Siete vo vzdelávaní: možnosti využitia analýzy sociálnych sietí v pedagogickom výskume

Tomáš Lintner


With its wide range of applications, social network analysis has found its place in a number of scientific fields. In educational research, social network analysis has the potential to uncover and investigate yet unknown configurations of relationships among actors in education. This paper provides an introduction to the issues, techniques, and applications of social network analysis in educational research. It first surveys the basic terminolog y and concepts in social network analysis. Using the example of a small network, it demonstrates basic network calculations at the level of both the individual actors and the network as a whole. Furthermore, the paper provides a brief overview of studies in the field of educational research that have employed social network analysis. Using the example of a fictional classroom and five research questions, the main part of the paper demonstrates the application of social network analysis in educational research ranging from crosssectional descriptive analysis to dynamic inferential analysis. Step by step, it introduces a range of methods and interprets their results. In addition to centrality, clustering, and connectedness measures, the example contains permutation tests used for significance testing with network data, exponential random graph models (ERGM), and separable temporal exponential graph models (STERGM). Finally, the paper discusses challenges related to the application of social network analysis.

Klíčová slova

SNA; analýza sociálnych sie; komplexné siete; metodológia v pedagogickom výskume; modely sociálnych sietí; ERGM

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Abbe, E. (2017). Community detection and stochastic block models: recent developments. The Journal of Machine Learning Research, 18(1), 6446–6531. | DOI 10.5555/3122009.3242034

An, W. (2015). Multilevel meta network analysis with application to studying network dynamics of network interventions. Social Networks, 43, 48–56. | DOI 10.1016/j.socnet.2015.03.006

Anderson, A., Locke, J., Kretzmann, M., Kasari, C., & AIR-B Network. (2016). Social network analysis of children with autism spectrum disorder: predictors of fragmentation and connectivity in elementary school classrooms. Autism, 20(6), 700–709. | DOI 10.1177/1362361315603568

Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social networks, 21(1), 37–66. | DOI 10.1016/S0378-8733(98)00012-4

Bakkenes, I., De Brabander, C., & Imants, J. (1999). Teacher Isolation and Communication Network Analysis in Primary Schools. Educational Administration Quarterly, 35(2), 166–202. | DOI 10.1177/00131619921968518

Barclay, J. R. (1967). Effecting behavior change in the elementary classroom: An exploratory study. Journal of Counseling Psychology, 14(3), 240–247. | DOI 10.1037/h0024541

Baron, D. (1951). Personal-social characteristics and classroom social status: A sociometric study of fifth and sixth grade girls. Sociometry, 14(1), 32–42. | DOI 10.2307/2785208

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.

Batagelj, V., & Mrvar, A. (1998). Pajek – Program for Large Network Analysis. Connections, 21(2), 47–57. | DOI 10.1007/978-3-642-18638-7_4

Beebee, H., Hitchcock, C., & Menzies, P. (Eds.). (2009). The Oxford Handbook of Causation. Oxford University Press. | DOI 10.1093/oxfordhb/9780199279739.001.0001

Berry, K. J., Johnston, J. E., & Mielke, J. P. W. (2019). A Primer of Permutation Statistical Methods. Springer International Publishing. | DOI 10.1007/978-3-030-20933-9

Bokhove, C. (2018). Exploring classroom interaction with dynamic social network analysis. International Journal of Research & Method in Education, 41(1), 17–37. | DOI 10.1080/1743727X.2016.1192116

Bonacich, P. (1987). Power and Centrality: A Family of Measures. American Journal of Sociology, 92(5), 1170–1182. | DOI 10.1086/228631

Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. | DOI 10.1016/j.socnet.2007.04.002

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta-analysis. John Wiley & Sons. | DOI 10.1002/9780470743386

Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. | DOI 10.1016/j.socnet.2004.11.008

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for Social Network Analysis. Analytic Technologies.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. SAGE.

Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. | DOI 10.1126/science.1165821

Breuer, R., Klamma, R., Cao, Y., & Vuorikari, R. (2009, September). Social network analysis of 45,000 schools: A case study of technology enhanced learning in Europe. In European Conference on Technology Enhanced Learning (s. 166–180). Springer.

Burk, W. J., Steglich, C. E., & Snijders, T. A. (2007). Beyond dyadic interdependence: Actor-oriented models for co-evolving social networks and individual behaviors. International journal of behavioral development, 31(4), 397–404. | DOI 10.1177/0165025407077762

Butts, C. T. (2007). 8. Permutation Models for Relational Data. Sociological Methodology, 37(1), 257–281. | DOI 10.1111/j.1467-9531.2007.00183.x

Butts, C. T. (2008). Social network analysis with sna. Journal of statistical software, 24(6), 1–51. | DOI 10.18637/jss.v024.i06

Butts, C. T., & Butts, M. C. T. (2019). Package 'sna'.

Carrington, P. J., Scott, J., & Wasserman, S. (2009). Models and methods in social network analysis. Cambridge University Press.

Cerezo, F., & Ato, M. (2005). Bullying in Spanish and English pupils: A sociometric perspective using the BULL-S questionnaire. Educational psychology, 25(4), 353–367. | DOI 10.1080/01443410500041458

Chen, J., Lin, T. J., Justice, L., & Sawyer, B. (2019). The social networks of children with and without disabilities in early childhood special education classrooms. Journal of autism and developmental disorders, 1–16. | DOI 10.1007/s10803-017-3272-4

Cherven, K. (2013). Network graph analysis and visualization with Gephi: visualize and analyze your data swiftly using dynamic network graphs built with Gephi. Packt Publishing.

Cherven, K. (2015). Mastering Gephi network visualization. Packt Publishing Ltd.

Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, complex systems, 1695(5), 1–9.

Csárdi, G., & Nepusz, T. (2010). igraph Reference manual. http://igraph. sourceforge. net/docu-mentation.html

Cunningham, D., Everton, S., & Murphy, P. (2016). Understanding dark networks: A strategic framework for the use of social network analysis. Rowman & Littlefield.

Daldal, A. (2014). Power and ideology in Michel Foucault and Antonio Gramsci: A compa-rative analysis. Review of History and Political Science, 2(2), 149–167.

Diviák, T. (2017). Ekvivalence a blokové modelování v analýze sociálních sítí. Naše společnost (Our Society), 15(1), 27–40. | DOI 10.13060/1214438X.2017.1.15.366

Edwards, G. (2010). Mixed-method approaches to social network analysis. National Centre for Research Methods.

Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3–5), 75–174. | DOI 10.1016/j.physrep.2009.11.002

Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the american Statistical association, 81(395), 832–842. | DOI 10.1080/01621459.1986.10478342

Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35–41. | DOI 10.2307/3033543

Freeman, L. C. (2004). The development of social network analysis. A Study in the Sociology of Science. Empirical Press.

Friedkin, N. E. (1991). Theoretical Foundations for Centrality Measures. American Journal of Sociology, 96(6), 1478–1504. | DOI 10.1086/229694 (2017). Learn how to use Gephi.

Goodreau, S. M., Handcock, M. S., Hunter, D. R., Butts, C. T., & Morris, M. (2008). A statnet Tutorial. Journal of statistical software, 24(9), 1–26. | DOI 10.18637/jss.v024.i09

Goodreau, S. M., Kitts, J. A., & Morris, M. (2009). Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography, 46(1), 103–125. | DOI 10.1353/dem.0.0045

Grimes, D. A., & Schulz, K. F. (2008). Making sense of odds and odds ratios. Obstetrics & Gynecology, 111(2), 423–426. | DOI 10.1097/01.AOG.0000297304.32187.5d

Grund, T. U., & Densley, J. A. (2015). Ethnic homophily and triad closure: Mapping internal gang structure using exponential random graph models. Journal of Contemporary Criminal Justice, 31(3), 354–370. 10.1177/1043986214553377 | DOI 10.1177/1043986214553377

Han, G., McCubbins, O. P., & Paulsen, T. H. (2016). Using Social Network Analysis to Measure Student Collaboration in an Undergraduate Capstone Course. NACTA Journal, 60(2), 176–182.

Handcock, M., Hunter, D., Butts, C., Goodreau, S., Krivitsky, P., & Morris, M. (2018). ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks. The Statnet Project ( R package version 3.9.4,

Harris, J. K. (2013). An introduction to exponential random graph modeling (Vol. 173). Sage Publications.

Heath, A. C., Kessler, R. C., Neale, M. C., Hewitt, J. K., Eaves, L. J., & Kendler, K. S. (1993). Testing hypotheses about direction of causation using cross-sectional family data. Behavior Genetics, 23(1), 29–50. | DOI 10.1007/BF01067552

Hervé, M. (2020). Package 'RVAideMemoire'.

Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460), 1090–1098. | DOI 10.1198/016214502388618906

Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social networks, 5(2), 109–137. | DOI 10.1016/0378-8733(83)90021-7

Huisman, M. (2009). Imputation of missing network data: Some simple procedures. Journal of Social Structure, 10(1), 1–29. | DOI 10.1007/978-1-4614-7163-9_394-1

Huitsing, G., & Veenstra, R. (2012). Bullying in classrooms: Participant roles from a social network perspective. Aggressive behavior, 38(6), 494–509. | DOI 10.1002/ab.21438

Hunter, D. R., & Handcock, M. S. (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15(3), 565–583. | DOI 10.1198/106186006X133069

Hunter, D., Handcock, M., Butts, C., Goodreau, S., & Morris, M. (2008). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3), 1–29. | DOI 10.18637/jss.v024.i03

Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one, 9(6). | DOI 10.1371/journal.pone.0098679

Jiao, C., Wang, T., Liu, J., Wu, H., Cui, F., & Peng, X. (2017). Using Exponential Random Graph Models to analyze the character of peer relationship networks and their effects on the subjective well-being of adolescents. Frontiers in psychology, 8, 583. | DOI 10.3389/fpsyg.2017.00583

Jimoyiannis, A., Tsiotakis, P., & Roussinos, D. (2013). Social network analysis of students' participation and presence in a community of educational blogging. Interactive Technology and Smart Education, 10(1), 15–30. | DOI 10.1108/17415651311326428

Juhaňák, L. (2017). Sociální sítě autorů publikujících v pedagogických vědách v letech 2009–2013: Exploratorní analýza. Studia paedagogica, 22(1), 9–36. | DOI 10.5817/SP2017-1-2

Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. OUP USA.

Kalkusová, L. (2017). Adaptační kurz jako nástroj změny sociálních vztahů ve třídním kolektivu. Studia sportiva, 11(1), 128–134. | DOI 10.5817/StS2017-1-30

Kindermann, T. A. (2007). Effects of Naturally Existing Peer Groups on Changes in Academic Engagement in a Cohort of Sixth Graders. Child Development, 78(4), 1186–1203. | DOI 10.1111/j.1467-8624.2007.01060.x

Kim, B., Lee, K. H., Xue, L., & Niu, X. (2018). A review of dynamic network models with latent variables. Statistics surveys, 12, 105–135. | DOI 10.1214/18-SS121

Kim, J. (2015). How to choose the level of significance: A pedagogical note.

Kolleck, N. (2015). Uncovering influence through Social Network Analysis: the role of schools in Education for Sustainable Development. Journal of Education Policy, 31(3), 308–329. | DOI 10.1080/02680939.2015.1119315

Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28, 247–268. | DOI 10.1016/j.socnet.2005.07.002

Krivitsky, P. N., & Goodreau, S. M. (2019). STERGM-Separable Temporal ERGMs for modeling discrete relational dynamics with statnet.­gnettes/STERGM.pdf

Krivitsky, P. N., & Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 29–46. | DOI 10.1111/rssb.12014

Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic journal of statistics, 6, 1100–1128. | DOI 10.1214/12-EJS696

Knoke, D., & Yang, S. (2020). Social network analysis. SAGE.

Landherr, A., Friedl, B., & Heidemann, J. (2010). A Critical Review of Centrality Measures in Social Networks. Business & Information Systems Engineering, 2 (6), 371–385. | DOI 10.1007/s12599-010-0127-3

Leung, B. P., & Silberling, J. (2006). Using sociograms to identify social status in the classroom. The California School Psychologist, 11(1), 57–61. | DOI 10.1007/BF03341115

Levy (2016). gwdegree: Improving interpretation of geometrically-weighted degree estimates in exponential random graph models. Journal of Open Source Software, 1(3), 36, | DOI 10.21105/joss.00036

Li, Y., & Carriere, K. C. (2013). Assessing goodness of fit of exponential random graph models. International Journal of Statistics and Probability, 2(4), 64. | DOI 10.5539/ijsp.v2n4p64

Lin, N. (2017). Building a Network Theory of Social Capital. Social Capital, 3–28. | DOI 10.4324/9781315129457-1

Lubbers, M. J. (2003). Group composition and network structure in school classes: a multilevel application of the p- model. Social Networks, 25(4), 309–332. | DOI 10.1016/S0378-8733(03)00013-3

Lubbers, M. J., & Snijders, T. A. (2007). A comparison of various approaches to the exponential random graph model: A reanalysis of 102 student networks in school classes. Social networks, 29(4), 489–507. | DOI 10.1016/j.socnet.2007.03.002

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Structural Analysis in the Social Sciences). Cambridge University Press. | DOI 10.1017/CBO9780511894701

Martınez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & De La Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers & Education, 41(4), 353–368. | DOI 10.1016/j.compedu.2003.06.001

Meijs, C., & De Laat, M. (2012). Social Network Analyses (SNA) as a method to study the structure of contacts within teams of a school for secondary education. In V. Hodgson, C. Jones, M. de Laat, D. McConnell, T. Ryberg, & P. Sloep (Eds.), Proceedings of the 8th International Learning Conference on Networked Learning.

Moreno, J. L. (1934). Who shall survive?: A new approach to the problem of human interrelations. Nervous and Mental Disease Publishing Co. | DOI 10.1037/10648-000

Morris, M., Handcock, M. S., & Hunter, D. R. (2008). Specification of exponential-family random graph models: terms and computational aspects. Journal of statistical software, 24(4), 1548–7660. | DOI 10.18637/jss.v024.i04

Morris, M., Krivitsky, P. N., Handcock, M. S., Butts, C. T., Hunter, D. R., Goodreau, S. M., & Bender de-Moll, S. (2019). Temporal Exponential Random Graph Models (TERGMs) for dynamic network modeling in statnet.

Mrvar, A., & Batagelj, V. (2019). Programs for analysis and visualization of very large networks: Reference manual.

Munoz, D. A., Queupil, J. P., & Fraser, P. (2016). Assessing collaboration networks in educational research. International Journal of Educational Management, 30(3), 416–36. | DOI 10.1108/IJEM-11-2014-0154

Murphy, P. (2020). Phil Murphy Tutorials.

Nagy, T., Nagyová, S., & Szárazová, B. (2018). Sociometria v pedagogickom výskume. Biológia, Ekológia, Chémia, 22(4), 4–11.

Naim, K., Yuldashev, F., Demiroz, F., & Arslan, T. (2010). Social network analysis (SNA) applications in evaluating MPA classes. Journal of Public Affairs Education, 16(4), 541–564. | DOI 10.1080/15236803.2010.12001614

Parkhurst, J. T., & Hopmeyer, A. (1998). Sociometric popularity and peer-perceived popularity: Two distinct dimensions of peer status. The Journal of Early Adolescence, 18(2), 125–144. | DOI 10.1177/0272431698018002001

Peery, J. C. (1979). Popular, amiable, isolated, rejected: A reconceptualization of socio-metric status in preschool children. Child Development, 50(4), 1231–1234. | DOI 10.2307/1129356

Quardokus, K., & Henderson, C. (2015). Promoting instructional change: using social network analysis to understand the informal structure of academic departments. Higher Education, 70(3), 315–335. | DOI 10.1007/s10734-014-9831-0

Radford, M. (2008). Complexity and truth in educational research. Educational Philosophy and Theory, 40(1), 144–157. | DOI 10.1111/j.1469-5812.2007.00396.x

Ripley, R. M., Snijders, T. A., Boda, Z., Vörös, A., & Preciado, P. (2020). Manual for SIENA version 4.0. University of Oxford.

Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173–191. | DOI 10.1016/j.socnet.2006.08.002

Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603. | DOI 10.1007/BF02289527

Sarkar, P., & Moore, A. W. (2006). Dynamic social network analysis using latent space models. In Advances in Neural Information Processing Systems (s. 1145–1152). | DOI 10.1145/1117454.1117459

Schofield, J. W. & Whitley, B. E. (1983). Peer Nomination vs. Rating Scale Measurement of Children's Peer Preferences. Social Psychology Quarterly, 46(3), 242–251. | DOI 10.2307/3033795

Scott, J. (2012). What is social network analysis? Bloomsbury Academic.

Scott, J. (2017). Social network analysis (Fourth Edition). SAGE.

Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646–1657. | DOI 10.1080/01621459.2014.988214

Shibutani, T. (2000). Social processes: an introduction to sociology.

Snijders, T. A. (1996). Stochastic actor- oriented models for network change. Journal of mathe-matical sociology, 21(1–2), 149–172. | DOI 10.1080/0022250X.1996.9990178

Snijders, T. A., & Baerveldt, C. (2003). A multilevel network study of the effects of delinquent behavior on friendship evolution. Journal of mathematical sociology, 27(2–3), 123–151. | DOI 10.1080/00222500305892

Snijders, T. A., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006). New specifications for exponential random graph models. Sociological methodology, 36(1), 99–153. | DOI 10.1111/j.1467-9531.2006.00176.x

Snijders, T. A., Van de Bunt, G. G., & Steglich, C. E. (2010). Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), 44–60. | DOI 10.1016/j.socnet.2009.02.004

Steglich, C., Snijders, T. A. B., & Pearson, M. (2010). 8. Dynamic Networks and Behavior: Separating Selection from Influence. Sociological Methodology, 40(1), 329–393. | DOI 10.1111/j.1467-9531.2010.01225.x

Stepanyan, K., Borau, K., & Ullrich, C. (2010). A social network analysis perspective on student interaction within the twitter microblogging environment. In 2010 10th IEEE international conference on advanced learning technologies (s. 70–72). IEEE.

Sweet, T. M., Thomas, A. C., & Junker, B. W. (2013). Hierarchical network models for education research: Hierarchical latent space models. Journal of Educational and Behavioral Statistics, 38(3), 295–318. | DOI 10.3102/1076998612458702

Šalamounová, Z., & Fučík, P. (2019). The relationship between peer status and students' participation in classroom discourse. Educational Studies. | DOI 10.1080/03055698.2019.1706042

The Statnet Development Team (2019). An Example Analysis Using LOLOG.

Titmanová, M. (2019). Klima ve školní třídě aneb šikana v praxi. Školský psychológ/Školní psycholog, 20(1), 67–76.

UCINET. (2020). UCINET Software.

Valente, T. W., Coronges, K., Lakon, C., & Costenbader, E. (2008). How Correlated Are Network Centrality Measures? Connections, 28(1), 16–26.

Van Der Pol, J. (2017). Introduction to network modeling using Exponential Random Graph models (ERGM).

Vítová, J., Balcarová, J., & Linhartová, V. (2013). The social position of pupils with special educational needs in the group intact peers. Paidagogos – Journal of Education in Contexts, 2013(2), 451–464.

Wang, P., Robins, G., & Pattison, P. (2009). PNet: program for the simulation and estimation of exponential random graph models. Melbourne School of Psychological Sciences, The University of Melbourne.

Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35(1), 96–115. | DOI 10.1016/j.socnet.2013.01.004

Wang, Y. J., & Wong, G. Y. (1987). Stochastic blockmodels for directed graphs. Journal of the American Statistical Association, 82(397), 8–19. | DOI 10.1080/01621459.1987.10478385

Wasserman, S., & Faust, K. (2019). Social network analysis: methods and applications. Cambridge University Press.

Wasserman, S., & Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p. Psychometrika, 61(3), 401–425. | DOI 10.1007/BF02294547

White, H. C. (2008). Identity and control: How social formations emerge. Princeton University Press.

Williams, B. T., & Gilmour, J. D. (1994). Annotation: Sociometry and peer elationships. Journal of Child Psychology and Psychiatry, 35(6), 997–1013. | DOI 10.1111/j.1469-7610.1994.tb01806.x


Časopis Ústavu pedagogických věd FF MU.

Výkonná redakce: Klára Šeďová, Roman Švaříček, Zuzana Šalamounová, Martin Sedláček, Karla Brücknerová, Petr Hlaďo.

Redakční rada: Milan Pol (předseda redakční rady), Gunnar Berg, Michael Bottery, Hana Cervinkova, Theo van Dellen, Eve Eisenschmidt, Peter Gavora, Yin Cheong Cheng, Miloš Kučera, Adam Lefstein, Sami Lehesvuori, Jan Mareš, Jiří Mareš, Jiří Němec, Angelika Paseka, Jana Poláchová Vašťatková, Milada Rabušicová, Alina Reznitskaya, Michael Schratz, Martin Strouhal, Petr Svojanovský, António Teodoro, Tony Townsend, Anita Trnavčevič, Jan Vanhoof, Arnošt Veselý, Kateřina Vlčková, Eliška Walterová.

Časopis vydává čtyři čísla ročně.

ISSN 1803-7437 (print), ISSN 2336-4521 (online)