This free webinar, organised by the UK Data Service, is the third in a series of three on understanding and using SNA methods for social science research purposes. In this webinar we define and demonstrate basic and intermediate methods of analysis, including measuring network size, density, cohesion, paths, structural holes and more.

Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. Thus, computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientist’s toolkit. Social Network Analysis (SNA) offers a rich and insightful methodological approach for uncovering and understanding social structures, relations and networks of association. This free webinar, organised by the UK Data Service, is the third in a series of three on understanding and using SNA methods for social science research purposes. In this webinar we define and demonstrate basic and intermediate methods of analysis, including measuring network size, density, cohesion, paths, structural holes and more. We also reference more advanced approaches (e.g. exponential random graph modelling, relational event modelling). As a result of attending this webinar, participants will understand how to make sense of and draw substantive insights from social network data.

Details:

Level: Intermediate, for individuals with some prior knowledge of social network concepts and data

Duration: 45 minutes, followed by questions

Pre-requisites: The following webinars cover necessary background knowledge and skills:

- Social Network Analysis: Basic Concepts

- Social Network Analysis: Getting and Marshalling Data

Audience: Researchers and analysts from any disciplinary background interested in employing network analysis for social science research purposes

Programming language: Python

Materials: Participants will have access to an interactive online notebook through which they can replicate the analysis using Python

Learning outcomes: Understand a range of basic and intermediate analytical methods for use with social network data and be able to use Python for analysing social network data