Considerations for disaggregation of data
Disaggregation and cohort tracking are two of the most powerful ways to work with data. Typically, student achievement data are reported for whole populations or as aggregate data. Disaggregation involves dismantling or separating the aggregate information and looking at the performances of specific subgroups of students by variables such as ethnicity, gender, or attendance records. Disaggregating academic data within and among student groups and to the classroom and building level are important ways to understand what happens in classrooms and schools and thus better serve all students. Disaggregation often sheds light on critical issues that were undetected previously.
Sample disaggregating oriented questions with regard to math and science achievement:
- Is there an achievement gap in math and science among different groups? Is it getting bigger or smaller?
- Are minority or female students enrolling in higher-level math and science courses at the same rate as other students?
- Are poor or minority students overrepresented in special education or underrepresented in gifted and talented programs?
- Are students at certain grade levels doing better in math and science than students at other grade levels?
- Is transience or attendance a factor in math and science achievement?
- Are students whose teachers participate in ongoing professional development doing better than students whose teachers do not?
- Are the improvements we are making in the math and science program raising the performance of students in the lowest quartile?
- Does English proficiency affect student acheivement in math and science?