In order to find out who is excluded in education, data disaggregated by variables associated with exclusion are helpful. These are data that are broken down according to units of social categorization that tends to define contours of inclusion/exclusion in education, such as categories based on differences in gender, wealth, ability, ethnic origin, language, social origin, parental status, place of residence, etc., in other words, markers of disparities in educational opportunities. Markers of inclusion/exclusion rarely operate in isolation. They intersect with each other to have compounded effects on inclusion/ exclusion. Therefore, it is important to disaggregate data not only by a single variable but also across multiple variables. Disaggregating data by multiple variables will help gain a finer insight into understanding the processes through which exclusion occurs.
