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, and dimension reduction techniques for analysis of questionnaire and survey data. |
| Course description |
DAPR3 builds on the content of DAPR2 and covers more advanced methods that are invaluable for analysing many types of psychological study, preparing students for their dissertations.
This course offers students a solid foundation in multilevel modeling, expanding the linear model to analyze 'hierarchical data.' Such data often involves observations clustered within higher-level groups, such as trials within participants, timepoints within individuals, or children within schools. In the second half of the course, we delve into data reduction techniques. These methods allow us to effectively summarize multiple correlated variables, either through weighted composites or by positing underlying latent factors. Additionally, students will gain insights into crucial concepts, including measurement error, validity, reliability, and replicability. These concepts are especially essential for researchers in psychology, where surveys or questionnaires are used to conduct studies of underlying constructs that cannot be directly measured.
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. 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 2024/25, 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) |
Learn Quizzes, Weekly (best 7 of 9) (10%)
Group Research 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 |
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| Main Exam Diet S1 (December) | Exam | 2:00 | |
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.
- Understand and interpret analyses using generalised multilevel models for hierarchical data arising from cross sectional, longitudinal, and repeated measures designs.
- Understand and interpret data reduction techniques such as principal components analysis (PCA) and exploratory factor analysis (EFA).
- Understand the importance of concepts of validity and reliability in psychological research.
- Understand the above referenced analyses 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 |
Enhancing the skills gained in previous courses, by the end of the course students will be independent programmers with the enhanced statistical knowledge to interpret and evaluate the results of the data programmed into R. They will be able to write scientific reports based off their data, and during assessments they can apply their statistical knowledge clearly and concisely to the questions posed, with clear justification for the stance or approach they use.
Core skills gained on this course: enhanced programming/coding, data and statistical analysis/evaluation, written communication, report writing, independence, problem solving, learning from mistakes, argumentation (justify their point of view with evidence). |
| Keywords | Not entered |
Contacts
| Course organiser | Mr Josiah King
Tel: (0131 6)50 4210
Email: josiah.king@ed.ac.uk |
Course secretary | Miss Anna Jarvis
Tel:
Email: ajarvis3@ed.ac.uk |
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