Take Care When Cutting
Five Approaches to Disaggregating School Data as Rural and Remote
DOI:
https://doi.org/10.3776/tpre.2020.v10n2p63-84Keywords:
rural schools, remoteness, place-based research methods, educational equityAbstract
Education research that omits or insufficiently defines geographic locale can impair policy formulation, enactment, and evaluation. Such impairments might be especially detrimental for communities in rural and/or remote areas, particularly when they pertain to gifted education programs that struggle to operate at large scale (e.g., Advanced Placement). To enhance researchers’ precision when analyzing school-level data, we developed five statistical approaches to operationalize rurality and remoteness using the Urban-Centric codes from the National Center of Education Statistics. With national data, we found important variations across these statistical approaches in (a) percentage of schools identified as rural and/or remote, (b) effect sizes, and (c) characterizations of schools’ relative disadvantage in the breadth of opportunity to learn Advanced Placement content that they provide. These findings challenge prevailing practices of classifying communities dichotomously as nonrural or rural. The authors demonstrate several ways to address policy makers’ and practitioners’ needs by incorporating geographic locale into analyses of school data, operationalizing geographic locale precisely in theoretically sound ways, and avoiding dichotomies that can obscure meaningful variation.
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