations from state and federal governments so that the high-cost burden does not fall on LEAs and local tax dollars to meet the IDEA compliance requirements. While overall compliance with IDEA has improved over time, it is still an issue that needs to be addressed. This study used multiple regression to determine if there is a link between compliance with the Cyclical Monitoring for Continuous Improvement and factors such as special education spending and percentage of population receiving special education supports. The data are all public record and was collected through Pennsylvania Department of Education online Databases. The data revealed a regression model suggests that the relationship between the number of areas of non-compliance has a significantly moderate negative correlation with both the number of special education expenditures and the percentage of special education students. The model can explain or predict 11.4% of the number of areas of noncompliance in a school district.
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Author

Justin Karam