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 | This course provides foundations in working with data, probability, hypothesis testing and the use of R statistical programming environment. |
| Course description |
The course is taught based on a mixture of theoretical and practical lectures, labs, and independent learning tasks. In semester 1, lectures cover fundamental principles of describing data and of probability theory. In semester 2, lectures build up to discussion of how we make inferences about our hypotheses in psychology, dealing with probability distributions, sampling, and hypothesis testing. The course then introduces simple statistical tests for two variables by way of example. The course starts from scratch, assuming no knowledge of programming.
In the practical lectures and labs, the course introduces the fundamental principles of R programming, with a focus on understanding in a general way how R works, such that these principles can be applied to the use of R for applied data analysis. Students will apply this learning to topics such as basic calculation, data management, plotting and use of simple statistical tests.
Collectively the course will teach basic programming and data analysis skills, including the principles of applying quantitative analysis to answering research questions, and the fundamentals of writing up and reporting results in an accurate way.
The course employs a collaborative learning approach based on pair programming principles, where students work in small groups throughout the labs. This method facilitates peer-to-peer learning of both programming and statistics simultaneously. Students alternate between roles of 'driver' (responsible for writing code) and 'navigator' (identifying potential issues with code or strategy), ensuring each student gains experience in both roles. This group-based approach serves a dual purpose: it gradually introduces first-year students to coding while they learn new statistical concepts, and it provides a supportive environment for working through formative milestones. These milestones are designed to prepare students for their assessed report, effectively bridging the gap between theoretical knowledge and practical application in a collaborative setting.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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
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| Academic year 2025/26, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 40,
Supervised Practical/Workshop/Studio Hours 20,
Formative Assessment Hours 16,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
120 )
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| Additional Information (Learning and Teaching) |
2 lectures x 1 hour per week
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Weekly Quizzes (10%)
Group-based Report (30%). Page/word count: six A4 pages (approx. 2000 words, including figures and tables)
Centrally Arranged Exam (60%) |
| Feedback |
Summative feedback is available for all assessments. Written feedback is available for weekly quizzes and the group-based report. Formative feedback is available through office hours, online discussion forums, and during labs. |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
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| Main Exam Diet S2 (April/May) | DAPR1 Exam (PSYL08013) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand how to describe different types of data graphically and statistically.
- Understand the fundamentals of probability and how it relates to hypothesis testing.
- Understand the structure of a hypothesis test and how this is implemented in psychology.
- Understand the purpose of, and to be able to compute and interpret, simple statistical tests.
- Understand the above referenced tests when implemented in R and how results can be presented and interpreted.
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Reading List
Open access readings will be provided via Learn each week.
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 1-5 and 6.3-6.4: 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 and 2: https://bookdown.org/pingapang9/linear_models_bookdown/ |
Additional Information
| Graduate Attributes and Skills |
Students will begin learning how to programme using R, and how to evaluate data statistically. They will learn to consider alternative perspectives and wider contexts when evaluating data. Whilst they learn the basics of programming, students will learn how to improve from their mistakes, work with peers to problem solve and complete reports, and where to find the answers/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. |
| Keywords | research methods; statistics; psychology |
Contacts
| Course organiser | Dr Umberto Noe
Tel: (0131 6)51 1990
Email: Umberto.Noe@ed.ac.uk |
Course secretary | Ms Fiona Thomson
Tel:
Email: fthomso3@ed.ac.uk |
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