GESIS Training Courses

Scientific Coordination

Julia Leesch
Tel: +49 221 47694-169

Administrative Coordination

Laura Rüwe

Introduction to ego-networks: theory, research design and analysis

Prof. Dr. Lea Ellwardt, Dr. Markus Gamper

Location: Cologne / Course language: Englisch

About the lecturer - Prof. Dr. Lea Ellwardt

About the lecturer - Dr. Markus Gamper

Course description

This three-day workshop provides an introduction into the theoretical concepts as well as the methods of data collection and analysis for egocentric social networks. The first day is dedicated to definitions and terminologies around egocentric and complete social networks, which a focus on the history of social network analysis, influential models and their importance for the empirical collection of network data. Exemplary research is discussed, together with the advantages and disadvantages of investigating ego- versus complete networks. The second day, which has a stronger focus on quantitative methods, is dedicated to reflecting on suitable research questions and the requirements of a research design in the process of collecting egocentric network data. Different possible approaches are discussed, along with the potential pitfalls and biases of the resulting measures. Examples from survey questionnaires are also provided. The third day is reserved for the analysis of egocentric data, that is the quantitative assessment of common metrics, such as size and density of a network. This includes hands-on demonstrations and a practical computer exercise (with Stata and/or UCINET).


Target group

The workshop is designed for participants with no or only little knowledge about theories and methods of egocentric social networks. The workshop thus provides a first introduction for people less familiar with this topic.

Learning objectives

After completion of this workshop, participants should be able to
1) cite and critically reflect the relevant key literature on (ego-centric) social networks,
2) develop a research design aimed at collecting quantitative ego-network data, and
3) analyze basic summary characteristics of ego-networks using a statistics software.


Participants should possess a general affinity toward social science theory. Participants should also be open to acquainting themselves with quantitative methods. However, no specific advanced statistical knowledge is required.

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