Multilevel Modelling for Social Scientists
Attendance dates:March 2020: 16, 17, 18
Time commitment: 3 days
Stag Hill campus, University of Surrey, Guildford, Surrey GU2 7XH
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 module 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 module, 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 module, 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 module 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
- Group discussions
Ian is a quantitative social scientist with particular expertise in multilevel modelling, survey methodology and missing data. He has taught multilevel modelling workshops nationally and internationally.
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.
Software and equipment
There are no formal entry requirements for this module.
You should have some knowledge of regression.
Fees and funding
Price per person:
£595Government and commercial sector applicants
£495Education and charitable sector applicants
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