Uitgebreide vaknaam 
Statistical Analysis of Social Networks 
Leerdoelen 
Students develop problem awareness related to the analysis of interdependent data, in particular sociocentric 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 nonstandard techniques of data analysis. While for personal (a.k.a. egocentric) 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 sociocentric network data, where the totality of network relations in a welldefined 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 
