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 lectures, labs and structured 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. In labs, the course starts from scratch, assuming no knowledge of programming.
In 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 | None |
Information for Visiting Students
| Pre-requisites | Visiting students welcome. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2019/20, Available to all students (SV1)
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Quota: 230 |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 40,
Formative Assessment Hours 23,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
113 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Weekly Quiz: 30% - weekly, best 14/18 quiz scores
Lab tests: 30% (3x10%) - practical coding tasks
Coursework report: 40% |
| Feedback |
Weekly marked assessments (quiz) across the 2 semesters.
Weekly office hours with lecturers (non-compulsory)
Weekly online Q&A sessions (non-compulsory)
Weekly lab sessions
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- To understand how to describe different types of data graphically and statistically.
- To understand the fundamentals of probability and how it relates to hypothesis testing.
- To understand the structure of a hypothesis test and how this is implemented in psychology.
- To have a basic understanding of the R programming environment in order to be able to complete data manipulations, plots and analyses.
- To understand the purpose of, and to be able to compute and interpret, simple statistical tests using R.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | research methods; statistics; psychology |
Contacts
| Course organiser | Dr Thomas Booth
Tel: (0131 6)50 8405
Email: Tom.Booth@ed.ac.uk |
Course secretary | Ms Stephanie Fong
Tel: (0131 6)51 3733
Email: S.Fong@ed.ac.uk |
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