Postgraduate Course: Statistical Modelling in the Social Sciences (PGSP11486)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This 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.
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| 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)
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Core quantitative data analysis 1 and 2 (SCIL11009)
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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
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Cast social science research questions in an intelligent, critical and creative way using a theoretically informed statistical model and an appropriate social science dataset
- Critically apply a unified conceptual and mathematical understanding of linear models
- 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
- Master the selection and analysis of Generalized Linear Models (GLMs), including handling overdispersion and quantifying uncertainty in model predictions and parameters.
- Critically analyse multidimensional data through dimension reduction.
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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
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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.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Christos Vrakopoulos
Tel:
Email: Christos.Vrakopoulos@ed.ac.uk |
Course secretary | Mr Adam Petras
Tel:
Email: Adam.Petras@ed.ac.uk |
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