Undergraduate Course: Bayesian Statistics for Social Scientists (SSPS10016)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This 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.
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| 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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Modelling (SSPS10027)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | Course open to visiting students with a demonstrable understanding of regression modelling for continuous and categorical outcomes. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2017/18, Available to all students (SV1)
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Quota: 20 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 20,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
165 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| 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 (65%)
3. A annotated copy of the syntax the student used to complete the analysis underpinning their project report (10%).
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| 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.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- understand the principles and philosophical approach of Bayesian analysis
- understand the situations within social science where the use of Bayesian methods maybe appropriate, and those where they are not
- be able to create models in an appropriate software package
- be able to report and interpret the output of Bayesian models. Covering both substantive interpretation and issues of model fit
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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
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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)
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Paul Norris
Tel: (0131 6)50 3922
Email: p.norris@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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