ESRC Summer School: Analysing Segregation
Applied Statistical Methods for Analysing Segregation & Inequality
1-4 August 2017, The University of Sheffield
About the Summer School
Significant advances in the measurement and analysis of segregation have been made over the past 5 years. This summer school offers delegates the opportunity to learn these cutting-edge methods from leading researchers in the field.
The summer school will provide 4 days of intensive hands-on training, providing an intensive introduction to measuring and modelling segregation. Participants will gain an understanding of, and develop their skills in, the following four topics:
1. Introduction to R – reading in data, plotting data, variables and subsetting, data manipulation and management, numerical summaries, reading in shape files, drawing maps of areal unit data.
We have an outstanding set of trainers and speakers including:
• Dr Nema Dean (University of Glasgow) – statistician with expertise in social network analysis and cluster modelling; and pioneer in the development of perceived substitutability approaches to segregation using social network analysis.
Who is the Summer School for?
This course is aimed at researchers interested in the quantitative analysis of segregation using the latest methods. The course is open to academics, postgraduate students and researchers outside of academia interested in improving their skills in this area. Most of the course will be taught in R and other freely available software. No prior knowledge of R is required, but you should have a good knowledge of introductory statistics and regression analysis – see “how to apply” below. Lunch and some evening meals will be provided.
The course will be preceded by a one-day conference on segregation in Europe and China hosted by the University of Sheffield to which you are warmly invited. For further details email firstname.lastname@example.org.
Both the conference and the Summer School are funded by the ESRC in collaboration with the Sheffield Methods Institute.