What are Disaggregates?
Disaggregating data can help decision makers assess disparities, expose hidden trends, and make informed decisions that lead to more equitable outcomes. In the E-W Framework, “disaggregates” refer to background or contextual characteristics of individuals and systems by which data should be examined to analyze disparities, monitor progress, and guide action. The framework recommends that education-to-workforce (E-W) systems collect or link data on 25 disaggregates such as race and ethnicity, age group, disability status, and parental education level.
Why Disaggregate Data?
It is important to disaggregate data by both individual and system characteristics to identify, expose, and act on the structural inequities that cause different outcomes across groups. For example, disaggregating data by K-12 school type (such as whether a school is a public, charter, or private school) can help illuminate the extent to which different types of schools are serving students well. See the framework’s data equity principles for additional guidance on data disaggregation to support more equitable outcomes, including suggestions on how to apply disaggregation throughout the data cycle, reflection questions and potential pitfalls for data users to consider (Data Equity Principle 3).
Some disaggregates will be more or less relevant in different contexts. For example, although all pre-K-to-workforce sectors should disaggregate data by background characteristics such as race and ethnicity and income level, postsecondary systems should also consider disaggregating data by factors specific to the postsecondary sector, such as students’ enrollment intensity (that is, whether they attend part-time or full-time) and field of study.
The E-W Framework provides detailed information about each of the 25 recommended disaggregates, including a definition, relevant sector(s), an explanation of why it matters, recommended metrics, and possible data sources.