Overview
This course develops statistical methods for the analysis of observations measured on multiple variables simultaneously. Topics include multivariate data visualization, matrix algebra and vector spaces, covariance and correlation structures, the multivariate normal distribution, mean vectors, covariance matrices, Mahalanobis distance, principal component analysis, factor analysis, canonical correlation, multivariate analysis of variance, discriminant analysis, and cluster analysis.
Students formulate multivariate research questions, select and apply appropriate techniques, assess assumptions including multivariate normality, independence, and covariance homogeneity, and interpret eigenvalues, loadings, scores, group separation, and model output. Practical work using statistical software emphasizes diagnostics, resampling, reproducibility, responsible interpretation, and clear communication of substantive findings.
Learning Outcomes
- Prepare, visualize, and explore multivariate datasets using appropriate statistical software.
- Derive and apply matrix-based procedures involving vectors, matrices, covariance structures, and Mahalanobis distance.
- Evaluate assumptions concerning multivariate normality, independence, covariance homogeneity, and model adequacy.
- Compare dimensionality-reduction methods, including principal component and factor analysis, using eigenvalues, loadings, scores, and diagnostic evidence.
- Apply canonical correlation, multivariate analysis of variance, discriminant analysis, and cluster analysis to appropriate research questions.
- Validate multivariate results using diagnostics, cross-validation, and resampling methods where appropriate.
- Interpret multivariate model output in relation to substantive research objectives and limitations.
- Produce clear, reproducible, and responsible multivariate analyses using documented statistical workflows.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Weekly problem sets (8 × 3%) | 24.00% |
| Deliverable | Data analysis report | 16.00% |
| Test | Practical statistical computing test | 15.00% |
| Exam | Mid-semester examination | 20.00% |
| Exam | Final examination | 25.00% |
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.