ST501      Half Unit
Multilevel Modelling

This information is for the 2017/18 session.

Teacher responsible

Prof Irini Moustaki COL 6.05


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.

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).


20 hours of lectures and 10 hours of seminars in the LT.

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.

Key facts

Department: Statistics

Total students 2016/17: 1

Average class size 2016/17: 1

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

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