Social populations are connected through people living in the same area, working or studying in the same organisation, or being in the same network. Substantive questions around these phenomena relate to area effects, organisational variations, and peer effects on individual outcomes. Here, multilevel modelling can often be used as a quantitative method. Students will build on the techniques presented in Quantitative Data Analysis, or for "with Quantitative Methods" students, on materials covered in QM2 and Advanced Regression (QM3). The necessary theory will be illustrated with real life examples, including key concepts in social network analysis. This course also has a practical element; students will use "R" software to carry out multilevel and network analyses.

10 x 2-hour lectures during a 10-week semester.

Day/time TBA.*

10 x 1-hour workshop labs during a 10-week semester - lecturer-led.

Day/time TBA *

NONE

NONE

Summative 1: 3 Individual problem sets at 500 words each plus statistical output - Worth 50% of course grade

Summative 2: 1 group project at 2,500 words plus statistical output - Worth 50% of course grade

Programme / course convenors and teaching staff will ensure that due consideration is given to students with disabilities, and that reasonable adjustments are implemented wherever possible, in consultation with relevant support services. 

A Flexible Assessment approach will be applied, in line with Disability Service recommendations. This includes:  

■ Consideration of alternative assessment formats where appropriate (e.g., adaptations to oral assessments) 

■ Implementation of reasonable adjustments on a case-by-case basis

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It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

The course aims to offer students a theoretical and practical presentation of multilevel and network analysis, which will thus enable them to examine social phenomena, both in published work, and in the students' own research. Students will be able to critically assess when the methods described in this course may be useful in helping to answer substantive research questions. Finally, the course has a practical aspect, with the aim to enable students to use R software to carry out their own multilevel and network analysis.

By the end of this course students will be able to:

■ Select and Apply multilevel and network methods appropriate to the research questions and understand their limitations.

■ Conduct analyses of multilevel and network datasets using R software.

■ Effectively communicate statistical findings through both written explanations and appropriate graphical representations.

■ Critically evaluate and synthesise published research on multilevel and network analysis within a relevant social science context.

■ Design and justify the application of multilevel and network analysis within the context of students' own research project.