Postgraduate Course: Multi-Level Modelling in Social Science (PGSP11424)
|School||School of Social and Political Science
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||Multilevel models are becoming an increasingly popular method of analysis in many areas of social science, medicine and natural science, and there are many situations where an improved analysis is obtained compared to conventional methods such as ANOVA or multiple regression. Potential advantages include:
- the scope for wider inference: for example in a study of school attainment, results can be related to a population of schools rather than just those assessed;
- more appropriate mean estimates, when the effect of spurious outlying results for small groups are reduced;
- a more efficient analysis with smaller standard errors, particularly when there are few observations per group;
- avoidance of problems caused by missing outcomes: this is an advantage in longitudinal studies (for example panel studies) where there are often dropouts;
- use of more appropriate variances and correlations: for example in a longitudinal analysis the correlation between observations on the same person may become less for measurements that are further apart in time.
The course enables students to understand and use multilevel models mainly in the context of social science, but examples are also given from medicine and some aspects of biological science. The focus is on multilevel models for quantitative, binary and multinomial outcomes, with shorter sessions on models for ordinal and count outcomes. The importance of multilevel modelling for longitudinal data is explained. Analysis is illustrated using the package MLwiN (dedicated to multilevel modelling and available free to academics and university students). Lectures are combined with practical sessions in order to reinforce concepts.
The course enables students to understand and use multilevel models mainly in the context of social science. The focus is on multilevel models for quantitative, binary and multinomial outcomes, with shorter sessions on models for ordinal and count outcomes. The importance of multilevel modelling for longitudinal data is explained. Analysis is illustrated using the package MLwiN (dedicated to multilevel modelling and available free to academics and university students). Lectures are combined with practical sessions in order to reinforce concepts.
The distinctive feature of the level 11 course will be five one-hour extra practicals, starting around week 4, in which, under the guidance of course staff, MSc students will present results from multilevel analysis. Honours students may take part in these practicals, but are not required to do so.
The topics of weekly sessions are:
Introduction to multilevel models
Multiple regression in a multilevel framework
Random slopes in multiple regression
Residuals and model diagnostics
Predictions from multilevel models
Multilevel binary logistic regression
Multilevel multinomial logistic regression
Models for multivariate outcomes
Models for longitudinal data
Model fitting methods
Other data structures
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2015/16, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 26,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Five fortnightly exercises (40% in total); end-of-course practical project (60%). «br /»
The MSc students will be expected to work at level 11 in the practical project.«br /»
|No Exam Information
On completion of this course, the student will be able to:
- Critically understand the conceptual and mathematical basis of multilevel models. In particular being able to understand the relationship between mathematical ideas and substantive social scientific questions.
- Use the software MLwiN and to link it to othersoftware.
- Cast scientific questions in multilevel terms.
- Interpret, critically evaluate and communicate the results of multilevel models clearly in writing.
- Communicate the results of multilevel models clearly in writing, and to debate these results orally, paying attention both to mathematical and substantive questions.
|Dale, A. and Davies, R. (1994), Analyzing Social and Political Change, London: Sage.|
Goldstein, H (2011) Multilevel Statistical Models (fourth edition), London: Edward Arnold.
Quintelier, E. (2010), ¿The effect of schools on political participation: a multilevel logistic analysis¿, Research Papers in Education, 25, pp. 137-154.
Snijders, T and Bosker, R (1999), Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modelling, London: Sage Publishing.
Steenbergen, M. and Jones, B. (2002), ¿Modeling multilevel data structures¿, American Journal of Political Science,46, pp. 218-237.
Sun, L., Bradley, K. D. and Akers, K. (2012), ¿A multilevel modelling approach to investigating factors impacting science achievement for secondary school students: PISA Hong Kong sample¿, International Journal of Science Education, 34, pp. 2107-2125.
Whitworth, A. (2012), ¿Inequality and crime across England: a multilevel modelling approach¿, Social Policy and Society, 11, pp 27-40.
|Graduate Attributes and Skills
||Advanced understanding of and competence with multilevel modelling.
Capacity to apply multilevel modelling to social scientific questions.
|Course organiser||Prof Lindsay Paterson
Tel: (0131 6)51 6380
|Course secretary||Mr Andrew Macaulay
Tel: (0131 6)51 5067
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:40 am