Examining the effect of social influence on student performance through network autocorrelation models

References

• G. Adelfio, G. Boscaino, and V. Capursi. A new indicator for higher education student performance, Higher Educ. 68 (2014), pp. 653–668.
   Google Scholar
• L. Anselin, Spatial Econometrics: Methods and Models, Kluwer Academic, Dordrecht, 1988.
   Google Scholar
• M. Attanasio and V. Capursi, eds., Statistical Methods for the Evaluation of University Systems, Physica, Heidelberg, 2011.
   Google Scholar
• S. Bianconcini and S. Cagnone, A general multivariate latent growth model with applications to student achievement, J. Educ. Behav. Stat. 37 (2012), pp. 339–364. doi: 10.3102/1076998610396886
   Web of Science ®Google Scholar
• Bologna Working Group, A framework for qualifications of the European Higher Education Area. Bologna Working Group Report on Qualifications Frameworks (Copenhagen, Danish Ministry of Science, Technology and Innovation), 2005.
   Google Scholar
• S.P. Borgatti, A. Mehra, D.J. Brass, and G. Labianca, Network analysis in the social sciences, Science323 (2009), pp. 892–895. doi: 10.1126/science.1165821
   PubMed Web of Science ®Google Scholar
• Y. Bramoullé, H. Djebbari, and B. Fortin, Identification of peer effects through social networks, J. Econom. 150 (2009), pp. 41–55. doi: 10.1016/j.jeconom.2008.12.021
   Web of Science ®Google Scholar
• C.T. Butts, Social network analysis: A methodological introduction, Asian J. Social Psychol. 11 (2008), pp. 13–41. doi: 10.1111/j.1467-839X.2007.00241.x
   Web of Science ®Google Scholar
• M. Byrne and B. Flood, Examining the relationships among background variables and academic performance of first year accounting students at an Irish University, J. Account. Educ. 26 (2008), pp. 202–212. doi: 10.1016/j.jaccedu.2009.02.001
   Google Scholar
• S. Celant, The analysis of students' academic achievement: the evaluation of peer effects through relational links, Qual. Quant. 47 (2012), pp. 615–631. doi: 10.1007/s11135-011-9536-8
   Web of Science ®Google Scholar
• W. Cheng, W. Ickes, and L. Verhofstadt, How is family support related to students' GPA scores? A longitudinal study, Higher Educ. 64 (2012), pp. 399–420. doi: 10.1007/s10734-011-9501-4
   Google Scholar
• A. Chevalier, S. Gibbons, A. Thorpe, M. Snell, and S. Hoskins, Students' academic self-perception, Econom. Educ. Rev. 28 (2009), pp. 716–727. doi: 10.1016/j.econedurev.2009.06.007
   Web of Science ®Google Scholar
• D. Contini, Immigrant background peer effects in Italian schools, Social Sci. Res. 42 (2013), pp. 1122–1142. doi: 10.1016/j.ssresearch.2013.02.003
   PubMed Web of Science ®Google Scholar
• M. De Paola and V. Scoppa, Peer group effects on the academic performance of Italian students, Appl. Econ. 42 (2010), pp. 2203–2215. doi: 10.1080/00036840701765478
   Web of Science ®Google Scholar
• A.K. Dills and R. Hernández-Julián, Course scheduling and academic performance, Econ. Educ. Rev. 27 (2008), pp. 646–654. doi: 10.1016/j.econedurev.2007.08.001
   Web of Science ®Google Scholar
• P. Doreian, Linear models with spatially distributed data: spatial disturbances or spatial effects?, Sociol. Methods Res. 9 (1980), pp. 29–60. doi: 10.1177/004912418000900102
   Web of Science ®Google Scholar
• P. Doreian, When the data points are not independent, in Developments in Data Analysis, A. Ferligoj and A. Kramberger, eds., Metodoloski zvezki, 1996, pp. 27–46.
   Google Scholar
• P. Doreian, K. Teuter, and C.-H Wang, Network autocorrelation models: some Monte Carlo results, Sociol. Methods Res. 13 (1984), pp. 155–200. doi: 10.1177/0049124184013002001
   Web of Science ®Google Scholar
• M.M. Dow, Galton's problem as multiple network autocorrelation effects cultural trait transmission and ecological constraint, Cross-cult. Res. 41 (2007), pp. 336–363.
   Web of Science ®Google Scholar
• M.M. Dow, M.L. Burton, and D.R. White, Network autocorrelation: a simulation study of a foundational problem in regression and survey research, Social Netw. 4 (1982), pp. 169–200. doi: 10.1016/0378-8733(82)90031-4
   Web of Science ®Google Scholar
• M. Enea and M. Attanasio, An association model for bivariate data with application to the analysis of university students' success, J. Appl. Stat. (2015), pp. 1–12 ( ahead-of-print). doi: 10.1080/02664763.2014.998407
   PubMed Web of Science ®Google Scholar
• G. Foster, Teacher effects on student attrition and performance in mass-market tertiary education, Higher Educ. 60 (2010), pp. 301–319. doi: 10.1007/s10734-009-9301-2
   Google Scholar
• K. Fujimoto, C. -P. Chou, and T.W. Valente, The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter, Social Netw. 33 (2011), pp. 231–243. doi: 10.1016/j.socnet.2011.06.001
   PubMed Web of Science ®Google Scholar
• G. Huon, B. Spehar, P. Adam, and W. Rifkin, Resource use and academic performance among first year psychology students, Higher Educ. 53 (2007), pp. 1–27. doi: 10.1007/s10734-005-1727-6
   Google Scholar
• V.E. Johnson, An alternative to traditional GPA for evaluating student performance, Statist. Sci. 12 (1997), pp. 251–269. doi: 10.1214/ss/1030037959
   Web of Science ®Google Scholar
• D. Kember, Q.W. Jamieson, M. Pomfret, and E.T.T. Wong, Learning approaches, study time and academic performance, Higher Educ. 29 (1995), pp. 329–343. doi: 10.1007/BF01384497
   Google Scholar
• D. Kennedy, Writing and Using Learning Outcomes: A Practical Guide, University College Cork, 2007.
   Google Scholar
• R.Th.A.J. Leenders, Longitudinal behavior of network structure and actor attributes: modeling interdependence of contagion and selection, in Evolution of Social Networks, P. Doreian and F. Stokman, eds., Routledge, The Netherlands, 1997, pp. 165–184.
   Google Scholar
• R.Th.A.J. Leenders, Modeling social influence through network autocorrelation: Constructing the weight matrix, Social Netw. 24 (2002), pp. 21–47. doi: 10.1016/S0378-8733(01)00049-1
   Web of Science ®Google Scholar
• A. Lizzio, K. Wilson, and R. Simons, University students' perceptions of the learning environment and academic outcomes: implications for theory and practice, Studies Higher Educ. 27 (2002), pp. 27–52. doi: 10.1080/03075070120099359
   Web of Science ®Google Scholar
• C.F. Manski, Identification of endogenous social effects: The reflection problem, Rev. Econ. Stud. 60 (1993), pp. 531–542. doi: 10.2307/2298123
   Web of Science ®Google Scholar
• S. Maroulis and L.M. Gomez, Does ‘Connectedness’ matter? Evidence from a social network analysis within a small-school reform, Teachers College Record 110 (2008), pp. 1901–1929.
   Web of Science ®Google Scholar
• P. Marsden, Survey methods for network data, in The SAGE Handbook of Social Network Analysis, J. Scott and P.J. Carrington, eds., Sage, London, 2011, pp. 370–388.
   Google Scholar
• P.V. Marsden and N.E. Friedkin, Network studies of social influence, Sociol. Methods Res. 22 (1993), pp. 127–151. doi: 10.1177/0049124193022001006
   Web of Science ®Google Scholar
• C. Mills, J. Heyworth, L. Rosenwax, S. Carr, and M. Rosenberg, Factors associated with the academic success of first year health science students, Adv. Health Sci. Educ. 14 (2009), pp. 205–217. doi: 10.1007/s10459-008-9103-9
   PubMed Web of Science ®Google Scholar
• M.S. Mizruchi and E.J. Neuman, The effect of density on the level of bias in the network autocorrelation model, Social Netw. 30 (2008), pp. 190–200. doi: 10.1016/j.socnet.2008.02.002
   Web of Science ®Google Scholar
• A.J. O'Malley, The analysis of social network data: an exciting frontier for statisticians, Stat. Med. 32 (2013), pp. 539–555. doi: 10.1002/sim.5630
   PubMed Web of Science ®Google Scholar
• O. Poldin, D. Valeeva, and M. Yudkevich, How social ties affect peer group effects: Case of university students, Higher School of Economics Research Paper No. WP BPR 15, 2013.
   Google Scholar
• M.G. Ramírez Ortiz, J.R. Caballero Hoyos, and M.G. Ramírez López, The social networks of academic performance in a student context of poverty in Mexico, Social Netw. 26 (2004), pp. 175–188. doi: 10.1016/j.socnet.2004.01.010
   Web of Science ®Google Scholar
• B. Rienties, S. Beausaert, T. Grohnert, S. Niemantsverdriet, and P. Kommers, Understanding academic performance of international students: the role of ethnicity, academic and social integration, Higher Educ. 63 (2012), pp. 685–700. doi: 10.1007/s10734-011-9468-1
   Google Scholar
• B. Sacerdote, Peer effects in education: How might they work, how big are they and how much do we know thus far? in Handbook of the Economics of Education, E.A. Hanushek, S.J. Machin and L. Woessmann, eds., Vol. 3, Elsevier, St. Louis, MO, 2011, pp. 249–277.
   Google Scholar
• A. Skrondal and P. Laake, Regression among factor scores, Psychometrika 66 (2001), pp. 563–575. doi: 10.1007/BF02296196
   Web of Science ®Google Scholar
• K.C. Soh, Grade point average: what's wrong and what's the alternative?, J. Higher Educ. Policy Manag. 33 (2010), pp. 27–36. doi: 10.1080/1360080X.2011.537009
   Google Scholar
• J.D. Vermunt, Relations between student learning patterns and personal and contextual factors and academic performance, Higher Educ. 49 (2005), pp. 205–234. doi: 10.1007/s10734-004-6664-2
   Google Scholar
• S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, Cambridge, 1994.
   Google Scholar
• E.A. Weihua, Models and methods to identify peer effects, in The SAGE Handbook of Social Network Analysis, J. Scott and P.J. Carrington, eds., Sage, London, 2011, pp. 514–532.
   Google Scholar
• B. Zhang, A.C. Thomas, P. Doreian, D. Krackhardt, and R. Krishnan, Contrasting multiple social network autocorrelations for binary outcomes, with applications to technology adoption, ACM Trans. Manag. Inf. Syst. 3 (2013), pp. 1–21.
   Google Scholar