Many social scientists 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 course guides participants who intend to collect and/or analyze longitudinal social network data. For this purpose, we rely on a mix of interactive lectures, individual and group work, guided examples, and practical exercises. We use R for all guided examples and exercises, and we use and provide exemplary school-based friendship network data.

 

On Day 1, we introduce basic concepts, typical research questions, and longitudinal social network data. Participants can bring forward their own research aims. Participants further learn how to handle and manage network data in R by guided examples and exercises, including the visualization of longitudinal networks. On Day 2, we cover the design of longitudinal social network studies and the collection of longitudinal social network data, discussing both general challenges and, if applicable, participants' own data collection projects. Further, we will introduce stochastic actor-oriented models (SAOM) for the co-evolution of networks and behavior.  

 

On Day 3 and 4, we address how to analyze selection (Day 3) and influence (Day 4) with SAOM. On both days, we first introduce the respective model and show and practice how to specify and estimate it using R.  Then we practice how to interpret the model results and graphically communicate findings.

 

On Day 5, we address several advanced topics that participants likely will encounter when working with SAOM, such as convergence in parameter estimates, goodness of fit, and different means of analyzing multiple networks. We close by giving participants group-based and individual feedback on their own projects.

 

A detailed syllabus will soon be available for download here.