Postgraduate Course: Multi-Level Modelling in Social Science (PGSP11424)
Course Outline
| 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 |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| 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. |
| Course description |
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 analysis of variance 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 you 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 further sessions on models for ordinal and count outcomes. The importance of multilevel modelling for longitudinal data is explained. Analysis is illustrated using the statistical package Stata.
Most of your learning will be in practical work. At each of the weekly practical sessions, two teachers will be available for consultation. There are also fortnightly practical sessions devoted to the presentation of the results of multilevel models.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2018/19, Available to all students (SV1)
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Quota: 10 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 26,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
160 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
There are two components to the assessment:
(1) The average of your best 2 out of 3 of your marks on practical exercises 2 to 4 will be used to calculate 40% of your final mark for the course.
(2) There will be an end-of-course practical project, worth 60% of the total marks for the course. You will be given a choice of one of three data sets to analyse, and some questions about each of them. You will have to apply the techniques which you have learnt in the course to answer the questions. Some of the questions will relate to specific, technical points about understanding multilevel models and the software. The main focus of the project will be on social scientific questions which you will have to answer in multilevel ways. |
| Feedback |
There will be five fortnightly, practical exercises. The feedback from each of the exercises will contribute towards your learning and your work on the subsequent exercises and in the final assessment. These exercises will concentrate on your technical skills, testing whether you have understood the concepts of multi-level modelling and whether you have learnt to use the software. |
| No Exam Information |
Learning Outcomes
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 Stata to estimate multilevel models.
- Cast scientific questions in multilevel terms.
- Interpret and critically evaluate 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.
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Reading List
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.
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Additional Information
| Graduate Attributes and Skills |
Advanced understanding of and competence with multilevel modelling.
Capacity to apply multilevel modelling to social scientific questions. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Paul Norris
Tel: (0131 6)50 3922
Email: p.norris@ed.ac.uk |
Course secretary | Mr Jack Smith
Tel: (0131 6)51 1485
Email: Jack.Smith@ed.ac.uk |
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