Prof Fiona Steele COL 7.08

This course is available on the MPhil/ PhD in Statistics. This course is available as an outside option to students on other programmes where regulations permit.

A knowledge of probability and basic statistical theory, including linear regression and logistic regression.

A practical introduction to methods for the analysis of repeated measures data, including continuous and binary outcomes. Topics include: longitudinal study designs, (random effects) growth curve models, marginal models, dynamic (autoregressive) models, latent class models, and multiprocess models for multivariate outcomes. The course will have an applied emphasis with weekly computer classes using appropriate software (e.g. Stata).

20 hours of lectures and 10 hours of computer workshops in the LT.

Week 6 will be a reading week.

Students will be expected to produce 5 exercises in the LT.

Formative assessment is based on data analysis problems that require the use of the statistical software to apply the statistical techniques taught in the lectures and computer classes. Coursework is given out to students every two weeks and returned with feedback and comments.

Hedeker D, Gibbons RD. Longitudinal Data Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc. (2006).

Rabe-Hesketh S,  Skrondal A. (2012) Multilevel and Longitudinal Modeling Using Stata, Third Edition. Volume I: Continuous Responses. College Station, Texas: Stata Press.

Singer JD, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press (2003). (Part I only).

Coursework (100%, 4000 words) in the ST.

Assessment is by 100% coursework which is given to students in week 8