We tend to think about working with data in terms of numbers and computation. But we collect and analyze data because it tells us stories about our students and the way they experience and are impacted by the community college system.
Large data sets collected over all students are important for studying gross trends and averages, but can mask differences in the needs and challenges of different subgroups.
Data disaggregation lets us break down or filter large data sets to reveal the underlying trends, patterns, or insights as they relate to specific segments of the total population. Disaggregation can help identify issues like disparities in standardized-test scores or enrollment patterns based on age, ethnicity, gender, and much more.
Content in this topic area is organized into three sections: Data sources, techniques of data disaggregation, and applications.