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 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.
Indicative topics include:
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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Five fortnightly exercises (40% in total)«br /»
End-of-course practical project (60%) |
| Feedback |
Five fortnightly exercises |
| 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 | Prof Lindsay Paterson
Tel: (0131 6)51 6380
Email: Lindsay.Paterson@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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