SOCIAL NETWORK ANALYSIS - 2023/4
Module code: SOCM058
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice during the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
In 1967 Stanley Milgram asked 160 people in Omaha, Nebraska, to move a message to a stock broker in Cambridge, Massachusetts, using only a chain of friends and acquaintances. 27.5% of chains were completed using, on average, five intermediate acquaintances (i.e. on average, there were only six degrees of separation between the people in Omaha and the stock broker in Cambridge). Milgram’s study demonstrates that, in some sense, ‘we are all bound together in a tightly knit social fabric.’ Social network analysis helps us understand individuals’ contact with the larger social world. It focuses on relationships between social entities, and analyses patterns of social interaction and their influence on individual behaviour.
This module introduces students to various concepts, methods, and applications of social network analysis drawn from the social sciences. We will start with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. We will then consider descriptive network- and individual-level statistics and their applications in social science research. Finally, we will discuss methods for testing hypotheses about social network structure, and introduce models for social networks. The emphasis will be on applying social network analysis theories and methods to real-world data, and on understanding and interpreting results, rather than on the underlying mathematics.
BERLUSCONI Giulia (Sociology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G310
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 105
Lecture Hours: 10
Laboratory Hours: 10
Guided Learning: 15
Captured Content: 10
Prerequisites / Co-requisites
None. Familiarity with basic mathematical notation and standard statistical methods is an advantage but not essential. Familiarity with R is also an advantage.
Indicative content includes:
- Historical and theoretical foundations
- Data sources and data collection strategies
- Graphs, matrices, and sociograms
- Centrality and centralisation
- Balance, reciprocity, and transitivity
- Density and cohesive subgroups
- Equivalence and blockmodels
- Dyads and triads
- Statistical models for social networks
|Assessment type||Unit of assessment||Weighting|
|Oral exam or presentation||INDIVIDUAL PROJECT ORAL PRESENTATION||20|
|Coursework||INDIVIDUAL PROJECT REPORT (3000 WORDS)||80|
The assessment strategy is designed to provide students with the opportunity to demonstrate an understanding of social network analysis theories and methods, investigating a chosen topic using network data. Using their own dataset or publicly available ones, students will use social network analysis statistics and models to describe the network and test hypotheses. They will also be required to interpret the results, and discuss their implications.
Thus, the summative assessment for this module consists of:
- An individual project oral presentation where students will present the preliminary results of their analysis
- An individual project report (3,000 words) where students will present and critically discuss their results
On days 1-3 students will complete workshop exercises, and they will discuss the results with each other and the lecturer, who will provide group and individual feedback.
For the summative assessment, students will receive individual, written feedback. The formative assessment includes verbal feedback in class, when students will be asked to work on class exercises which apply their knowledge of social network analysis methods.
- • Provide students with an introduction to theories and methods in social network analysis
- • Give a clear understanding of data collection and data management strategies
- • Outline descriptive and inferential methods for social networks commonly used in social research
- • Enable students to critically evaluate empirical social network research
- • Provide training to use software to investigate social networks
|001||Describe social network analysis concepts, data collection strategies, and analytic techniques|
|002||Have a critical understanding of the key network data collection strategies and their potential limitations|
|003||Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results|
|004||Be able to implement a social network analysis on real world data and critically evaluate the results|
|005||Engage with different applications of social network analysis in the social sciences|
|006||Use social network analysis software to analyse network data|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Expose students to core theories and methods in social network analysis whilst also offering an opportunity to familiarise themselves with the technical and practical issues of analysing network data. Alongside discussion of social network analysis theories and current applications in social research, the use of existing datasets and software during the workshops will give students practical experience of applying theory to novel scientific problems, conducting network analysis, and critically interpreting the results.
The learning and teaching methods include:
- Practical workshops
- Group discussion and feedback
This module is taught intensively over one week. Days 1-3 will include a mix of lectures and workshops. On day 4 students will engage in independent study and preparation for the individual oral presentation. On day 5 students will return to the classroom to present their project and receive feedback.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: SOCM058
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.