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Semester 1

Statistical Modelling (SSPS10027)


Social and Political Studies





Normal Year Taken


Delivery Session Year



Visiting students must have passed at least 2 social science courses (i.e. Sociology, Politics/International Relations, Social Policy, Public Policy, Social Anthropology, Gender Studies, Women's Studies, Queer Studies, African Studies, American Studies) at grade B or above, INCLUDING some background in multivariate analysis as well as some knowledge of the statistical data analysis package R. We will only consider University/College level courses. Please note that spaces on SSPS 3rd year courses are limited so enrolment cannot be guaranteed. This course cannot be taken alongside Doing Social Research with Statistics (SSPS08007).

Course Summary

This course covers generalized linear models, some major statistical learning tools, and models for complex causal relationships, mainly in the context of social sciences. Lectures are combined with practical computer lab tutorials in order to illustrate the applications of the theoretical tools. The analysis is carried out using the statistical software environment R, which is freely available under the GNU General Public License.

Course Description

The course employs a hands-on approach through analysis using the statistical software R. The applications are mostly chosen from real social science research questions but examples from other disciplines like biology, medicine and engineering are also given. The course will provide a unifying framework for linear models through generalized linear models framework ad ├žntroduce some common learning algorithms. (Dimensionality reduction techniques such as PCA and factor analysis, clustering algorithms, and discriminant analysis will be discussed.) On top of the theoretical tools introduced, the course aims to equip students two other computational skills: data management and data visualization. R packages dplyr and ggplot2 will be introduced and used for these purposes. Topics typically covered include: Data Management and Visualization with R; Generalized Linear Models ; Unsupervised Learning (PCA/Explanatory Factor Analysis, Clustering) ; Supervised Learning (Discriminant Analysis).

Assessment Information

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

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