20 August 2021
Collecting and Analyzing Longitudinal Social Network Dataonline course
Social scientists often are interested in understanding how social networks emerge and/or how they shape individual behavior. These questions of network formation (“selection”) and network effects (“influence”) concern both human individuals and organizational units. Examples for selection are the emergence of friendship between people or cooperation between firms; examples for influence are adolescents start smoking because of their friends or firms copying other firms' strategies. Selection and influence are inherently dynamic processes, but few social scientists have been trained in collecting, processing and analyzing longitudinal social network data.
This practical course guides participants who intent to collect and/or analyze longitudinal social network data. We start by conceptualizing and planning data collection, discussing both general challenges and, if applicable, participants' own projects. Thereafter, participants learn how to handle and manage network data in R by guided examples and exercises. The main part of the course focuses on specifying, estimating and interpreting stochastic actor-oriented models (SAOM) for network dynamics, again with a mix of guided examples and practical exercises using the R package RSiena. We consider selection and influence as well as how SAOM can help to empirically disentangle these competing processes.
Dr. Lars Leszczensky is a postdoctoral research fellow at the Mannheim Centre for European Social Research at the University of Mannheim.
Dr. Sebastian Pink is a postdoctoral researcher at the Chair of General Sociology at the University of Mannheim.
Participants will find the course useful if they:
- (intend or consider to) collect longitudinal social network data
- (intend or consider to) analyze longitudinal social network data to help them answering substantive research questions
- already are analyzing social network data and want to discuss their work
- Basic knowledge in quantitative data analysis
- Prior knowledge of social network analysis and/or R is helpful but not necessarily required
By the end of the course participants will:
- know how to design and conduct a longitudinal social network study
- be able to manage and handle longitudinal network data
- know how to exploit the potential of stochastic actor-oriented models for their research aims
- understand how to specify and estimate stochastic actor-oriented models for network dynamics in R
- have learned how to interpret and communicate results of stochastic actor-oriented models
- Certificate of attendance issued upon completion.
- 4 ECTS credit points via the University of Mannheim for regular attendance and satisfactory work on daily assignments and for submitting a paper of about 5000 words to the lecturer(s) up to 4 weeks after the end of the summer school (50 EUR administration fee).
EUR 400: Student/PhD student rate.
EUR 600: Academic/non-profit rate.
The rates include the tuition fee and the course materials.
Scholarships covering the participation fee are available from the German Academic Exchange Service (DAAD).