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.
Dr 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 we discuss the issues around their extension to web-based network surveys, data mining and research on social media activities. The course explores ways to best manage and explore different kinds of network data in Excel, its advanced query procedures and, where necessary, Access. The course uses UCINET and NetDraw to produce network diagrams and descriptive statistics and can review other software related to participants' needs. The course serves as a transition to the associated ACSPRI courses 'Network Analysis and Modelling for Social Research' and 'Big Data Analysis for Social Scientists'. The course provides a overview of the possibilities for working with rich network data from interview or online sources and the unique perspectives that qualitative and social research traditions bring to these tasks.
Network research designs covered in this course are:
- Large-scale surveys using ego-centric network (egonet) methods
- Sociometric (‘round robin’) ‘whole network’ studies and small group research
- Associational/ Affiliation (‘2-mode’) networks (including ethological studies)
- Intra-organizational, inter-organizational and community network studies
- Cognitive mapping and socio-cognitive mapping (SCM)
- Data downloads from web sources.
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.
- Course aims and class introductions;
- Research templates and example datasets;
- Egonet data collection;
- Coding and organizing network data;
- Using NetDraw and UCINET.
- Affiliation and associational, 2-mode networks;
- Whole networks – cohesion and paths;
- Whole networks – node centrality;
- Whole networks – regressions, relational contingency tables.
- Working with rich tie data;
- (Latent) subgroup detection and assessing modularity;
- Boundaries, hot spots and clusters;
- Cognitive data and socio-cognitive mapping.
- Web-based network surveys
- Downloading network data from websites
- Consultations with instructor
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
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 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.
Course was suitably practical, very valuable much focussed on learning a new analysis package. (Spring 2016)
This has opened a whole new way of thinking about the analysis of my subject mater. (Spring 2016)
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)
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’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.