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

Undergraduate Course: Bayesian Statistics for Social Scientists (SSPS10016)

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 will cover the basics of Bayesian analysis, drawing on a range of examples from across the social sciences. It will cover the philosophical reasons for employing Bayesian methods and discussion of their limitations. Attention will then turn to practical issues related to the estimation of different types of models. Models covered will 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 will be undertaken using the R statistical environment and the teaching of all statistical models will be 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 will begin 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 will be discussed with particular reference to their application across the social sciences.
The course will then consider 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 will be on constructing and interpreting models, illustrated by examples relevant to a range of social science disciplines.
The course will conclude by considering more advanced statistical models, which are generally not easily estimated without using a Bayesian approach. These will include multilevel models, latent variable models and models with missing data.
The course will be 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 Students MUST have passed: Statistical Modelling (SSPS10027)
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.
Information for Visiting Students
Pre-requisitesCourse open to visiting students with a demonstrable understanding of regression modelling for continuous and categorical outcomes.
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework submissions will be as follows:-

1. A mid-semester guided assignment where students are required to run, and report, some simple analysis relating to specific variables. (25%)

2. A project report. Student complete a 2500 word project report analyzing a dependent variable of their choice from the datasets used on the course (and considering at least 5 explanatory variables). This report will include a rationale for their chosen statistical method, along with presentation and interpretation of relevant statistical results. This will include an annotated copy of the syntax the student used to complete the analysis underpinning their project report (75%)

Feedback Students will receive written feedback on the mid-term assignment within 15 working days.

In addition, they will receive verbal feedback on the non-assessed group presentations in weeks 9 and 10 of the course, and there will be weekly non-assessed multiple choice tests at the start of each lab session to allow students to evaluate their understanding of key concepts.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand the principles and philosophical approach of Bayesian analysis
  2. understand the situations within social science where the use of Bayesian methods maybe appropriate, and those where they are not
  3. be able to create models in an appropriate software package
  4. be able to report and interpret the output of Bayesian models. Covering both substantive interpretation and issues of model fit
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 secretaryMr Ethan Alexander
Tel: (0131 6)50 4001
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