Undergraduate Course: Multi-Level Modelling in Social Science (SSPS10024)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | 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.
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| 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 package MLwiN (dedicated to multilevel modelling and available free to academics and university students).
Conceptual introduction to multi-level models.
Introducion to the software MLwiN.
Models with two levels.
Random slopes models/
Longitudinal models.
Models for binary and binomial data (1).
Models for binary and binomial data (2).
Models for multinomial data.
Models for ordinal data.
Models for multivariate outcomes.
Most of your learning will be in practical work. At each of the weekly practical sessions, two teachers will be available for consultation.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Modelling (SSPS10027)
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Multi-Level Modelling in Social Science (PGSP11424)
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Other requirements | For those students who are required to take a Quantitative Methods course as part of their degree programme, this course can be counted towards that condition. |
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: 20 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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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 3 out of 4 of your marks on practical exercises 2 to 5 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 scientfic questions which you will have to answer in multilevel ways.
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| Feedback |
There will be five fortnightly, practical exercises. The feedback from each of the exercises will contribute towards your learnng 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:
- Understand the conceptual and mathematical basis of multilevel models.
- Be able to use the software MLwiN and to link it to other software
- Be able to cast scientific questions in multilevel terms.
- Be able to interpret and communicate the results of multilevel models clearly.
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Additional Information
| Graduate Attributes and Skills |
Developing advanced quantitative skills and the capacity to use them in applied scientific context. |
| Keywords | Multi-level modelling; regression; longitudinal data. |
Contacts
| Course organiser | Dr Paul Norris
Tel: (0131 6)50 3922
Email: p.norris@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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