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.

Participants will find the course useful if:

 

 

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 (The version on CRAN tends to be outdated.)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.