Disaggregated data refers to information broken down by specific student subgroups—demographics, programs, grade levels, or other characteristics—rather than reported only as school-wide averages. Disaggregation reveals whether all students experience school equitably or whether specific groups face different outcomes or experiences.

Common disaggregation categories include race/ethnicity, gender, special education status, English learner status, socioeconomic status (free/reduced lunch eligibility), grade level, and enrollment timing (legacy families versus recent enrollees). For example, school-wide average belonging score might be 4.2 out of 5, but disaggregation might reveal that students of color rate belonging at 3.5 while white students rate it 4.6—a disparity the aggregate hides.

Disaggregated data supports equity by making visible whether schools serve all students well or only some. It enables targeted interventions: if data shows 6th graders consistently report lower belonging than other grades, schools can implement 6th grade-specific advisory programs. If students entering mid-year report lower satisfaction, schools can strengthen onboarding.

For charter and private schools, disaggregated retention data reveals which students are at highest risk of leaving: specific grades, demographic groups, program participants, or enrollment cohorts. Pulse survey data disaggregated by student characteristics identifies equity gaps in climate and belonging that might drive disparate attrition rates. Schools committed to serving all families well use disaggregation to ensure no group systematically experiences school as unwelcoming or unsupportive.