Disaggregated Data—Definition and Example
What is Disaggregated Data?
Disaggregated data is data that is broken down and examined based on subgroups (gender, ethnicity, special education, limited English proficiency, free and reduced lunch, mobility, continuous enrollment, age, experience, education level, etc.). This data is necessary to fairly and accurately assess the needs of both students and teachers. By looking at subgroups we can assess not only what improvement areas there are but also specifically what needs to be improved in those areas.
What is an Example of Disaggregated Data?
Example:
At Gopher State Middle School 20% of the total student population in grade seven scored below proficient on the MCA reading assessment. Upon examination it was found the following subgroups of students scored below proficient on the MCA-II reading assessment:
• 18% of white students
• 43% of the black students
• 15% of the Asian students
• 20% of the non-migrant (mobility status) students
• 53% of the special education students
• 13% of the regular education students
• 20% of the students without limited English proficiency
• 32% of the students with limited English proficiency
• 23% of the continuously enrolled students
• 35% of the eligible for free and reduced lunch.
The data reveals the students with the highest need are the special education students. When we examine the data in more detail we find that our female students in this population are the most in need. As for the skills they specifically need, we find that evaluative comprehension and comprehension skills as a whole are the largest areas needing improvement for all of our students, especially our identified improvement population.
This example can be seen by graphing the data and analyzing the created graphs.
MDE E-mail Updates