ST405 Half Unit
Multivariate Methods
This information is for the 2024/25 session.
Teacher responsible
Dr Yunxiao Chen
Availability
This course is available on the MPhil/PhD in Statistics, MSc in Data Science, MSc in Health Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.
This course is not controlled access. If you request a place and meet the criteria you are likely to be given a place.
Pre-requisites
Students must have completed Further Mathematical Methods (MA212) and Probability, Distribution Theory and Inference (ST202).
Course content
An introduction to the theory and application of modern multivariate methods used in the Social Sciences: Multivariate normal distribution, principal components analysis, factor analysis, latent variable models, latent class analysis and structural equations models.
Teaching
This course will be delivered through a combination of computer workshops and lectures, totalling a minimum of 28 hours across Winter Term. This course includes a reading week in Week 6 of Winter Term.
Formative coursework
Coursework assigned fortnightly and returned to students via Moodle with comments/feedback before the computer workshops.
Indicative reading
- D J Bartholomew, F Steele, I Moustaki & J Galbraith, Analysis of Multivariate Social Science Data (2nd edition);
- D J Bartholomew, M Knott & I Moustaki, Latent Variable Models and Factor Analysis: a unified approach;
- C Chatfield & A J Collins, Introduction to Multivariate Analysis;
- B S Everitt & G Dunn, Applied Multivariate Data Analysis;
- K.V. Mardia, J.T. Kent and J.M. Bibby, Multivariate Analysis.
Assessment
Exam (100%, duration: 2 hours) in the spring exam period.
Student performance results
(2020/21 - 2022/23 combined)
| Classification | % of students |
|---|---|
| Distinction | 42.1 |
| Merit | 42.1 |
| Pass | 7.9 |
| Fail | 7.9 |
Key facts
Department: Statistics
Total students 2023/24: 15
Average class size 2023/24: 15
Controlled access 2023/24: Yes
Value: Half Unit
Course selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills