Course Catalogue

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In this optional course students will build on the techniques presented in Honours "Quantitative Methods in the Social Science" and (for students on the "with quantitative methods"-pathway) Advanced Regression (SPS4004).  The course will help students expand their exposure to a range of methods that allows them to answer more complex questions than a basic OLS regression model. The course will examine differences and patterns with a focus on themes connected to: inequality, welfare, health, crime and conflict using models that help address problems such as: missing data, data that engage with elements of time, nonlinear patterns and grouped data.

10 x 1 hour lectures during a 11 week semester - lecture led.

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

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 "QM1 Measuring your social world" (SPS1001) and "QM2 Analysing your social world" (SPS2001).

■ 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.

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The assessment will consist of two practical projects, both worth 50%. These projects will involve being given datasets and asked to fit and interpret appropriate models to answer questions related to social and political issues. The projects will allow the students to demonstrate their ability to choose appropriate models for correlated responses, to fit such models in R, and to interpret their analysis in an appropriate format.

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 increase training on advanced statistical methods, to prepare students for quantitative dissertation work using complex data such as large datasets from administrative surveys, to provide students with relevant social research skills useful for future employment, and to support students in developing critical thinking in relation to model selection.

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

■ Demonstrate a critical awareness of when different statistical techniques are appropriate and their limitations.

■ Apply appropriate statistical models to conduct advanced statistical analyses of relationships on datasets using R software.

■ Assess model performance.

■ Interpret the results of statistical analyses conducted in R.

■ Communicate findings to audiences from different backgrounds.

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