Multilevel Modelling for Social Scientists

Key information

Start date: To be confirmed

Attendance dates:

To be confirmed

Time commitment: 3 days

Venue:

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

Contact details:

Overview

Complex structures exist in the social world and can influence the experiences of individuals, for example:

  • The school you attend can have an impact on the grades you achieve and future life chances.
  • Life expectancy varies dramatically across neighbourhoods, even in the same city.

This course introduces statistical methods for dealing effectively with these types of data structures, enabling us to make robust inferences about the effects of groups, individuals, and the effects of being in a particular group on different individuals.

You will start by covering some of the basic concepts in multilevel modelling and the fundamentals of random intercept and random coefficient models. You will then move on to consider more advanced topics including: nonlinear models for binary responses, repeated measures and cross-classified models. Throughout the course, you will be exposed to the practical issues involved in multilevel modelling and the critical interpretation of results, rather than on the underlying statistical derivations.

Learning outcomes

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

  • Have a critical understanding of the ideas behind multilevel modelling, and to know when their use is appropriate (C and K)
  • Be able to fit multilevel models to continuous and binary response data (C and P)
  • Have a comprehensive understanding of more advanced topics including binary response models,  and methods for longitudinal data (K)
  • Be able to engage with existing research studies using multilevel models, highlighting their key strengths and weaknesses (C and T)
  • Be able to interpret the results from multilevel models critically (C and T)

Attributes

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

Course content

This course provides a thorough discussion of multilevel models and demonstrates how they can be deployed to answer social science questions.

Indicative content includes:

  • Multilevel data structures
  • Random intercept models
  • Random coefficient models
  • Context effects and cross-level interactions
  • Multilevel models for binary responses
  • Longitudinal modelling
  • Cross-classified data structures.

Practical workshops will provide you with experience of:

  • Fitting multilevel models to real world data
  • Models designed to deal with linear and binary responses
  • Models for cross-classified data structures
  • Analysing longitudinal data.

Learning and teaching methods

  • Lectures
  • Practical workshops
  • Computer sessions in R

Course leader

Ian Brunton-Smith

See profile

Reading list

Brunton-Smith, I. and Sturgis, P. (2011) ‘Do neighborhoods generate fear of crime?: An empirical test using the British Crime Survey’. Criminology, 49(2), pp.331-369.

Hox, J J. (2002) Multilevel analysis: Techniques and applications, 2nd ed. Routledge. Chapters 1 and 2.

Class size

Maximum of 25 people. Please note, five participants will be students from our MSc Social Research Methods course.

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.

Software background

Entry requirements

There are no formal entry requirements for this course.

You should have some knowledge of regression.

Fees and funding

Price per person:

£595

Government and commercial sector applicants

£495

Education and charitable sector applicants

£395

Students

How to apply

Applications for this course are currently closed.

Register your interest

Terms and conditions

When you accept an offer of a place at the University of Surrey, you are agreeing to comply with our policies and regulations and our terms and conditions. You are also confirming you have read and understood the University's prospective student privacy notice.

Further details of our terms and conditions will follow.

Disclaimer

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.