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

Postgraduate Course: Bayesian Statistics for Social Scientists (PGSP11553)

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 covers the basics of Bayesian analysis, drawing on a range of examples from across the social sciences. It covers the philosophical reasons for employing Bayesian methods and discusses their limitations. Attention is brought to practical issues related to the estimation of different types of models. Models covered include, comparisons of means (t-tests) and single level regression models for different types of outcome variables, along with overviews of more advanced issues including multilevel modelling, latent variable models, and missing data.

Analysis is undertaken using the R statistical environment and the teaching of all statistical models is rooted in specific social science related examples.
Course description This course provides a practical introduction to the use of Bayesian statistical methods within social science.

The course begins by considering how a Bayesian approach to statistics varies from the more common Frequentist interpretation. As well as exploring the philosophical differences between the traditions, the strengths and weaknesses of each approach is discussed with particular reference to their application across the social sciences.

The course then considers how Bayesian methods can be implemented with regards to different generalised linear models (covering continuous, binary, ordinal and count outcomes). The central focus of this work is on constructing and interpreting models, illustrated by examples relevant to a range of social science disciplines.

The course concludes by considering more advanced statistical models, which are generally not easily estimated without using a Bayesian approach. These include multilevel models, latent variable models and models with missing data.

The course is practical in nature with students fitting models using the open source software packages R and Stan - a familiarity with R syntax for fitting glm models will be useful, but is not assumed.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Be able to critically discuss the principles and philosophical approach of Bayesian analysis;
  2. Be able to identify appropriate modelling strategies for a range of social science related research questions;
  3. Be able to execute Bayesian analysis in an appropriate software package;
  4. Be able to understand and report Bayesian model output in a way which will be appropriate to both technical and non-technical audiences .
Reading List
Kaplan, D (2014). Bayesian Statistics for the Social Sciences. Guildford Press: New York (this would be the nearest to a course textbook for the course).

McElreath, R (2016). Statistical Rethinking - A Bayesian Course with Examples in R and Stan. CRC Press: London

Kruschke, J (2015). Doing Bayesian Data Analysis. 2nd Edition. Academic Press: London.

Jackman, S (2009). Bayesian Statistics for the Social Sciences. John Wiley: Chichester
Additional Information
Graduate Attributes and Skills Plan and carry out a research project and report its findings appropriately (Skills and abilities in research and enquiry)

Be independent learners who take responsibility for their own learning and are committed to continuous reflection, self-evaluation and self-improvement (Skills and abilities in personal and intellectual autonomy)

Make effective use of oral, written and visual means to critique, negotiate, create and communicate understanding (Skills and abilities in communication)

Use visualisation of quantitative data to convey complex evidence to a general audience (Skills and abilities in communication)

Be able to work effectively with others, capitalising on their different thinking, experience and skills (Skills and abilities in personal effectiveness)

Use of Statistical Package for the Social Sciences (SPSS) and other data processing software and word processing packages (Technical/Practical Skills)

Presentation skills and using presentation software (Technical/Practical Skills)
KeywordsNot entered
Course organiserDr Paul Norris
Tel: (0131 6)50 3922
Course secretaryMrs Beth Richardson-Mills
Tel: (0131 6)51 1659
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