Guy Abel is the leader of the International Migration research group at the Asian Demographic Research Institute and Professor in the School of Sociology and Political Sciences at Shanghai University. He received China’s National 1000 Expert Award for Young Professionals in 2017 and Shanghai’s 1000 Foreign Expert Award in 2015. His research focuses on techniques for estimating migration patterns and applying statistical methods to better forecast components of population change.
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Abel, GJ, DeWaard, J, Ha, JT, Almquist, ZW. The form and evolution of international migration networks, 1990–2015. Popul Space Place. 2021; 27:e2432. https://doi.org/10.1002/psp.2432
Lee Fiorio, Emilio Zagheni, Guy Abel, Johnathan Hill, Gabriel Pestre, Emmanuel Letouzé, Jixuan Cai; Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data. Demography 1 February 2021; 58 (1): 51–74. https://doi.org/10.1215/00703370-8917630
📖Data Science for Social Sciences
This course focuses on the use of the R statistical language. Practical hands-on exercises will be emphasized throughout the course to build up participants R experience. No prior knowledge of R is necessary, although participants should be comfortable using computers to handle data sets in statistical software (such as SPSS or Stata) and spreadsheets (such as Excel). Upon completion of this course, students will be familiar with the R environment, its basic functions and some more advanced methods. We focus predominantly on the tidyverse suite of packages for importing data into R, manipulating data frames and different types of visualisations including creating maps and animations.
This course focuses on the use of building and fitting statistical models using R. Throughout the course we use demographic data sets from a number of countries around the world. In the first part of the course we review popular R packages for presenting exploratory data analyses. The second part of the course covers the Central Limit Theorem in the context of regression analysis. The third part of the course illustrates the Generalized Linear Model (GLM) framework, including standard linear regression, binomial logistic regression and Poisson count regression and their implementation in R. In the final part, multilevel/hierarchical models are discussed and fitted. Upon completion of this course students will be familiar with standard statistical modelling techniques for analysing data as well as displaying and interpreting results effectively.