Multilevel Modelling

In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. So, for example, we might conceptualise children's educational process as being influenced by individual or family-level factors, as well as by factors operating at the level of the school or the neighbourhood. Similarly, outcomes for prisoners might be influenced by individual and/or family-level characteristics, as well as by the characteristics of the prison in which they are detained.

• Introduction to Stata/MLM theory
• Applications I - Random intercept models
• Applications II - Random slope models
• Applications III - Revision session/growth models

• University Students from Tier 1 Departments
• Further details regarding eligibility criteria are available here

• Students need to have a basic knowledge of statistics up to chi-square, correlation and multiple regression before attending this module
• A working knowledge of Stata is also advisable

There may be a test at the end of the module consisting of a written exercise; for most students, the test is not compulsory.

• Field, A. (2009) Discovering Statistics Using SPSS. (3rd ed). London:Sage.
• Tarling, R (2009) Statistical Modelling for Social Researchers: Principles and Practice . London: Routledge.

Stata

Click the "Booking" panel on the left-hand sidebar (on a phone, this will be via a link called Booking/Availability near the top of the page).