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 is open only to those students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS). Students outwith PPLS may contact the Course Organiser to query if any space is available after week 2. |
| 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 | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: 110 |
| 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 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Quizzes 35%
Group Report 65% |
| Feedback |
Formative feedback is provided throughout the course during discussions and guidance in practical sessions. |
| 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 R and markdown
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Reading List
This course does not directly follow a single text. An online workbook is provided in which readings, explanations of analyses, and key concepts, and exercises are given. For further, more in depth reading, we recommend the following books (freely available):
- Learning Statistics with R (version 0.6 or 0.6.1), by Danielle Navarro. https://learningstatisticswithr.com/
- 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 |
| Special Arrangements |
Course is open only to those students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS).
Students outwith PPLS may contact the Course Organiser to query if any space is available after week 2. |
| 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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