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.
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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 2024/25, 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%
Report (Group Based) (30%)
Exam (Centrally arranged) (60%)
The DAPR1 report, released in semester 2 and worth 30% of the final grade, is a group-based assessment which builds upon pair programming principles and allows peer to peer learning of a new programming language while students are also being taught statistics.
The pedagogical rationale for the group-based work is to introduce first-year students to coding gradually, while they get to grips with novel statistical concepts, and to have peers acting as 'navigators' (responsible for identifying potential problems with the code or strategy) while another student acts as the 'driver' (the person responsible for actually writing the code). The roles are switched so that everyone in the group has experience of each role.
This structure mirrors the labs, where students will work in small groups and be divided in 'drivers' and 'navigators' to produce three formative reports for which they will receive feedback and will prepare them for the assessed report. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | DAPR1 Paper 1 | 2:00 | |
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
| Weekly content including relevant readings will be provided on Learn |
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 | |
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