Multilevel Modelling

Prerequisites

Bookings for this module open on THURSDAY, 11 DECEMBER at 10:00 am
For more information see: http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/core/making/windows

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour.

STATA will be used during this module. No prior knowledge of STATA will be assumed.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• Students need to have a basic knowledge of statistics up to chi-square, correlation and multiple regression before attending this module
• You must have a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have access to CamTools

Number of sessions: 4

• Session 1: Introduction to Stata/MLM theory
• Session 2: Applications I - Random intercept models
• Session 3: Appllications II - Random slope models
• Session 4: Applications III - Revision session/growth models

• To understand use of MLM in nested and clustered data - paradigmatic examples are: pupils nested in schools: prisoners nested in prisons.
• to understand MLM use in longitudinal data, observations nested within individuals.

• To learn multilevel modelling techniques

Presentations, demonstrations and practicals

One written exercise [optional, dependent upon department]

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

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

Four sessions of two hours

Once a week for four weeks.