Statistical Analysis of Social Networks

Uitgebreide vaknaam	Statistical Analysis of Social Networks
Leerdoelen	Students develop problem awareness related to the analysis of interdependent data, in particular socio-centric network data. They gain knowledge about the prevalent statistical techniques to analyse these data, and develop the practical skills to perform these analyses with the pertinent software packages.
Omschrijving	Social network analysis is the study of interdependencies, between social actors and between dyads (i.e., pairs of actors). As such, the whole discipline is at odds with the independence assumptions underlying most of the common statistical methods. Social network data require non-standard techniques of data analysis. While for personal (a.k.a. ego-centric) network data, some independence can be retained through sampling (e.g., by assessing personal networks of a random sample of focal individuals), this is not the case for socio-centric network data, where the totality of network relations in a well-defined group of social actors is assessed. During the course, we cover prominent statistical approaches and techniques specially designed for complete network data analysis, such as: Methods based on permutation tests, dyad dependence models, exponential random graph models, and stochastic models for network evolution and peer influence processes in networks. In the accompanying computer labs, students will learn how to practically work with these models, making use of different software packages (Ucinet, StOCNET, PNet, Statnet, RSiena).
Uren per week	2
Onderwijsvorm	computerpracticum, hoorcollege (attendance of the computer practicals is mandatory)
Toetsvorm	opdrachten, tentamen
Vaksoort	master
Coördinator	dr. C.E.G. Steglich
Docent(en)	dr. C.E.G. Steglich
Verplichte literatuur	Titel Auteur ISBN Prijs The course material will consist of a series of scientific papers linked to via Nestor.
Entreevoorwaarden	Participants should have a basic understanding of statistical principles (bachelor level statistics). Prior knowledge of social networks (e.g., from attending the ReMa sociology stream’s course ‘Social Networks and Social Capital’, or the sociology bachelor course ‘Social Networks’) is of advantage, but not required.
Opmerkingen
Opgenomen in	Opleiding Jaar Periode Type ReMa Behavioural and Social Sciences - semester I a keuze