Date Approved


Degree Type

Open Access Dissertation

Degree Name

Doctor of Education (EdD)

Department or School

Leadership and Counseling

Committee Member

William Price

Committee Member

James Berry

Committee Member

David Anderson

Committee Member

Derrick Fries


The primary purpose of this study was to compare the Michigan Department of Education’s (MDE) school ranking system, the Top to Bottom Ranking, to the school’s percentage of economically disadvantaged students as measured by the percentage of free and reduced-price lunch. In addition, finding that a correlation exists, the study sought to identify an alternate method of reporting school effectiveness, taking into consideration the school’s percentage of economically disadvantaged students. Utilizing the current Top to Bottom Ranking system, the Michigan Department of Education assigns an array of requirements, sanctions, and rewards, depending on where a school building ranks. This ranking system does not take into account the factor of the socio-economic status of the students attending the school. It was the intent of the study to show an inverse correlation between a school’s percentage of economically disadvantaged students and the school’s relative ranking on the Top to Bottom list. Additionally, the study aimed to provide an alternate solution for reporting and classifying school quality. Specific research questions included 1) Does an inverse correlation exist between a school’s rank and its economically disadvantaged (ED) population? and 2) Is it possible to create a ranking system that uses the MDE metrics of a building’s student achievement scores, students achievement change, and students achievement gap while factoring in a school’s percentage of economically disadvantaged student population? Using a Pearson product-moment correlation coefficient on the 2011-12 and 2012-13 MDE’s school data-based information, a significant correlation, -0.7525514 and -0.7379997, respectively, confirmed a p-value < 0.0001. Furthermore, using quantile regression, a new ranking model was created allowing one to control for an acceptable correlation between a school building’s ranking and a school’s percentage of economically disadvantaged.