This course covers data collection and research design, visualisation and basic analytic methods used in social network research. It is designed for mixed methods and qualitative researchers.
Associate Prof Malcolm Alexander is one of Australia’s leading sociologists working in the area of social network analysis and mathematical sociology. He made intensive studies of Australian business elite networks of the 1990s directed to public issues of corporate governance and investor capitalism. In recent years he has developed network analysis in new directions through his focus on 2-mode network mapping and investigations of elite networks in the civic cultures of Australian cities. He has published numerous articles in sociological journals, is the editor of two books and was also an editorial member of the Journal of Sociology and executive member and Treasurer of The Australian Sociological Association.
Designed for mixed methods and qualitative social researchers, this course covers research designs and methods used in social network research. The basic, pioneering methodologies of Social Network Analysis (SNA) are examined and extended to internet-mediated network research (primarily web-based surveys), and research on social media activities in Facebook, Twitter and Youtube. The course explores ways to best manage and explore different kinds of network data in Excel to produce network diagrams and descriptive statistics using UCINET, NetDraw and other software related to participants' needs. The course includes transition modules that relate its work to the associated ACSPRI courses 'Network Analysis and Modelling for Social Research' and 'Big Data Analysis for Social Scientists'.
Network research designs covered in this course are:
- Sociometric (‘round robin’) ‘whole network’ studies and small group research
- Large-scale surveys using ego-centric network (egonet) methods
- Associational/ Affiliation (‘2-mode’) networks (including ethological studies)
- Web-based intra-organizational and community surveys
- Cognitive mapping and socio-cognitive mapping (SCM)
- Data and text downloads of social media activity
For each design we consider the best ways to code and manage raw data, the most efficient formats for importing such data into visualisation and analysis software and appropriate measures and statistics for reporting findings. We also discuss the process of relating validated findings to research questions (‘meta-inference’) and extrapolating them to other settings.
Day 1
- Course aims and class introductions;
- Research templates and example datasets;
- Egonet data collection;
- Coding and organizing network data;
- Using NetDraw and UCINET.
Day 2
- Affiliation and associational, 2-mode networks;
- Whole networks – cohesion and paths;
- Whole networks – node centrality;
- Whole networks – regressions, homophily, valued (‘thick’) data.
Day 3
- (Latent) subgroup detection;
- Boundaries, hot spots and clusters;
- Cognitive data and socio-cognitive mapping;
- Web-based network surveys.
Day 4
- Social media data: where is the network data?
- How do we deal with text data?
- What software do we need?
- Consultations with instructor
Day 5
Mini-conference: Participants present a short account of the particular project they have worked on through the course.
This course is run in a computer lab. Equipment will be provided.
No prior knowledge of social network analysis is required.
Participants should be comfortable with using spreadsheets (Excel), have some social science background and be familiar with discussions of research methods.
- Scott, J. (2013). Social Network Analysis, SAGE Publications.
- Borgatti, S., M. Everett and J. Johnson (2013), Analyzing Social Networks, SAGE Publications.
- Garry L. Robins, Doing Network Research: Network-based Research Design for Social Scientists (2015), SAGE Publications.
- Borgatti, S. P., M. G. Everett, et al. (2002). Ucinet 6 for Windows: Software for social network analysis. Harvard, Analytic Technologies. Manual; User Guide. www.analytictech.com
- Pajek: Program for Large Network Analysis: at http://vlado.fmf.uni-lj.si/pub/networks/pajek/
Q: Was this course run under another name?
A: This course incorporates foundational materials from the previous ACSPRI course 'Introduction to social network research and network analysis'. It has extra materials on web-based data collection and social media research but less discussion of network theories, statistics and modelling.
Q: What is social network analysis?
A: is a strategy for investigating social structures through the use of network and graph theories. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties or edges (relationships or interactions) that connect them.
Malcolm explains things so well/clearly! (Summer 2016)
New methods for analysis & presenting complex relationships. (Summer 2016)
Well balanced in terms of ”hands on” time compared with face to face teaching. (Summer 2016)
Yes, a good mix of lecture and computer use (Winter 2015)
Great intro to the research skills I need and helped enormously shaping my project. (Summer 2015)
Very useful instruction that will allow further substantial self study. (Spring 2014)
I can see very well how to apply many things I’ve learnt to my job. (Summer 2014)
I’ve learnt a lot about UC Net and Netdraw, about its use and capability. (Summer 2014)
The instructor's bound, book length course notes will serve as the course texts.