Vst.-Nr.: 32312e
Mi, 24.06 um 08:00 - 12:00 c.t ALFI 319
Do, 25.06 um 08:00 - 18:00 c.t, Vielberthgebäude - VG 1.36
Fr, 26.06 um 08:00 - 18:00 c.t, Vielberthgebäude - VG 0.24
EDU-BA-M05.1
EDU-BA-M05.2
EDU-M 08.1
Lecturer: Dr. Jasperina Brouwer
Content overview
In this course, the foundations of (longitudinal) social network analysis will be covered. Participants will learn how they can set up a (longitudinal) social network study: theoretical background of social networks, research questions at different levels, data collection and ethical concerns, metrics and analysis, and conclusions. They get an overview over the current methods that are used in this field and what kind of questions you could explore. For instance, often it is not just the attributes that matter for learning in school or work contexts, but also the relationships between the learners.
In sum, the course shows the necessary tools and ideas that allow participants to conceptualize and execute a social network study on their own. By practical assignments and in-depth discussions, the lecturer will support them in finding answers to the following questions: Why use social network analysis? What social network-research question to ask? How to get social network data? What are the options for analyzing social network data?
Participants need to bring their own laptops (any Operating System) and, please, install R and R studio before the course.
Learning goals
After finishing the course, the participants are expected to:
- be able to explain when and why social network analysis should be applied rather than conventional statistical techniques
- be able to formulate research questions at different levels (node, dyadic, and network level)
- be able to design a social network study which can be used in their own PhD research;
- have insight in the ethical concerns related to social network research, e.g., data collection
- know how to import the network files and the attribute files in R
- can explain and calculate some network descriptives in R and draw preliminary conclusions
- be able to make a graphical presentation of social network data
- apply the content of the course to their own research project by asking critical questions and take part in discussions and assignments.
Preparation
Specific prior knowledge with regard to social network analysis is not required, but a thorough study of the preparatory literature is necessary. In advance, think critically about a social network study.
Literature (preparation):
Borgatti, S. P., Mehra, A., Brass, D. J., Labianca, G. (2009). Network analysis in the social sciences. Science, 323, 892-895
Kadushin, C. (2004). Introduction to Social Network Theory. Chapter 2. Some Basic Network Concepts and Propositions
Sweet, T. M. (2016). Social network methods for the educational and psychological sciences. Educational Psychologist, 51(3–4), 381–394.
Programme
Interactive lectures: assignments in small groups or pairs, discussions and providing information will be alternated.
Hands-on assignments in R
“Pecha Kucha” = 7 minutes presentation. During the course, the lecturer will guide you through the process from designing a network study until calculating some preliminary finding based on the network descriptives. At the second day, you will present this in a Pecha Kucha.
Dies ist eine parallel angebotene Veranstaltung. Bei diesen Kursen wird die Anmeldung über den "Anmeldekurs bei Parallelveranstaltungen" (ganz oben in der Kursübersicht des jeweiligen Semesters zu finden) organisiert
