Undergraduate Course: Data Analysis for Psychology in R 2 (PSYL08015)
|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
|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.
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.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2020/21, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Weekly quiz (10%): best 14/18 quiz scores - LO1-LO5
Reports: 90% (2x 45% each, one per semester) - LO1-LO5
|No Exam Information
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 results as Rmarkdown reproducible documents.
|Reading will be predominantly drawn from a series of free open source texts. |
Ismay, C., & Kim, A.Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the tidyverse. Chapman and Hall/CRC.
Navarro, D. (2018). Learning Statistics with R.
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.
|Graduate Attributes and Skills
||The course will develop students' skills in working with and using data to answer a research question of interest. Particular attention will be given to the specification and estimation of linear models for a wide variety of research questions, and the accurate presentation of these results in text, tables and visualizations.
|Course organiser||Dr Thomas Booth
Tel: (0131 6)50 8405
|Course secretary||Ms Alex MacAndrew
Tel: (0131 6)51 3733