Scientific Coordination
Dr.
Sebastian E. Wenz
Tel: +49 221 47694-159
Sebastian E. Wenz
Tel: +49 221 47694-159
Administrative Coordination
Angelika Ruf
Tel: +49 221 47694-162
Tel: +49 221 47694-162
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Course 10: Collecting and Analyzing Longitudinal Social Network Data
Lecturer(s):
Dr. Lars Leszczensky, Dr. Sebastian Pink
Date: 16.08 - 20.08.2021 ics-file
Location: Online via Zoom / Time: 09:00-17:00 (CEST) - including breaks
Course description
[This is a 30 hour class.]
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.
Keywords
Target group
Participants will find the course useful if:
Learning objectives
By the end of the course participants will:
Organizational Structure of the Course:
This is a five-day course with a total amount of 30 hours of virtual class time. Participants can expect a mix of interactive lectures, hands-on exercises, and opportunities for group discussions and individual consultation. Guided exercises in R deepen the understanding of the course material. The lecturers will be available for individual consultations on participants' planned or current projects.
Prerequisites
Software and Hardware Requirements:
The practical examples and exercises will be done in R, so participants should have a recent version installed on their local computer. For working with R in general, we recommend using RStudio.
Prior to the course, participants should install the following R-packages from CRAN, with dependencies:
Participants also should install the newest version of the R-package “RSiena” from R-Forge, with dependencies. (The version on CRAN tends to be outdated.) Section 2.2 of the RSiena manual (Ripley et al. 2020) explains how to install the RSiena package.