Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11099)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
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 semester long course is taught using a combination of lab and instruction sessions and is suitable for students following Masters programmes in Psychology and Linguistics. It starts with an introduction to basic statistics and the basics of R, and will give students competence in the standard univariate methodology and analysis using R. |
| Course description |
Univariate Statistics and Methodology in R (USMR) is a semester long crash-course aimed at providing Masters students in psychology with a competence in standard statistical methodologies and data analysis using R. Typically the analyses taught in this course are relevant for when there is just one source of variation - i.e., when we are interested in a single outcome measured across a set of independent observations. The first half of the course covers the fundamentals of statistical inference using a simulation-based approach, and introduces students to working with R & RStudio. The latter half of the course focuses on the general linear model, emphasising the fact that many statistical methods are simply special cases of this approach. This course introduces students to statistical modelling and empowers them with tools to analyse richer data and answer a broader set of research questions.
Typical Syllabus:
* Introduction to the use of statistical methods in research.
* Introduction to R for statistics
* Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares.
* Linear regression: including diagnostics, transformation, different families of models.
* Multiple regression: extending linear regression to multiple IVs and including interactions, effects coding.
* The generalized linear model (GLM).
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is only open to MSc students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS), with priority given to Psychology students and those students with ESRC funding. PhD students and students outwith PPLS may audit the course depending on space - please contact the Course Organiser and the PPLS Teaching organisation pplspgoffice@ed.ac.uk for permission to enrol. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
156 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Quizzes 35%
Report (65%): Group based. Page/word count: Six A4 pages (approx. 2000 words, including figures and tables). |
| Feedback |
Summative feedback is available for all assessments. Written feedback is available for group-based reports and weekly homework quizzes. Opportunities for formative feedback through weekly office hours, online forums, labs and lectures |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- determine which statistical analyses are appropriate to the research designs of particular studies
- understand how a common framework unifies seemingly disparate data analysis methods
- use the R statistical programming language to analyse real data and interpret the outputs
- create any required graphs using R
- create reproducible statistical reports using modern technologies with R
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Reading List
Weekly open access readings will be provided via Learn each week. Additional readings that contain a list of indicative topics include: -Cetinkaya-Rundel, M. & Hardin, J. OpenIntro:Introduction to Modern Statistics. (https://openintro-ims.netlify.app/)
- Introduction to Modern Statistics, by Mine Çetinkaya-Rundel and Johanna Hardin.
https://openintro-ims.netlify.app/index.html
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | r,statistics |
Contacts
| Course organiser | Dr Martin Corley
Tel: (0131 6)50 6682
Email: Martin.Corley@ed.ac.uk |
Course secretary | Miss Mollie Fordyce
Tel:
Email: mfordyc2@ed.ac.uk |
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