Training and Events
Advanced Training in R
|University of Essex|
Dominik Duell is a Lecturer in the Government Department and teaches quantitative methods on the undergraduate and post-graduate (summer school) level. He studies voting behaviour mostly using experimental methods. In his research and teaching, he uses R and Stata every day.
27/03/2020 - 15/05/2020
University of Essex
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The course takes place on 27 March, 3 April and 15 May (9am-5pm).
This course aims to pick up students at their level of acquired skills and give them training in implementing more advanced statistical models in R but in the area of statistical modelling relevant to their dissertation.
This is a course on advanced use of R. The course will first review basic R tools for data manipulation and plotting (Base R and tidyverse) as well as programming (i.e., writing programs, loops, simulations). Then, the main part of the course focusses on the implementation of various statistical methods (e.g., basic and advanced regression models, parametric and non-parametric test procedures, predictive models, latent variable models) on a wide variety of data types (cross-sectional, panel, time-series, hierarchical data) as well as Monte Carlo simulations and bootstrapping inference. Finally, it gives guidelines how to table and visualise results from statistical estimation for publication. Cornerstone of the seminar is the two-part schedule: In two training sessions, learning material is introduced through a series of exercise g.on training data. Then, the course meets again 6 weeks later to allow participants to implement newly learned skills in their research projects in the meantime. The second part gives room for a discussion of problems encountered while participants tried to apply the newly learned skills to their own research projects and to receive feedback on their coding practice from the teacher and other participants. The teacher and a teaching assistant will be available by email between the first and second part of the course to answer questions and give feedback on participants’ coding.
Participants should know how to properly set up a working environment for using R efficiently (e.g. creating R projects, installing/loading packages) as well as have a basic understanding of running standard statistical models and visualisation in R.
Intermediate (some prior knowledge)
External academics, students and not-for-profit organisations - £300
Website and registration
East of England
ICT and Software, Quantitative Software, R, Research Management and Impact, data manipulation and plotting (Base R and tidyverse) , programming (i.e., writing programs, loops, simulations). , statistical methods (e.g., basic and advanced regression models, parametric and non-parametric test procedures, predictive models, latent v
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