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

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

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

[3] 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. https://doi.org/10.1177/1362361315603568 | DOI 10.1177/1362361315603568

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

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

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

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

[8] 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. https://gephi.org/publications/gephi-bastian-feb09.pdf

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

[10] Beebee, H., Hitchcock, C., & Menzies, P. (Eds.). (2009). The Oxford Handbook of Causation. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199279739.001.0001 | DOI 10.1093/oxfordhb/9780199279739.001.0001

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

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

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

[14] Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. https://doi.org/10.1016/j.socnet.2007.04.002 | DOI 10.1016/j.socnet.2007.04.002

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

[16] Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008 | DOI 10.1016/j.socnet.2004.11.008

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

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

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

[20] 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.

[21] 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. https://doi.org/10.1177/0165025407077762 | DOI 10.1177/0165025407077762

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

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

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

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

[26] 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. https://doi.org/10.1080/01443410500041458 | DOI 10.1080/01443410500041458

[27] 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. https://doi.org/10.1007/s10803-017-3272-4 | DOI 10.1007/s10803-017-3272-4

[28] 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.

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

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

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

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

[33] 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.

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

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

[36] Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3–5), 75–174. https://doi.org/10.1016/j.physrep.2009.11.002 | DOI 10.1016/j.physrep.2009.11.002

[37] Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the american Statistical association, 81(395), 832–842. https://doi.org/10.2307/2289017 | DOI 10.1080/01621459.1986.10478342

[38] Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35–41. https://doi.org/10.2307/3033543 | DOI 10.2307/3033543

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

[40] Friedkin, N. E. (1991). Theoretical Foundations for Centrality Measures. American Journal of Sociology, 96(6), 1478–1504. https://doi.org/10.1086/229694 | DOI 10.1086/229694

[41] Gephi.org. (2017). Learn how to use Gephi. https://gephi.org/users/

[42] 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. https://doi.org/10.18637/jss.v024.i09 | DOI 10.18637/jss.v024.i09

[43] 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. https://doi.org/10.1353/dem.0.0045 | DOI 10.1353/dem.0.0045

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

[45] 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. https://doi.org/ 10.1177/1043986214553377 | DOI 10.1177/1043986214553377

[46] 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. https://lib.dr.iastate.edu/ageds_pubs/33/

[47] 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 ( http://www.statnet.org). R package version 3.9.4, https://CRAN.R-project.org/package=ergm

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

[49] 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. https://doi.org/10.1007/bf01067552 | DOI 10.1007/BF01067552

[50] Hervé, M. (2020). Package 'RVAideMemoire'.

[51] 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. https://doi.org/10.1198/016214502388618906 | DOI 10.1198/016214502388618906

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

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

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

[55] 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. https://doi.org/10.1198/106186006X133069 | DOI 10.1198/106186006X133069

[56] 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. https://doi.org/10.18637/jss.v024.i03 | DOI 10.18637/jss.v024.i03

[57] 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). https://doi.org/10.1371/journal.pone.0098679 | DOI 10.1371/journal.pone.0098679

[58] 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. https://doi.org/10.3389/fpsyg.2017.00583 | DOI 10.3389/fpsyg.2017.00583

[59] 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. https://doi.org/10.1108/17415651311326428 | DOI 10.1108/17415651311326428

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

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

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

[63] 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. https://doi.org/10.1111/j.1467-8624.2007.01060.x | DOI 10.1111/j.1467-8624.2007.01060.x

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

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

[66] 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. https://doi.org/10.1080/02680939.2015.1119315 | DOI 10.1080/02680939.2015.1119315

[67] Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28, 247–268. https://doi.org/10.1016/j.socnet.2005.07.002 | DOI 10.1016/j.socnet.2005.07.002

[68] Krivitsky, P. N., & Goodreau, S. M. (2019). STERGM-Separable Temporal ERGMs for modeling discrete relational dynamics with statnet. https://cran.r-project.org/web/packages/tergm/vi­gnettes/STERGM.pdf

[69] 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. https://doi.org/10.1111/rssb.12014 | DOI 10.1111/rssb.12014

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

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

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

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

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

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

[76] Lin, N. (2017). Building a Network Theory of Social Capital. Social Capital, 3–28. https://doi.org/10.4324/9781315129457-1 | DOI 10.4324/9781315129457-1

[77] 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. https://doi.org/10.1016/S0378-8733(03)00013-3 | DOI 10.1016/S0378-8733(03)00013-3

[78] 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. https://doi.org/10.1016/j.socnet.2007.03.002 | DOI 10.1016/j.socnet.2007.03.002

[79] 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. https://doi.org/10.1017/CBO9780511894701 | DOI 10.1017/CBO9780511894701

[80] 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. https://doi.org/10.1016/j.compedu.2003.06.001 | DOI 10.1016/j.compedu.2003.06.001

[81] 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.

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

[83] 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. https://doi.org/10.18637/jss.v024.i04 | DOI 10.18637/jss.v024.i04

[84] 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. http://statnet.org/tergm_tutorial.html

[85] Mrvar, A., & Batagelj, V. (2019). Programs for analysis and visualization of very large networks: Reference manual. http://mrvar.fdv.uni-lj.si/pajek/pajekman.pdf

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

[87] Murphy, P. (2020). Phil Murphy Tutorials. https://rpubs.com/pjmurphy

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

[89] 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. https://doi.org/10.1080/15236803.2010.12001614 | DOI 10.1080/15236803.2010.12001614

[90] 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. https://doi.org/10.1177/0272431698018002001 | DOI 10.1177/0272431698018002001

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

[92] 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. https://doi.org/10.1007/s10734-014-9831-0 | DOI 10.1007/s10734-014-9831-0

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

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

[95] 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. https://doi.org/10.1016/j.socnet.2006.08.002 | DOI 10.1016/j.socnet.2006.08.002

[96] Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581–603. https://doi.org/10.1007/bf02289527 | DOI 10.1007/BF02289527

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

[98] 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. https://doi.org/10.2307/3033795 | DOI 10.2307/3033795

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

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

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

[102] Shibutani, T. (2000). Social processes: an introduction to sociology. iUniverse.com.

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

[104] 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. https://doi.org/10.1080/00222500305892 | DOI 10.1080/00222500305892

[105] 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. https://doi.org/10.1111/j.1467-9531.2006.00176.x | DOI 10.1111/j.1467-9531.2006.00176.x

[106] 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. https://doi.org/10.1016/j.socnet.2009.02.004 | DOI 10.1016/j.socnet.2009.02.004

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

[108] 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.

[109] 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. https://doi.org/10.3102/1076998612458702 | DOI 10.3102/1076998612458702

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

[111] The Statnet Development Team (2019). An Example Analysis Using LOLOG. https://cran.r-project.org/web/packages/lolog/vignettes/lolog-ergm.pdf

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

[113] UCINET. (2020). UCINET Software. https://sites.google.com/site/ucinetsoftware/

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

[115] Van Der Pol, J. (2017). Introduction to network modeling using Exponential Random Graph models (ERGM). https://hal.archives-ouvertes.fr/hal-01284994/document

[116] 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.

[117] 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.

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

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

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

[121] 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. https://doi.org/10.1111/j.1469-7610.1994.tb01806.x10.1007/BF02294547 | DOI 10.1007/BF02294547

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

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