In this 5-day (9.30am-5.00pm daily) intensive course you will learn how to conduct social network research, moving from the fundamentals of networks to an introduction to cutting-edge statistical network models. A general knowledge of statistics is assumed (e.g., correlation, logistic regression). You will require your own PC laptop (or Mac with Windows installed – Mac OS is not supported). Software will be provided.

This Blended Learning course will include a mix of both in-person and online components designed to work together to enhance your learning experience. We will introduce topics to you, then, using social network software, apply that knowledge straight away through applied social network exercises. The course will cover the following key themes:

Network Fundamentals

What is distinctive about social network research?
Network data: Representations and formats
Qualitative versus quantitative data collection
Primary versus secondary data sources
Ethics for network research
Organisational network methods
Data entry, data processing and management
VPNet software for visualisation of networks

Key Concepts & Descriptive SNA

Network Building blocks: density; reciprocity; degree; connectivity; centrality; clustering; and preferential attachment
Social Selection vs Social Influence
Multiplex and bipartite networks
Introductory approaches to statistical inference

Introduction to Exponential Random Graph Models (ERGMs)

What are exponential random graph models (ERGMs)?
Formation of network structure
MPNet software for network models

Network Evaluation

Network effectiveness
Network problem solving

Pricing
Standard: AUD$3,500 (including GST)

Non-Swinburne PhD Student: AUD$1,750 (including GST)

Swinburne Staff and PhD Students: Free

For more information, please contact Dr Bopha Roden – broden@swin.edu.au