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Stochastic Network Models

Social network models instantiate how actors in a social system depend on one another. As such, they make it possible to understand how a social group functions as an organic whole. The complex dependencies characteristic for network data pose a challenge to empirical data analysis. They require non-standard estimation tools employing simulation-based statistical inference. My main work focuses on the development of such models and their application in various social science disciplines.

• Steglich, Ch., T.A.B. Snijders, & M. Pearson (2010). ‘Dynamic Networks and Behavior: Separating Selection from Influence’, Sociological Methodology 40, 329-393.
• Snijders, T.A.B., & Ch. Steglich (2015). ‘Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models’, Sociological Methods and Research 44, 222-271.
• Snijders, T.A.B., G. van de Bunt, & Ch. Steglich (2010). ‘Introduction to stochastic actor-based models for network dynamics’, Social Networks 32, 44-60.
• Huisman, M., & Ch. Steglich (2008). ‘Treatment of Non-Response in Longitudinal Network Studies’, Social Networks 30, 297–308.
  
  

Networks in Adolescent Health

No network has been studied as often as the friendship network among the students in a school class. For sociologists, the school context provides a convenient and accessible opportunity to study social behaviour in a bounded social system. Develop¬mental psychologists have a more specific interest in the age group under study. Adolescence is a period in life when parental influence wanes and peer influence increases. Adolescents undergo puberty, experience a maturity gap, experiment with risk behaviour and explore new relationships. Data about friendship networks in school make it possible to test many theories of adolescent development. This includes the diffusion of substance use, which starts in adolescence to later become a major public health issue.

• Steglich, Ch., P. Sinclair, J. Holliday, & L. Moore, (2012). ‘Actor-based analysis of peer influence in A Stop Smoking in Schools Trial (ASSIST)’, Social Networks 34, 359-369.
• Mercken, L., T.A.B. Snijders, Ch. Steglich, E. Vartiainen, & H. de Vries (2010). ‘Smoking-based selection and influence in gender-segregated friendship networks’, Addiction 105, 1280-1289.
• Knecht, A., W.J. Burk, J. Weesie, & Ch. Steglich (2011). ‘Friendship and alcohol use in early adolescence: a social network approach’, Journal of Research on Adolescence 21, 475-487.
• Mercken, L., E. Sleddens, H. de Vries, & Ch. Steglich, (2013). ‘Choosing adolescent smokers as friends: The role of parenting and parental smoking’, Journal of Adolescence 36, 383-392.
  
  

Networks in Ò»±¾µÀ

Students do not just to go to school to socialise with peers. The school as a social institution also has a societal purpose: to give students a formal education and equip them with knowledge and skills needed to navigate in society. Social networks can help or hinder academic progress of students, depending on details of the peer interactions taking place, and teachers’ skills to respond to them.

• Brouwer, J., A. Flache, E. Jansen, A. Hofman, & Ch. Steglich (2018). ‘Emergent achievement segregation in freshmen learning community networks’, Higher Ò»±¾µÀ. https://doi.org/10.1007/s10734-017-0221-2
• Steglich, Ch., & A. Knecht (2014). ‘Studious by association? Effects of teacher’s attunement to students’ peer relations’, Zeitschrift für Erziehungswissenschaft 17, 153-170.
• Rambaran, J.A., A. Hopmeyer, D. Schwartz, Ch. Steglich, D. Badaly, & R. Veenstra (2017). ‘Academic Functioning and Peer Influences: A Short-Term Longitudinal Study of Network-Behavior Dynamics in Middle Adolescence’, Child Development 88, 523-543.
• Lomi, A., T.A.B. Snijders, Ch. Steglich, & V.J. Torlò (2011). ‘Why Are Some More Peer Than Others? Class-level Evidence from a Longitudinal Study of Social Networks and Individual Academic Performance’, Social Science Research 40, 1506-1520.
  
  

Networks and Organisations

Adults’ social networks are much more loosely structured than those of school children, because no social context has as firm a grip on an adult’s daily life as the school has on a student’s. The probably strongest such context is defined by paid work, which introduces a split between, on the one hand, the actor as representative of the employer interacting with representatives of other employers in an inter-organisational network, and, on the other hand, the actor as one of many employees interacting in an intra-organisational network.

• Labun, A., Ch. Steglich, & R. Wittek (2016). ‘The Co-evolution of Power and Friendship Networks in an Organization’, Network Science 4, 364-384.
• Pauksztat, B., Ch. Steglich, & R. Wittek, (2011). ‘Who speaks up to whom? A network analysis of employee voice’, Social Networks 33, 303-316.
• Checkley, M., Ch. Steglich, D. Angwin, & R. Endersby (2014). ‘Firm Performance and the Evolution of Cooperative Interfirm Networks: UK Venture Capital Syndication’, Strategic Change 23, 107-118.
• lwardt, L., Ch. Steglich, & R. Wittek (2012). ‘The co-evolution of gossip and friendship in workplace social networks’, Social Networks 34, 623-633.
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Networks and the Labour Market

Since short, I work on network models for mobility data derived from the Swedish national population registers. Combining insights from the 1970s network sociology of labour markets (White, Granovetter) with the vast data material available in Swedish registers, we are currently studying how the various Swedish school reforms since the 1990s affected teachers’ labour mobility, and how the Swedish labour market absorbs the recent wave of mainly Syrian immigrants.

Networks and the Internet

The facilitation of interpersonal contact on online social networking sites is a currently ongoing revolution in the field of human relations and human network structures. In a new research line at the IAS, we aim to study analytically the societal consequences of this revolution, which at first sight range from very positive effects (overall reduced opportunity costs; empowering of minorities) to very negative ones (increased polarisation; diffusion of hate speech and fake news).