MULTILEVEL MODELLING FOR SOCIAL SCIENTISTS - 2023/4
Module code: SOCM057
Complex structures exist in the social world and can influence the experiences of individuals – for example, the school you attend can have an impact on the grades you achieve and future life chances, and life expectancy varies dramatically across neighbourhoods, even in the same city. This module introduces statistical methods for dealing effectively with these types of data structures, enabling us to make robust inferences about the effects of groups, individuals, and the effects of being in a particular group on different individuals.
We will start by covering some of the basic concepts in multilevel modelling and the fundamentals of random intercept and random coefficient models. We will then move on to consider more advanced topics including: nonlinear models for binary responses, repeated measures, and cross-classified models. Throughout the course, the emphasis will be on the practical issues involved in multilevel modelling and the critical interpretation of results, rather than on the underlying statistical derivations.
BRUNTON-SMITH Ian (Sociology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G310
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 20
Independent Learning Hours: 105
Guided Learning: 15
Captured Content: 10
Prerequisites / Co-requisites
Students must have completed SOCM010 (Social data analytics), or be able to demonstrate that they have received an equivalent level of quantitative methods training
This module provides a thorough discussion of multilevel models and demonstrates how they can be deployed to answer social science questions. The module content will cover:
- Multilevel data structures
- Random intercept models
- Random coefficient models
- Context effects and cross-level interactions
- Multilevel models for binary responses
- Longitudinal modelling
- Cross-classified data structures
Hands on practical workshops will provide students with experience of:
- Fitting multilevel models to real world data
- Models designed to deal with linear and binary responses
- Models for cross-classified data structures
- Analysing longitudinal data
|Assessment type||Unit of assessment||Weighting|
|Coursework||Two-part multilevel modelling exercise (4000 words)||100|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
A detailed understanding of how to use multilevel models to account for complex data structures. Using real data, students will estimate random intercept and random coefficient models, requiring them to make appropriate modelling decisions and to correctly interpret the model output. They will also demonstrate their ability to judge the fir of their models, and discuss their implications.
Thus, the summative assessment for this module consists of:
A two part analytic report (4,000 words) where students will conduct multilevel modelling and write up their results.
Formative assessment and feedback
Students will be able to complete workshop exercises during days 1-3 of the short-course, giving them the chance to discuss the answers with each other and the lecturer. Day 4 of the course is devoted to independent study, where students will be able to make initial progress on their assignment. All students will be invited back to the classroom on day 5 where there will be additional opportunities to receive formative feedback from the lecturer and other students.
- • Provide a clear description, definition, and discussion of multilevel modelling, including the sorts of data structures it is appropriate for
- • Give students a thorough understanding of random intercept and random coefficient models, and discuss how results from these models should be interpreted
- • Enable students to critically evaluate the findings of existing studies using multilevel modelling, recognising the strengths and weaknesses of the approach
- • Introduce students to a number of advanced multilevel approaches, including models for longitudinal data, variance functions, and cross-classified models
- • Provide practical experience of estimating multilevel models in R
|001||Have a critical understanding of the ideas behind multilevel modelling, and to know when their use is appropriate|
|002||Be able to fit multilevel models to continuous and binary response data|
|003||Have a comprehensive understanding of more advanced topics including binary response models, cross-classified data structures, and methods for longitudinal data||K|
|004||Be able to engage with existing research studies using multilevel models, highlighting their key strengths and weaknesses|
|005||Be able to interpret the results from multilevel models critically|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Provide students with a thorough understanding of when and how to use multilevel modelling to account for clustered data structures. Alongside engagement with the theoretical underpinnings of these methods, the hands on workshops will give students practical experience of using multilevel models, and how they should be applied. Students will also learn to correctly interpret the results from multilevel models and draw inferences about the social world.
The learning and teaching methods include:
- Practical workshops
- Group discussions
This module is taught intensively during one-week. Days 1-3 will consist of a combination of lectures and hands on practical sessions fitting multilevel models in R (www.r-project.org). Day 4 is devoted to independent study, allowing students to undertake preparatory work on their assignment. Finally, on day 5 students will get the opportunity to receive formative feedback on their initial assignment plans and peer feedback during group discussion. Students will then complete their practical assignment.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: SOCM057
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.