This free webinar, organised by the UK Data Service, is the first in a series of three on understanding and using SNA methods for social science research purposes. In this webinar we cover the fundamental concepts and terms underpinning SNA, and demonstrate how network data is structured and differs from more traditional social science datasets (e.g. social surveys).

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 first in a series of three on understanding and using SNA methods for social science research purposes. In this webinar we cover the fundamental concepts and terms underpinning SNA, and demonstrate how network data is structured and differs from more traditional social science datasets (e.g. social surveys). We will also outline a simple analysis of social network data using the Python programming language. As a result of attending this webinar, participants will possess the necessary knowledge and vocabulary to undertake a SNA research project.

Details:

Level: Introductory, for individuals with no prior knowledge or experience of social network analysis

Duration: 45 minutes, followed by questions

Pre-requisites: None

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

Programming language: Python is used to examine the structure of social network data and to perform a simple analysis

Materials: Participants will have access to an interactive online notebook through which they can learn more about SNA and execute Python code

Learning outcomes: Understand fundamental concepts and terms associated with SNA and understand how social network data are structured