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Geographical, Statistical, and Qualitative Network Analysis: A Multifaceted Method-Bridging Tool to Reveal and Model Meaningful Structures in Education Research

  • Manuel S. González CanchéEmail author
Chapter
Part of the Higher Education: Handbook of Theory and Research book series (HATR, volume 34)

Abstract

A network consists of a group of units where sets of connections may be observed. When these connections are recorded in social settings, a network is depicted as a social network. Nonetheless, a network is realized whenever researchers can offer feasible definitions of rules and boundaries for when these connections among units may be noted. This latter property enables networks to not be constrained to social settings, but rather to take place in an almost infinite set of contexts. The overarching goal of this chapter is to showcase some of these contexts and leverage the power of network analysis to unveil and model meaningful structures in (higher) education research. Accordingly, with the goal to strengthen the study of higher education, this chapter depicts the manners in which network principles are merged with geographical, statistical, and qualitative approaches to test innovative, timely, and relevant hypotheses. Examples of these tools are the analysis of dependence among units, wherein connections can be established considering measures of geographical and/or social proximity. The method-bridging properties resulting from network principles are also used to reveal the mathematical and dynamic structure contained in qualitative data. All the analyses can be freely replicated using the R code and data provided herein – some analyses rely on protected data, accordingly those data and its corresponding code are not included in this chapter but the code is available upon request.

Keywords

Big data Georeferenced data Geographical network analysis Geographical information systems Spatial analysis Data visualization Quantitative tools for the analysis of qualitative data Network analysis of qualitative data Quadratic assignment procedures Random permutation tests Inferences using qualitative data Data management Matrix manipulation Critical research and theory Text mining Peer effects Literature network analysis or biblionetwork analysis 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Higher Education DivisionUniversity of PennsylvaniaPhiladelphiaUSA

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