The separation of an aggregate body into its component parts.
This does not usually refer to “undoing” the process of aggregating two or more indicators together into an index. Rather, it refers to breaking down a single indicator into subgroups of geographic or demographic variables. For example, instead of simply stating that 15% of people in your city live in poverty, you might break down the population by age, ethnicity or neighborhood of residence.
The process of breaking data into smaller subsets in order to more closely analyze student performance. Disaggregation is an analysis tool that enables a school district to determine whether there is equity on outcomes measures: whether different groups of students are performing similarly on the outcomes.
Separated into component parts; breakouts of information-often demographic, geographic, or by the component of an organization.
These are PSSA scaled scores and proficiency levels for the following groups of students: Title 1, gender, race/ethnicity, IEP, Limited English Proficient Students, Migrant Students and Economically Disadvantaged Students
Loss of aggregate in a stone usually as a result of dissolution of binder material.
Looking at data by subgroups to determine how the groups performed on the test is known as disaggregating the data. These subgroups may be various populations, e.g. minority students (racial/ethnic groups) or a group of students who are new to the school. The purpose disaggregation is to discover if there is learning for all students and to find areas for focus or intervention.
This is the process of dividing up the trade area into component geographic units. The most common units are ZIP Codes, census tracts, block groups, and grids. This technique allows the analyst to more precisely examine the influence of individual variables on sales performance. Spatial relationships such as distance, access and bias orientation can be more effectively assessed in disaggregated datasets. This process also has the statistical advantage of adding more observations to the dataset, allowing for greater accuracy.