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
Part of the Higher Education: Handbook of Theory and Research book series (HATR, volume 34)


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.


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 


  1. Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353.CrossRefGoogle Scholar
  2. Bell, M. G., & Iida, Y. (1997). Transportation network analysis. New York: Wiley Online Library.CrossRefGoogle Scholar
  3. Bivand, R. S., Pebesma, E. J., Gomez-Rubio, V., & Pebesma, E. J. (2013). Applied spatial data analysis with r (Vol. 747248717). New York: Springer.CrossRefGoogle Scholar
  4. Bogdan, R., & Biklen, S. (2007). Qualitative research for education: An introduction to theory and practice. Needham Heights, MA: Allyn and Bacon.Google Scholar
  5. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.CrossRefGoogle Scholar
  6. Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational & Mathematical Organization Theory, 12(1), 21–34.CrossRefGoogle Scholar
  7. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895.CrossRefGoogle Scholar
  8. Borgatti, S. P., & Molina, J.-L. (2005). Toward ethical guidelines for network research in organizations. Social Networks, 27(2), 107–117.CrossRefGoogle Scholar
  9. Breiger, R. L. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190.CrossRefGoogle Scholar
  10. Charmaz, K. (2011). Grounded theory methods in social justice research. The Sage Handbook of Qualitative Research, 4(1), 359–380.Google Scholar
  11. Cliff, A., & Ord, K. (1969). The problem of spatial autocorrelation. In A. J. Scott (Ed.), London papers in regional science (pp. 25–55). London: Pion.Google Scholar
  12. Cliff, A., & Ord, K. (1972). Testing for spatial autocorrelation among regression residuals. Geographical Analysis, 4(3), 267–284.CrossRefGoogle Scholar
  13. Cressie, N. A. (2015). Statistics for spatial data. New York: Wiley Online Library.Google Scholar
  14. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Los Angeles: Sage Publications.Google Scholar
  15. Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.Google Scholar
  16. Curtis, G. E., & Karacan, T. (2002, December). The nexus among terrorists, narcotics traffickers, weapons proliferators, and organized crime networks in western Europe. Washington, DC: The Library of Congress.Google Scholar
  17. Elijah, A. (1990). Streetwise: Race, class, and change in an urban community. Chicago: University of Chicago.Google Scholar
  18. ESRI. (1998). Shapefile technical description, Jul. 1998. Environmental Systems Research Institute [ESRI], Inc. Retrieved from
  19. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174.CrossRefGoogle Scholar
  20. Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  21. González Canché, M. S. (2014). Localized competition in the non-resident student market. Economics of Education Review, 43, 21–35.CrossRefGoogle Scholar
  22. González Canché, M. S. (2017a). Community college scientists and salary gap: Navigating socioeconomic and academic stratification in the U.S. higher education system. The Journal of Higher Education, 88(1), 1–32. Retrieved from,
  23. González Canché, M. S. (2017b). Financial benefits of rapid student loan repayment: An analytic framework employing two decades of data. The Annals of the American Academy of Political and Social Science, 671(1), 154–182.CrossRefGoogle Scholar
  24. González Canché, M. S. (2017c). The heterogeneous non-resident student body: Measuring the effect of out-of-state students’ home-state wealth on tuition and fee price variations. Research in Higher Education, 58(2), 141–183.CrossRefGoogle Scholar
  25. González Canché, M. S. (2017d). Measuring universities’ spillover effects on community college students’ educational outcomes. In Association for the study of higher education, 42nd annual conference. Houston, TX.Google Scholar
  26. González Canché, M. S. (2018a). Geographical network analysis and spatial econometrics as tools to enhance our understanding of student migration patterns and benefits in the U.S. higher education network. The Review of Higher Education, 41(2), 169–216.CrossRefGoogle Scholar
  27. González Canché, M. S. (2018b). Nearby college enrollment and geographical skills mismatch: (re)conceptualizing student out-migration in the American higher education system. The Journal of Higher Education, 1–43.Google Scholar
  28. González Canché, M. S. (2018c). Reassessing the two-year sector’s role in the amelioration of a persistent socioeconomic gap: A proposed analytical framework for the study of community college effects in the big and geocoded data and quasi-experimental era. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research: Published under the sponsorship of the association for institutional research (air) and the association for the study of higher education (ASHE) (pp. 175–238). Cham, Switzerland: Springer International Publishing. Retrieved from;
  29. González Canché, M. S. (2018d). Geographical Bias in testing: Is cultural Bias a problem of the past or are we simply not looking in the right space? Diverse Issues in Higher Education. Available from
  30. González Canché, M. S. (2019a). Challenges and opportunities in the use of big and geocoded data in higher education research and policy. In G. M. & A. Castro-Samoya (Eds.), Contemporary issues in higher education (Ist edn., Chapter 3). New York: Routledge. Retreived from
  31. González Canché, M. S. (2019b). Spatial econometrics and network analysis as means to assess the assumption of independence in higher education research: Interrogating social dependence using spatial econometrics. New Directions for Institutional Research, 179, 11–29.Google Scholar
  32. González Canché, M. S. (2019c). The statistical power of “zooming” in: Applying geographically-based difference in differences using Spatio-temporal Analysis to the study of college aid and access. New Directions for Institutional Research, 179, 71–89.Google Scholar
  33. González Canché, M. S., D’Amico, M. M., Rios-Aguilar, C., & Salas, S. (2014). It’s who you know: Leveraging social networks for college and careers. The Community College Enterprise, 20(1), 17.Google Scholar
  34. González Canché, M. S., & Rios-Aguilar, C. (2015). Critical social network analysis in community colleges: Peer effects and credit attainment. New Directions for Institutional Research, 2014(163), 75–91.CrossRefGoogle Scholar
  35. Griffith, D. A. (1993). Advanced spatial statistics for analyzing and visualizing geo-referenced data. International Journal of Geographical Information Science, 7(2), 107–123.CrossRefGoogle Scholar
  36. Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of statistical software, 24(3), nihpa54860.Google Scholar
  37. Jargowsky, P. A., & Tursi, N. O. (2015). Concentrated disadvantage. In J. D. Wright (Ed.), International encyclopedia of the social and behavioral sciences (2nd Ed., pp. 525–530). Oxford, UK: Elsevier. Retrieved from
  38. Kadushin, C. (2005). Who benefits from network analysis: ethics of social network research. Social Networks, 27(2), 139–153.CrossRefGoogle Scholar
  39. Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York: Springer.Google Scholar
  40. Lazega, E. (2001). The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford, UK: Oxford University Press on Demand.CrossRefGoogle Scholar
  41. Lewis, D. (1973). Causation. The Journal of Philosophy, 70, 556–567.CrossRefGoogle Scholar
  42. Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health Promotion Practice, 16(4), 473–475.CrossRefGoogle Scholar
  43. Liu, B. (2011). Social network analysis. In Web data mining (pp. 269–309). Springer.Google Scholar
  44. Mall, R., Cerulo, L., Bensmail, H., Iavarone, A., & Ceccarelli, M. (2017). Detection of statistically significant network changes in complex biological networks. BMC Systems Biology, 11(1), 32.CrossRefGoogle Scholar
  45. Mastrobuoni, G., & Patacchini, E. (2012). Organized crime networks: An application of network analysis techniques to the American mafia. Review of Network Economics, 11(3).Google Scholar
  46. McMillen, D., Singell Jr., L., & Waddell, G. (2007). Spatial competition and the price of college. Economic Inquiry, 45(4), 817–833.CrossRefGoogle Scholar
  47. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems. An introduction to computational models of social life. Princeton, NJ: Princeton University Press.Google Scholar
  48. Mills, C. W. (2000). The sociological imagination. New York: Oxford University Press.Google Scholar
  49. Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human interrelations. Philadelphia, PA: Nervous and Mental Disease Publishing Co.CrossRefGoogle Scholar
  50. Pacione, M. (1997). The geography of educational disadvantage in Glasgow. Applied Geography, 17(3), 169–192.CrossRefGoogle Scholar
  51. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web.Google Scholar
  52. Pastor, M. J. (2001). Geography and opportunity. In N. J. Smelser, W. J. Wilson, & M. Faith (Eds.), America becoming: Racial trends and their consequences (Vol. 1, pp. 435–468). Washington, D.C: National Academies Press.Google Scholar
  53. Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., et al. (2011). Using graph theory to analyze biological networks. BioData mining, 4(1), 10.Google Scholar
  54. Phipson, B., & Smyth, G. K. (2010). Permutation p-values should never be zero: Calculating exact p-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, 9, 1.Google Scholar
  55. Rapino, M. A., & Fields, A. K.. 2013. Mega Commuters in the U.S.: Time and Distance in Defining the Long Commute Using the American Community Survey. Working Paper 2013–03, United States Census Bureau, Atlanta, GA.Google Scholar
  56. Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, (469), 100.Google Scholar
  57. Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. CRC press.Google Scholar
  58. Tate IV, W. F. (2008). Geography of opportunity: Poverty, place, and educational outcomes. Educational Researcher, 37(7), 397–411.Google Scholar
  59. Turley, R. N. L. (2009). College proximity: Mapping access to opportunity. Sociology of Education, 82(2), 126–146.Google Scholar
  60. USDAERS. (1993). Codes, rural-urban continuum. US Department of Agriculture. Retrieved from
  61. USDAERS. (2003). Codes, rural-urban continuum. US Department of Agriculture. Retrieved from
  62. USDAERS. (2013). Codes, rural-urban continuum. US Department of Agriculture. Retrieved from
  63. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.Google Scholar
  64. Webber, K. L., & González Canché, M. S. (2015). Not equal for all: Gender and race differences in salary for doctoral degree recipients. Research in Higher Education, 56(7), 645–672.Google Scholar
  65. Whitbred, R. (2011). Quadratic assignment procedure (qap). In G. A. Barnett (Ed.), Encyclopedia of social networks (Vol. 1, pp. 733–734). Thousand Oaks, CA: SAGE Publications, Inc.Google Scholar
  66. Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Higher Education DivisionUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations