杏吧论坛

Dr Yunxiao Chen

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 has a limited number of places (it is controlled access). In previous years we have been able to provide places for all students that apply but that may not continue to be the case.

Students must have completed Further Mathematical Methods (MA212) and Probability, Distribution Theory and Inference (ST202).

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.

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. 

Coursework assigned fortnightly and returned to students via Moodle with comments/feedback before the computer workshops.

• 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.