SOW-SCS211
Social Networks
Course infoSchedule
Course moduleSOW-SCS211
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Social and Cultural Sciences;
Lecturer(s)
Coordinator
prof. dr. J. Tolsma
Other course modules lecturer
Lecturer
prof. dr. J. Tolsma
Other course modules lecturer
Contactperson for the course
prof. dr. J. Tolsma
Other course modules lecturer
Examiner
prof. dr. J. Tolsma
Other course modules lecturer
Academic year2020
Period
PER1  (01/09/2020 to 01/11/2020)
Starting block
PER1
Course mode
full-time
Remark
Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course.
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesNo
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
At the end of this course,

you will have the knowledge to:
•    define core concepts related to social networks;
•    review possible causes and consequences of social networks with respect to core topics in the social sciences, such as inequality, cohesion and diversity;
•    review the major developments in social network research;

you will have the research attitude to
•    discuss the implications of employing a social network perspective for the development of theories; i.e. deduce innovative research questions, hypotheses and designs;
•    discuss the implications of employing a social network perspective for research methods and statistical modeling;
•    be aware of ethical issues involved with repect to the collection of social network data (especially with respect to online social networks and social networks among children).

you will have the skills to:
•    describe social networks (e.g. by using network characteristics as density, reciprocity, (mean) distance, transitivity)
•    should be able to apply social network analysis in order to test hypotheses (e.g. with respect to selection and influence). 
•    use the software package R to perform manipulations on social network data
•    use the software package R to perform social network analysis (including RSiena) 
•    use the software package R to graphically summarize social network data and results from SNA
•    write simple functions in R
•    programme basic Agent Based Models in R
 
Content
Social Science researchers study how and with whom people interact and how societies and communities influence our attitudes and behavior. This course about social networks therefore lies at the core of the social sciences. We will study both the development of social networks as well as the consequences of the social networks in which we are embedded for outcomes related to inequality and cohesion. 

We will start our discussion with the simplest social network, namely a dyadic relationship (e.g. between partners). We will study trends in partner homogamy with respect to ethnicity, age and education. Then we will look how inequality is accumulated within households. 

We then move to egocentric networks. Here we will focus on core discussion networks; Who is discussing important matters with whom?  And how do our direct social relations affect our health? Can social networks compensate for a lack in personal resources or is health inequality also transmitted via social gradients in social networks? 

A large part of the course will be devoted to research on sociocentric networks. We will use examples from recent research with respect to online social networks (e.g. twitter networks of policitians)  as well as social (friendship and bullying) networks in schoolclasses. How can we describe sociocentric social networks? What are important network properties? How do intitial network properties influence the development of the network? We will demonstrate that longitudinal data of sociocentric networks are very well suited to distentangle selection versus influence effects. 

Finally, we briefly discuss the relationship between SNA (social network analysis) and ABM (agent based modelling).
 
Level

Presumed foreknowledge

Test information

Specifics

Assumed previous knowledge
SOW-SCS131: Advanced Regression Analysis B

Required materials
Articles
Articles, a reading list will be announced in Blackboard

Instructional modes
Lecture
Attendance MandatoryYes

Remark
Lectures, presentations, discussion meetings, assignments, practical.

Tests
Theoretical examniation
Test weight1
Test typeExam
OpportunitiesBlock PER1, Block PER2

Practical examination
Test weight1
Test typeLab course
OpportunitiesBlock PER1, Block PER2