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 |
R is a language and environment for statistical computing and graphics that is highly flexible and increasingly popular for statistical analysis. It provides a wide variety of statistical and graphical techniques, including facilities to produce well-designed publication-quality plots.
Design and analysis are taught under a unifying framework which shows that all statistical methods are special cases of a more general model. The course concentrates on research designs and analysis for problems in which there is a single outcome variable.
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 2022/23, Available to all students (SV1)
|
Quota: 84 |
| 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 20%
Written Assessment 80% |
| 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 simple reports using RMarkdown
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Reading List
This course does not directly follow a single text. A workbook is provided in which explanations of analyses and key concepts are given. For further, more in depth reading, we recommend the following books (freely 4available):
- Learning Statistics with R (version 0.6), by Danielle Navarro. https://learningstatisticswithr.com/lsr-0.6.pdf
- OpenIntro Statistics (4th Edition), by Christopher D. Barr, David M. Diez, and Mine Çetinkaya-Rundel; https://www.openintro.org/book/os/
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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 | Mr Josiah King
Tel: (0131 6)50 4210
Email: josiah.king@ed.ac.uk |
Course secretary | Ms Pilar Rodriguez Couceiro
Tel: (0131 6)51 5002
Email: mrodrig8@ed.ac.uk |
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