Prof Irini Moustaki COL 6.05

This course is available on the MPhil/PhD in Health Policy and Health Economics and 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 multilevel modelling with applications in social research. This course deals with the analysis of data from hierarchically structured populations (e.g., students nested within schools, individuals nested within households or geographical areas) and longitudinal data (eg repeated measurements of individuals in a panel survey). Multilevel (random-effects) extensions of standard statistical techniques, including multiple linear regression and logistic regression, will be considered. The course will have an applied emphasis with computer sessions using appropriate software (e.g., Stata).

This course will be delivered through a combination of computer classes and lectures totalling a minimum of 30 hours across  Lent Term. This year, some of this teaching may be delivered through a combination of classes and virtual lectures delivered synchronously. This course includes a reading week in Week 6 of Lent Term.

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

Formative coursework is assigned fortnightly and returned to students with comments/feedback via Moodle before the lab sessions

T. Snijders & R Bosker Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modelling, Sage (2011, 2nd edition)

S Rabe-Hesketh & A Skrondal, Multilevel and Longitudinal Modeling using Stata, (Third Edition), Volume I: Continuous responses (plus Chapter 10 from Volume II, which is available free on the publisher's website). Stata Press (2012).

Also recommended are:

A Skrondal & S Rabe-Hesketh, Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models, Chapman & Hall (2004);

H Goldstein, Multilevel Statistical Models, Arnold (2003);

S W Raudenbush & A S Bryk, Hierarchical Linear Models: Applications and Data Analysis Methods, Sage (2002);

G Verbeke & G Molenberghs, Linear Mixed Models for Longitudinal Data, Springer (2000);

E Demidenko, Mixed Models, Wiley (2004).

Coursework (100%, 4000 words).

Assessment is by 100% coursework given to students in week 8 of the course.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.