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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Multi-Level Modelling in Social Science (PGSP11424)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryMultilevel models are 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 There are many situations where an improved analysis is obtained compared to single level models, both in terms of improved statistical fit to the data, and also improving our understanding of social structures and policy interventions versus individual-level analysis.

Potential advantages include:

- the scope for wider inference: for example in a study of school attainment, the different association between the outcome at individual, class, school and education authority can potentially be understood;
- similarly, the relationship between an outcome of interest for individuals, households and their area-level context can be analysed;
- 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 conducted using the Noteable service and the R Stan statistical modelling package, which is free to all users. Labs are also available in Stata for those that prefer it. Lectures are combined with practical sessions in order to reinforce concepts.
Course description Most of your learning will be in practical work. At the labs run in the weekly practical sessions at least one teacher will be available for consultation, and in addition I offer bookable office hours.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Core quantitative data analysis 1 and 2 (SCIL11009)
Prohibited Combinations Students MUST NOT also be taking Multi-Level Modelling in Social Science (SSPS10024)
Other requirements or MUST have passed equivalent pre-requisite course.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  40
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) There are three components to the assessment:

1) A formative assessment early in the course using single level regression. This allows students a chance to check their familiarity with relevant statistical software, and to get early feedback on the presentation of statistical results. Students will be offered online tuition in R prior to the course.

2) The mid-term coursework will involve students fitting, reporting and briefly interpreting a set of multilevel models which will use a continuous outcome. There will be a series of short questions to guide you through the type of analysis you need to complete. Models covered will include variance component, random intercept and random slope. Sections of the exercise will also be concerned with providing brief substantive interpretations of the results you present

3) An end-of-course practical project, worth 70% of the total marks for the course. Students are given access to a general social science dataset (such as the European Social Survey) which is suitable for multilevel analysis. Students are then asked to identify an appropriate dependent variable and develop a research question involving at least 5 independent variables. Alternatively, students may find their own data and research question. They are required to write a report of no more than 3250 words which answers their research question and uses at least one variance component model, one random intercept model and one random slope model.
Feedback Feedback is provided within three weeks.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically evaluate when the use of multilevel models is appropriate in social science research.
  2. Exercise autonomy in developing research questions than can be addressed using multilevel models.
  3. Estimate theoretically informed multilevel models in an appropriate software package .
  4. Interpret and communicate the results of multilevel models to a variety of audiences (including non-technical practitioners) with understanding of their limitations and assumptions.
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.
Additional Information
Graduate Attributes and Skills Advanced understanding of and competence with multilevel modelling.

Capacity to apply multilevel modelling to social scientific questions.
KeywordsNot entered
Course organiserDr Orian Brook
Course secretaryMrs Beth Richardson-Mills
Tel: (0131 6)51 1659
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