Statistical Modelling

Key information

Start date: 24 February 2020

Attendance dates:

February 2020: 24, 25, 26

Time commitment: 3 days


Stag Hill campus, University of Surrey, Guildford, Surrey GU2 7XH

Contact details:


This module investigates the underlying principles and uses of statistical models and not on the mathematical and statistical theory. It will give you a solid empirical grounding to be able to critically evaluate the findings from a wide range of quantitative social science research

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.

Learning outcomes

On successful completion of this module, you 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 and K)
  • Have a comprehensive understanding of the logic of model development and testing (C and K)
  • Be able to develop multiple regression, logistic regression, multinomial logistic and poisson regression models and critically evaluate the results (P and T)
  • Be able to clearly tabulate and present the results of regression outputs (P and T)


Code Description
C Cognitive/analytical
K Subject knowledge
P Professional/practical skills
T Transferable skills

Course content

This module elaborates on quantitative approaches to social science, 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.

Practical workshops will provide you with experience of:

  • Linear regression
  • Logistic regression
  • Multinomial regression
  • Poisson regression
  • Interaction effects and nonlinear relationships
  • Model fit and diagnostics
  • Missing data adjustments.

Learning and teaching methods

  • Lectures
  • Practical workshops in R
  • Group discussion and feedback

Course leader

Ian Brunton-Smith

Ian is a quantitative social scientist with particular expertise in multilevel modelling, survey methodology and missing data. 

Reading list

View our recommended reading list.

Software and equipment

All computing workshops will be in R, using RStudio. For a basic introduction to R for data manipulation and analysis, see our interactive workshops.

Entry requirements

There are no formal entry requirements for this module.

You should have some knowledge of regression.

Fees and funding

Price per person:


Government and commercial sector applicants


Education and charitable sector applicants



How to apply

You can apply for this module through our online store.

Apply now

Terms and conditions

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Further details of our terms and conditions will follow.


This online prospectus has been prepared and published in advance of the commencement of the course. The University of Surrey has used its reasonable efforts to ensure that the information is accurate at the time of publishing, but changes (for example to course content or additional costs) may occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for a course with us. Read more.