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.
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)
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
- Practical workshops
- Computer sessions in R
Ian Brunton-SmithSee profile
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.
Maximum of 25 people. Please note, five participants will be students from our MSc Social Research Methods course.
Software and equipment
There are no formal entry requirements for this course.
You should have some knowledge of regression.
Fees and funding
Price per person:
£595Government and commercial sector applicants
£495Education and charitable sector applicants
Terms and conditions
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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.