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

Postgraduate Course: Statistical Modelling in the Social Sciences (PGSP11486)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course introduces a set of models that deal with various forms of dependent variables and that may be used for investigation of complex causal relationships, mainly in the context of social sciences. The models are introduced through the statistical learning techniques although links to generalised linear models are also considered. 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.

The use of quantitative models to investigate a wide variety of social outcomes, to consider the evidence for causal relationships between a set of explanatory variables and a variable of interest are likely to be useful across many social science subjects. This course provides a foundation for other specific advanced quantitative modules such as Longitudinal Data Analysis, Multilevel Modelling and Bayesian Statistics that provide logical extensions.
Course description There are three main objectives of this course:

1. Provide a unifying framework for linear models through statistical learning with reference to the generalized linear models framework.

2. Introduce some common learning algorithms. (Dimensionality reduction techniques such as PCA and factor analysis, clustering algorithms, and discriminant analysis will be discussed.)

3. Introduce specific models to deal with complex causal relationships (IV regression and selection model)

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
Statistical Learning
Generalized Linear Models
Unsupervised Learning (PCA/Explanatory Factor Analysis, Clustering)
Supervised Learning (Discriminant Analysis)

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Core quantitative data analysis 1 and 2 (SCIL11009)
Other requirements In order to take Statistical Modelling in the Social Sciences you must be competent in the use of a statistical package (SPSS, Stata or R) including the use of syntax to run analysis
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  25
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Eight weekly exercises (40% in total).
End-of-course practical project (60%)

The end of course assignment differs to the sister course at undergraduate level in that at level 11, students will be expected to undertake and convincingly communicate all aspects of the quantitative research process relating to a research question of their choice. They will be required to identify a secondary data source to apply the techniques covered on the course to address their research question. The project could be related to the MSc dissertation, PhD thesis, or something entirely separate. Informal one-to one meetings with students (or feedback on project plans) will provide additional support for students to undertake this level 11 assignment. In summary the level 11 assignment requires students to demonstrate not only an ability to apply and interpret complex models critically, but also the capacity to link together theory, data preparation, modelling choices and interpretation of statistical output in an intelligent and convincing way.
Feedback Verbal feedback will be given throughout the course.
Written feedback will be given on the assignments.
A completed and annotated version of the test will be published on Learn.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Cast social science research questions in an intelligent, critical and creative way using a theoretically informed statistical model and an appropriate social science dataset
  2. Critically apply a unified conceptual and mathematical understanding of linear models
  3. Exercise substantial autonomy to use the R software for data management, data analysis and data visualization including the use of syntax to record and reproduce analysis
  4. Critically analyse multidimensional data through dimension reduction, clustering and discriminant analysis
  5. Critically evaluate sophisticated statistical issues, including endogeneity and omitted variable bias, and the implications these carry for causal claims in the social sciences.
Reading List
Madsen, Henrik, and Poul Thyregod. Introduction to General and Generalized linear models. CRC Press, 2010.

James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning. Springer. 2013

Matloff, Norman. The Art of R Programming: A tour of statistical software design. No Starch Press, 2011.

Grolemung, F., Wickham Hadley. R For Data Science. 2016

Chang, Winston. R Graphics Cookbook. O'Reilly Media, Inc., 2012.

Madsen, Henrik, and Poul Thyregod. Introduction to General and Generalized Linear Models. CRC Press, 2010

Additional Information
Graduate Attributes and Skills Developing advanced quantitative skills and the capacity to use them in applied scientific context.
Generic cognitive skills relating to the research process (e.g. evaluation, critical analysis).
Communication, numeracy and IT skills.
Autonomy, accountability and working with others.
KeywordsNot entered
Course organiserDr Christos Vrakopoulos
Course secretaryMr Adam Petras
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