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Semester 2

Multi-Level Modelling in Social Science (SSPS10024)

Subject

Social and Political Studies

College

CAHSS

Credits

20

Normal Year Taken

3

Delivery Session Year

2023/2024

Pre-requisites

Visiting students must have completed at least 2 Social Science courses (i.e. Sociology, Politics, Social Policy, Social Anthropology, African Studies, American Studies, Gender/Queer Studies) at grade B or above, including background in multivariate analysis as well as knowledge of the statistical data analysis package R. We will only consider University/College level courses. Please see Additional Restrictions below.

Course 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 conducted using the Noteable service and the R Stan statistical modelling package, which is free to all users. 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. 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 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 conducted using the Noteable service and the R Stan statistical modelling package, which is free to all users. Most of your learning will be in practical work. As well as the labs run in the weekly practical sessions, a teacher will be available for consultation, and I offer bookable office hours.

Assessment Information

Written Exam 0%, Coursework 100%, Practical Exam 0%

Additional Restrictions

Students cannot take this course alongside Statistical Modelling (SSPS10027).

view the timetable and further details for this course