ST501      Half Unit
Multilevel Modelling

This information is for the 2021/22 session.

Teacher responsible

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.

Course content

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.

Formative coursework

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

Indicative reading

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.

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.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2020/21: 1

Average class size 2020/21: 1

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Team working
  • Problem solving
  • Communication
  • Application of numeracy skills
  • Specialist skills