Course Catalogue

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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 *

Mandatory entry requirements in line with those generally required by the MA Social Science degree and having Mandatory entry requirements in line with those generally required by the MA Social Science degree and having completed, at grade D3 or above :

■ EITHER "Quantitative Methods in the Social Sciences" course (using codes: CEES3027; CEES4073; POLITIC3018; POLITIC4137; PUBPOL3010; PUBPOL4137; SOCIO3021; SOCIO4095.

■ OR for students on the "with Quantitative Methods" pathway the "Advanced Regression" (SPS4004) (Can be taken simultaneously or prior to the course)

■ OR if a student's degree path does not include any of the above but they have other quantitative/statistical methods training the course convenor may accept this as fulfilling the requirements of entry.

NONE

NONE

The assessment will consist of a portfolio made up of 4 x individual formative assessments throughout the semester - each with a 1,000 word limit, as follows:

1. Educational outcomes by organisation.

2. Subjective well being by area (public health/sociology)

3. Political Outcomes (voting intention/attitudes) by area

4. Conceptualising the multilevel analysis of social network data

The substantive themes of these four assessments align with the School of Social and Political Science's research on inequalities, comparative politics, urban futures, and health and wellbeing. Each formative assessment corresponds to a technique taught on this course.

The four individual formative assessments are then modified, given feedback from the course convenor, and are then combined to a portfolio with four parts, in an iterative process. This is the summative assessment, with a word limit of 4,000 (4 x 1000).

This portfolio-based summative assessment is worth 100%. If a student does not submit a formative assessment, they can still submit the final portfolio, but they will not benefit from the feedback on their formative work to make improvements to the final draft, and are therefore likely to attain a lower overall mark.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The course aims to offer students a theoretical and practical presentation of multilevel and network analysis, which will thus enable them to examine social and (public) health 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.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.

Students must regularly attend and participate in lectures and taught labs; undertake all aspects of the course work (including formative and summative assignments). If a student does not submit a formative assessment, they can still submit the final portfolio but they will not benefit from the feedback on their formative work to make improvements to the final draft, and are therefore likely to attain a lower overall mark.