Prof Fiona Steele

This course is available on the MPA in Data Science for Public Policy, MRes/PhD in Management (Marketing), MSc in Financial Statistics, MSc in Financial Statistics (Research), MSc in Health Data Science, MSc in Marketing, MSc in Social Research Methods, MSc in Statistics and MSc in Statistics (Research). This course is freely available as an outside option to students on other programmes where regulations permit. It does not require permission.

Students should check that they meet the pre-requisites in the course guide before applying but do not need to provide a written statement.

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

Please log into moodle.lse.ac.uk and self-enrol in the 'R for Statistics Pre-sessional Course

A practical introduction to methods for the analysis of repeated measures data, including continuous and binary outcomes. Topics include: longitudinal study designs, models for two measurements, (random effects) growth curve models, marginal models, missing data, latent class models, models for binary data and dynamic (autoregressive) models. The course will have an applied emphasis with fortnightly computer classes using R.

20 hours of lectures and 10 hours of computer workshop in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students are required to install R on their own laptops for use in the computer workshops.

Coursework assigned fortnightly and returned to students via Moodle with feedback.

• Singer JD, Willett JB. (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press. (Part I only).
• Rabe-Hesketh S,  Skrondal A. (2012) Multilevel and Longitudinal Modeling Using Stata, Third Edition. Volume I: Continuous Responses. College Station, Texas: Stata Press.
• Hedeker D, Gibbons RD. (2006) Longitudinal Data Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc.

Exam (100%), duration: 120 Minutes in the Spring exam period.