Undergraduate Course: Data Analysis for Psychology in R1 (PSYL08013)
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 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | In this course, students will develop the foundational skills for working with data and conducting hypothesis tests. The course builds towards a unified model-based approach to statistical analysis, giving students a flexible toolkit for analysing complex real-world data. To run these statistical analyses, students will learn the basics of the R programming language. |
| Course description |
The course teaches the foundations of exploratory data analysis, statistical inference, and common hypothesis tests, ending with simple linear regression. Data modelling follows a unified, model-based framework that will be shared across the follow-up courses. The curriculum begins with the principles of exploratory data analysis, covering data structures, central tendency, and variation. As the course progresses, we move into probability and sampling, establishing the groundwork for significance testing, which is then demonstrated in a range of common statistical tests. The final stage of the course introduces the linear model as a flexible, integrated toolkit, demonstrating that common tests like t-tests and correlations are all expressions of the same underlying model. A central component of the course is the development of technical skills in the R programming language to handle data, perform calculations, and generate visualisations.
The course structure involves a mix of weekly lectures, practical workshops, and independent activities. Lectures provide the conceptual and mathematical foundation, while the workshops offer a facilitated environment for hands-on problem-solving, coding, and reporting. In the workshops, students program in small groups ("peer programming"), in which one person is the "driver" (responsible for typing the code on the PC) and the others are the "navigators" (responsible for suggesting the strategy to follow, spotting and fixing code typos, and supporting the driver). The role of "driver" rotates regularly to ensure everyone has contributed both in a coding capacity and in a problem-solving capacity. Another benefit of this setup is that it helps students create a sense of community and builds peer-to-peer support networks.
Lectures and workshops are supplemented by asynchronous materials for students to complete independently: a mix of instructional videos, readings, and formative quizzes designed to reinforce learning and prepare students to apply their data analysis skills in academic and professional contexts.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Priority will be given to Year 1 students, in particular those who need to take this course as a requirement of their degree programme. |
Information for Visiting Students
| Pre-requisites | Visiting students welcome. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
|
Quota: 0 |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Formative Assessment Hours 4,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
152 )
|
| Additional Information (Learning and Teaching) |
1x one-hour lecture each week 1x one-hour workshop each week
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
This course uses a skills-based framework of assessment which culminates in a Pass/Fail outcome.
To achieve a Pass, students must demonstrate 75% of the course skills during the year (the Coursework component) as well as 75% of the skills included in the final exam (the Exam component).
Key to the skills-based assessment structure is that in the Coursework component, students will have multiple attempts to demonstrate each skill, and they will receive feedback which will feed forward into their learning throughout the year.
Exam: 70% Coursework: 30% |
| Feedback |
Students receive feedback on all attempts to demonstrate a skill during coursework. In addition, formative submission points throughout the year will allow students (working in groups) to submit work to receive formative feedback about their progress toward acquiring the course skills. All students are encouraged to attend office hours to get more detail on how to evidence each skill. |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | DAPR1 Exam (PSYL08013) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate digital literacy: Solve problems by using the R programming language to perform simple data manipulation tasks and run basic statistical tests/models
- Demonstrate numeracy: Calculate appropriate statistics to summarise the distribution of individual variables and associations between pairs of variables
- Think analytically: Analyse individual variables and associations between pairs of variables using basic statistical tests/models
- Think critically: Interpret basic statistical tests/models in the context of a study design
- Integrate and apply knowledge: Create written and visual descriptions of datasets and models and reports of the results of basic statistical tests/models
|
Reading List
Open access readings will be provided via Learn.
Additional readings that contain a list of indicative topics include:
Diez, D. M., Barr, C. D., & Çetinkaya-Rundel, M. (2016). OpenIntro Statistics, 3rd Edition. Specifically, chapters 1-7: https://openresearchlibrary.org/viewer/f46462dd-0fe1-4c66-b80d-9504aa1a7200
van den Berg, S. M. (2024). Analysing Data using Linear Models. University of Twente. Specifically, chapters 1 to 4.11: https://bookdown.org/pingapang9/linear_models_bookdown/ |
Additional Information
| Graduate Attributes and Skills |
Students will acquire the basics of programming using R and evaluating data statistically. They will learn to consider alternative perspectives and wider contexts when evaluating data. Whilst they pick up the basics of programming, students will develop their ability to learn from their mistakes, to work with peers to solve problems and complete reports, and to find answers independently/ask for help when needed.
Core skills gained on this course: programming/coding, statistical analysis and evaluation, considering multiple perspectives, learning from mistakes, teamwork, problem solving, written communication, report writing. |
| Additional Class Delivery Information |
1 x one-hour lecture each week
1 x one-hour workshop each week |
| Keywords | statistics,programming,research methods,psychology |
Contacts
| Course organiser | Ms Emma Waterston
Tel:
Email: Emma.Waterston@ed.ac.uk |
Course secretary | Ms Fiona Thomson
Tel:
Email: fthomso3@ed.ac.uk |
|
|
University Homepage