Module 11: Multilevel Modelling

Prerequisites

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

Mphil Students from participating departments taking the Social Science Research Methods Course as part of their research degree

Students need to be familiar with basic logic of statistical reasoning and concepts of variance, correlation and basic linear regression

Number of sessions: 4

• Session 1: Introduction to Stata/data clustering
• Session 2: MM Applications I - Random intercept models
• Session 3: MM Appllications II - Random slope models
• Session 4: Revision session/growth models

The objective is to understand use of MLM in nested and clustered data - paradigmatic examples are: pupile nested in schools: prisoners nested in prisons. MLM use in longitudinal data, observations nested within individuals.

• To learn multilevel modelling techniques

Presentations, demonstrations and practicals

Three exercises

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

• To gain the 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

Three times in Lent term

• Advanced Statistics
• JISC Digital creation, problem solving and innovation