View Specification DocumentPsychometric and simulation methods in R 4H PSYCH4111
- Academic Session: 2026-27
- School: School of Psychology and Neuroscience
- Credits: 20
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course is aimed at students who want to continue their R programming and statistical journey. The focus is on simulation and resampling methods, and the analysis of psychometric data. The course offers a mix of lectures, class activities and demonstrations, to provide practical and transferable quantitative skills important to psychologists.
Timetable
Weekly two-hour lectures consisting of teaching and practical
Requirements of Entry
Successful completion of level 3H psychology single honours.
Excluded Courses
None
Co-requisites
None
Assessment
50% examination: at the end of the semester, there will be a 90-minute exam with a mixture of multiple choice and short essay questions assessing students' understanding of key concepts and interpretation of statistical output.
50% research report: students will have several days to make a reproducible R document in which they analyse several datasets.
Main Assessment In: December
Course Aims
This course aims to teach students various techniques for the evaluation and use of psychometric scales, enabling them to examine and improve the internal consistency of psychometric measurements, identify the dimensional structure of psychometric scales, identify clusters of observations and use psychometric scales for prediction and measurement of psychological constructs. This course also introduces students to simulation methods, such as the percentile bootstrap, cross-validation and how to perform a power analysis. The course teaches practical R skills using concrete examples relevant to psychologists. Other practical skills include the illustration of results in ggplot2 and the creation of reproducible reports using RStudio.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ critically evaluate the goal and implementation of the percentile bootstrap, cross-validation and simulation methods at an abstract level;
■ use the percentile bootstrap, cross-validation and other simulation methods to make statistical inferences and interpret the results, including the description of p values, confidence intervals, sampling distributions and performance measures;
■ evaluate and, if necessary, improve the internal consistency of a multi-item psychometric measurement;
■ identify the dimensional structure of a psychometric measurement;
■ in R, perform analyses of regression, reliability, principal components, factors, k-means clusters;
■ illustrate raw, modelled and simulated data using ggplot2;
■ write reproducible reports using RStudio.