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DRPS : Course Catalogue : School of Social and Political Science : School (School of Social and Political Studies)

Undergraduate Course: Statistical Modelling (SSPS10027)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis 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)
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Doing Social Research with Statistics (SSPS08007)
Prohibited Combinations Other requirements For those students who are required to take a Quantitative Methods course as part of their degree programme, this course can be counted towards that condition.

For PPE students, Statistical Methods for Economics can replace the pre-requisite of Doing Social Research with Statistics.
Information for Visiting Students
Pre-requisitesVisiting students must have completed at least two Social Sciences courses at grade B or above previously, including some background in multivariate analysis as well as some knowledge of the statistical data analysis package R
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 9, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 167 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Eight weekly exercises (40% in total)
Timed assignment (60%)
Feedback The feedback and guidance will be provided during the course convenor's weekly office hours and by appointment. For the timed assignment, feedback will be provided in written form.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Have a unified conceptual and mathematical understanding of linear models
  2. Be able to use the R software for data management, data analysis and data visualization.
  3. Be able to analyze multidimensional data through dimension reduction, clustering and discriminant analysis.
  4. To appreciate the uses and limits maximum likelihood estimation.
  5. Be able to deal with particular endogeneity and omitted variable bias problems.
Reading List
Additional Information
Graduate Attributes and Skills Developing advanced quantitative skills and the capacity to use them in applied scientific context.
KeywordsStatistical analysis; regression; generalized liner models; statistical learning; R
Course organiser Course secretaryMr Ian McClory
Tel: (0131 6)50 3932
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