9am - 5:45pm
Monday 24 February - Wednesday 26 February 2020

Statistical Modelling in R

Throughout the course, the emphasis is on the underlying principles and uses of statistical models and not on the mathematical and statistical theory. It therefore gives participants a solid empirical grounding to be able to critically evaluate the findings from a wide range of quantitative social science research. In the accompanying workshops you will get hands on experience of estimating a number of different statistical models in R, engaging with important issues including how to select an appropriate model, assessing the adequacy of a fitted model (in comparison to alternative models) and the statistical and substantive interpretation of the results

from £395.00 to £595.00

University of Surrey


This course discusses some of the main statistical models available to researches in social sciences, combining this with practical model building experience and critique using R. Indicative content includes:

  • Designing and building statistical models to answer social science questions
  • The general linear model
  • Operationalising concepts and selecting variables
  • Interpreting results and finding the narrative

Hands on practical workshops will provide students with experience of:

  • Linear regression
  • Logistic regression
  • Multinomial regression
  • Poisson regression
  • Interaction effects and nonlinear relationships
  • Models for spatial data


On successful completion of this course, participants will be able to:

  • Have a critical awareness of the rationale and terminology of statistical modelling (C)
  • Be able to engage with existing quantitative research, highlighting its key strengths and weaknesses (C,K)
  • Have a comprehensive understanding of the logic of model development and testing         (C,K)
  • Be able to develop multiple regression, logistic regression, multinomial logistic and poisson regression models and critically evaluate the results (P,T)
  • Be able to clearly tabulate and present the results of regression outputs (P,T)

Key: C-Cognitive/Analytical; K-Subject Knowledge; T-Transferable Skills; P- Professional/ Practical skills


Professor Brunton-Smith is a quantitative social scientist with particular expertise in multilevel modelling, survey methodology, and missing data. He has more than 10 years experience teaching statistical modelling. For full details of his research interests and experience, see: https://www.surrey.ac.uk/people/ian-brunton-smith


No prior knowledge is required, but it is assumed that participants will have a basic understanding of regression. All computing workshops will be in R (https://www.r-project.org), using the GUI RStudio (https://www.rstudio.com). For a basic introduction to R for data manipulation and analysis, the following interactive workshops are recommended (https://tinyurl.com/IntroRSurreySC).


Intermediate (some prior knowledge)

*Participants on the course will include some students completing the MSc in Social Research Methods*

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