# Wiss. Koordination

Reinhard Schunck

Tel: +49 221 47694160

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Tel: +49 221 47694160

# Administrative Koordination

Angelika Ruf

Tel: +49 221 47694-162

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Bitte haben Sie einen Moment Geduld........

# Week 3: Longitudinal Network Analysis Using RSiena

**Dozent(en):**

Prof. Tom A.B. Snijders, PhD, Dr. András Vörös

Datum: 05.03 - 09.03.2018 ics-Datei

## Seminarinhalt

Stochastic actor-based models for network dynamics are models for statistical inference for network panel data, i.e., repeated measures of a network, or of network and behaviour, or of multiple networks, on a given group of actors (where some turnover of the group is allowed). This methodology combines network analysis and statistical inference by representing network dynamics by simulation models, akin to agent-based simulation, but with a flexibility that allows the connection with empirical data, expressing the operation of several 'mechanisms' jointly, and testing of hypotheses while controlling for other mechanisms that may also be operating. Statistical procedures for applying these models are implemented in the R package

*RSiena*.*Siena*stands for*Simulation Investigation for Empirical Network Analysis*. The course will give an explanation of the model, and how it is applied to longitudinal panel data of networks, which may be combined with actor attributes, and/or with other networks as co-evolving dependent variables. There will be practical exercises with*RSiena*. Participants are encouraged to bring their own data for analysis.**Keywords**

Longitudinal network analysis, statistical inference, Siena, Social selection, Social influence, Co-evolution.

## Zielgruppe

Participants will find the course useful if they

- are social scientists who currently or in the near future are conducting studies of social network dynamics, perhaps of social influence in networks;
- have experience with statistical modelling;
- enjoy working with R (even if perhaps having as yet little experience with it).

## Lernziel

By the end of the course participants will

- understand the principles of the stochastic actor-oriented model for network dynamics;
- have some understanding of translating hypotheses about network dynamics into propositions about effects in actor-oriented models;
- be able to assess model specification in actor-oriented models;
- have seen a variety of network data structures where the stochastic actor-oriented model can be applied;
- have experience in operating with
*RSiena*and interpreting its results.

## Voraussetzungen

- Knowledge of basic and intermediate statistics (including linear and logistic regression).
- Understanding of basic network concepts
- Basic working knowledge of R.