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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Undergraduate Course: Data Analysis for Psychology in R 3 (PSYL10168)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryDAPR3 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Data Analysis for Psychology in R 2 (PSYL08015)
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) 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 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) Weekly Quizzes (10% best 7 of 9)
Report (30%)
Exam (60%)
Feedback Mid-course based on a lab report; weekly feedback from homework quizzes; weekly office hours.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Exam2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically evaluate research questions concerning correlated and grouped data, and recognise appropriate analytical tools to study them.
  2. Conduct using R and interpret analyses involving mutilevel generalised linear models, and recognise when they are an appropriate analytical method.
  3. Conduct using R and interpret principal components analysis (PCA) and exploratory factor analysis (EFA).
  4. Conduct using R and interpret path mediation models, developing an understanding of the underlying statistical principles of path analysis.
Reading List
Roback, P., & Legler, J. (2021). Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R.

Booth, Doumas, Murray, Noe, King. Data Analysis for Psychology in R.
(pre-print copies of relevant chapters will be provided via Learn)
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.
KeywordsNot entered
Course organiserMr Josiah King
Tel: (0131 6)50 4210
Course secretaryMiss Anna Jarvis
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