Course Summary: This 5-day short course, which will take place 10-14 of June 2019, will serve as an introduction to the theory and practice of social and ecological network analysis. Whereas standard statistical analyses assume that data on different entities (e.g., people, organizations, animals) are independent, network analysis also describes the relationships among entities to explain why a particular configuration of relationships is observed, and how the structure of a network explain emergent properties of the social and/or ecological system under investigation. Although network science has a long tradition, the field has recently exploded with the advent of new data resources and computational methods, particularly with regards to the application of network analysis to socio-environmental systems.
The course will begin with an introduction to networks, and then cover a variety of techniques used to analyze social and ecological networks, including examples from the literature and hands-on practicals. The focus of the last two days will be how to develop questions about networks in a socio-environmental context and testing them appropriately. Participants are encouraged to bring their own data. There will be reserved time each day to apply new concepts to your own datasets. For those without data on hand, the instructor will help you find interesting datasets to practice on during the course.
Target Audience: This is a foundational course for anyone interested in adding network analysis to their methodological toolkit, regardless of prior experience. Applicants whose research or teaching involves the social or biophysical components of environmental problems will be given preference, but applicants with other areas of interest are also welcome. The course material is intended for students who have heard of network analysis and want to apply it in their own work, but lack hands-on training. Preliminary readings will be made available to participants. The target class size is 12-15, so space is limited and competition for places is expected. Some experience with computer programming, ideally with the R language, is expected, as well as an understanding of basic statistics (distributions, hypothesis testing, and regression).
● What is network data? What are the problems in collecting it? What kinds of questions can we use it to answer? How is it different from other data?
● structural & locational properties of actors/locations/resources (centrality, prestige, & prominence to determine popular resources, organizations, etc.), structural cohesion (subgroups & cliques), equivalence of actors (structural equivalence & block models to determine niche differentiation or social isomorphism), local analyses (dyadic & triadic analysis, brokerage to determine structural hierarchies and key resources or actors), community detection algorithms
● matrix permutation tests, conditional uniform random graphs, network autocorrelation models, introduction to statistical global analyses (p1, p*, ERGMs, & their relatives), temporal models
For questions, please contact Dr. Lorien Jasny at L.Jasny@exeter.ac.uk.
The University of Maryland is an Equal Opportunity Employer.
Minorities and Women Are Encouraged to Apply.