Undergraduate Course: Data Analysis for Psychology in R 2 (PSYL08015)
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 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides a thorough introduction to linear models applied to both correlational and experimental study designs in answering psychological questions. We will cover the theoretical background to linear models, as well as practical implementation using R. |
| Course description |
This course will provide a thorough introduction to the linear model, and describe how a variety of methods discussed in the psychological literature (correlation, t-tests, ANOVA, regression) are all specific examples of linear models. We will teach students how to specify, run and interpret linear models to answer a variety of psychological questions, analysing data from both correlational and experimental study designs.
Specifically, the course will cover data cleaning, management, and visualization, linear models with continuous and categorical predictors and outcomes, model assumption checking and diagnostics, and model interpretation.
Lectures will primarily provide the theoretical background alongside applied examples. Labs will provide the practical data skills as well as practice producing reproducible documents using RMarkdown.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
|
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 20,
Summative Assessment Hours 4,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
112 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Weekly Quiz (10%): best 14/18 quiz scores.
Report (30%): Group based
Exam (60%) |
| Feedback |
Formative feedback is available via the weekly quizzes, office hours, lab sessions and discussion boards. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | DAPR2 Paper 1 | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and interpret linear models for continuous outcomes and predictors.
- Understand and interpret linear models for experimental designs with categorical predictors.
- Understand and interpret generalized linear models for binary outcomes.
- Understand and discuss statistical inference, model building and model evaluation for linear models.
- Implement the above referenced models using R and present and interpret the results.
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Reading List
Reading will be predominantly drawn from a series of free open source texts.
Cetinkaya-Rundel, M. & Hardin, J. OpenIntro::Introduction to Modern Statistics. (https://openintro-ims.netlify.app/ )
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.
Wickham, H. (2014). Advanced R. Chapman and Hall/CRC.
In addition, draft materials from in the prep textbook being written by Alex Doumas, Aja Murray and Tom Booth (SAGE) will be provided to students. Further reading may be added during course development. |
Additional Information
| Graduate Attributes and Skills |
On this course students will continue developing their programming skills, and how to create and use linear models for a range of research questions. For their group report, students will learn how to best present the data, how to write scientific reports and how to work as a team to complete a project. Building on previous courses, students will learn how to effectively evaluate the results of data, considering all interpretations before deciding on a conclusion.
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, argumentation (justify their point of view with evidence). |
| Keywords | Research Methods,Statistics |
Contacts
| Course organiser | Ms Emma Waterston
Tel:
Email: Emma.Waterston@ed.ac.uk |
Course secretary | Ms Fiona Thomson
Tel:
Email: fthomso3@ed.ac.uk |
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