Undergraduate Course: Data Analysis for Psychology in R 3 (PSYL10168)
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 10 (Year 3 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | DAPR3 builds on the learning from pre-honours by teaching you tools to model dependent and correlated data using the R statistical software. It covers multi-level models for repeated measures designs; principal components analysis and factor analysis; and path analysis (with a particular focus on path mediation) |
| Course description |
DAPR3 builds on the content of DAPR2 and covers more advanced data analysis topics using in R. Topics covered include multi-level models for repeated measures designs; dimension reduction (principal components analysis and factor analysis) commonly used, but not restricted to, the analysis of questionnaire and survey data; and path analysis (especially path mediation). The course will focus on the theoretical background to the approaches, as well as their applications in psychology.
The semester-long course will be taught via a combination of lectures and practical labs. The latter is designed to help students put learning into practice and get hands-on experience with implementing the techniques they learn about in the lecture in R, and will also teach further skills in using RMarkdown for reproducible reports.
Suggested readings will be provided for each topic. In addition, attendance at lecturer and/or teaching co-ordinator office hours and engagement with the online Discussion board for the course is encouraged.
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Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
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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% best 10 of 14)
Report (30%)
Exam (60%) |
| Feedback |
Mid-course based on a lab report; weekly feedback from homework quizzes; weekly office hours. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically evaluate research questions concerning correlated and grouped data, and recognise appropriate analytical tools to study them.
- Conduct using R and interpret analyses involving mutilevel generalised linear models, and recognise when they are an appropriate analytical method.
- Conduct using R and interpret principal components analysis (PCA) and exploratory factor analysis (EFA).
- Conduct using R and interpret path mediation models, developing an understanding of the underlying statistical principles of path analysis.
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Reading List
Roback, P., & Legler, J. (2021). Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R. https://bookdown.org/roback/bookdown-BeyondMLR
Booth, Doumas, Murray, Noe, King. Data Analysis for Psychology in R.
(pre-print copies of relevant chapters will be provided via Learn)
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Additional Information
| Graduate Attributes and Skills |
Problem solving, analytical thinking, critical thinking, knowledge integration and application, handling complexity and ambiguity, digital literacy, numeracy, independent learning and development, written communication. |
| Keywords | Not entered |
Contacts
| Course organiser | Mr Josiah King
Tel: (0131 6)50 4210
Email: |
Course secretary | Miss Susan Scobie
Tel: (0131 6)51 5505
Email: |
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