Methodology challenges and cutting edge designs for rural education research

James A. Bovaird, Kirstie L. Bash

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The unique contexts and features of rural education systems lead to the need for unique and innovative solutions. In particular, rural research is commonly perceived to face major logistical research hurdles such as small populations, low densities, poor access, and geographic isolation. These limitations make the rural setting a challenging context within which to conduct education research. This chapter presents considerations for overcoming such challenges while still striving towards employing rigorous methodologies, achieving desired generalizability, and reaching causal inferences when relevant. To accomplish this, a number of interdisciplinary statistical and design-based solutions can be translated to rural education research. In particular, this chapter discusses: (a) using advanced statistical modeling to preserve and feature the uniqueness of rural settings, (b) alternatives to traditional simple random assignment, (c) measurement paradigms to reduce the amount of data required, and (d) innovations for working with small samples and complex models. Most of these topics and approaches can be combined to accommodate the complexities and realities of conducting rural research. The fundamental message is that all research contexts present their own unique challenges, but as researchers, we can look outside of our disciplines to find solutions that can help us pursue our necessary research agendas.

Original languageEnglish (US)
Title of host publicationRural Education Research in the United States
Subtitle of host publicationState of the Science and Emerging Directions
PublisherSpringer International Publishing
Pages95-119
Number of pages25
ISBN (Electronic)9783319429403
ISBN (Print)9783319429380
DOIs
StatePublished - Jan 1 2016

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Keywords

  • Causal inference
  • Cluster randomized trials
  • Ecological systems
  • Finite samples
  • Multilevel modeling
  • Planned missing data
  • Quasi-experimental design
  • Small sample inference

ASJC Scopus subject areas

  • Social Sciences(all)

Cite this

Bovaird, J. A., & Bash, K. L. (2016). Methodology challenges and cutting edge designs for rural education research. In Rural Education Research in the United States: State of the Science and Emerging Directions (pp. 95-119). Springer International Publishing. https://doi.org/10.1007/978-3-319-42940-3_6