Erasing rurality: On the need to disaggregate statistical data

This chapter examines statistical categorisations used to determine rurality in pub-lic policy regarding education and the impact these categorisations have on de-scribing and measuring data that depicts educational achievement and access. Drawing on models used in Australia and the USA, the chapter illustrates how different levels of inclusion of statistical areas in what is deemed ‘rural’ in data analysis can produce significantly different results. The chapter shows the im-portance of disaggregating data categories to gain the most precise picture of edu-cational achievement and access. The approaches presented in this chapter suggest ways to overcome the significant problem of grouping communities and categories in ways that can generate misleading comparisons and conclusions.