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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in July/August 2023 or February 2024.
Date: Monday 5 – Friday 9 February 2024
Duration: 3 hours of live teaching per day
Time: 08:45 – 12:00 CET
This course introduces you to multilevel models for a variety of nested data structures. The course focuses on the statistical foundation of this approach, the practical estimation of multilevel models (using R or STATA), as well as the interpretation of multilevel results. Using a mixture of pre-recorded lectures, interactive sessions and practical exercises, you will learn about topics such as random coefficients, variance decomposition, centering, and complex data structures.
By the end of this course you will be able to specify, estimate and interpret multilevel models, ranging from simple two-level random intercept models to more complex ones (including three-level, logistic or longitudinal models).
4 credits - Engage fully in class activities and complete a post-class assignment
Bart Meuleman is a Full Professor at the Centre for Sociological Research, KU Leuven (Belgium).
His research focuses on cultural and socio-economic conflict lines in increasingly diverse societies. He has studied the structure and roots of intergroup attitudes, and ethnic threat perceptions and prejudices, from a majority and a minority perspective. He is particularly interested in how increasing migration movements and ethnic diversity affect preferences for welfare redistribution and social justice.
Bart's methodological research interests include comparative survey analysis, attitude measurement, structural equation modelling and multilevel models.
Bart is the National Coordinator of ESS Belgium, co-supervisor of the Belgian National Elections Study and the Belgian Ethnic Minority Elections Study 2014 and 2019, and a member of the Methodology Group of the European Values Study.
Over the course of five modules (one per day), a variety of topics that are fundamental to multilevel modelling are discussed.
Covers the multilevel modelling. You will have the opportunity to join in discussions to do with basic concepts and will be introduced to the idea of nested data. In this module, you will study our very first multilevel model -the two-level random intercepts model- and discover how this approach decomposes the total variation into level 1 and level 2 components.
It takes the two-level regressions a couple of steps further, and adds random slopes as well as cross-levels interactions to the model. You will learn how clever ways of entering predictors can help people to disentangle within- from between-effects - a challenging topic!
The assumptions made by the multilevel regression model are the point of departure of Module 3. The Generalized Linear Model is introduced as a way to deal with data that does not follow a normal distribution. Discussions will take on one such a GLM -the two-level logistic regression model- in greater detail.
It focuses on a particular but very useful application of multilevel models, namely for analysing longitudinal data. The Growth Curve Model is introduced, and you will acquire knowledge about various functional forms of time and covariance structures.
It applies our multilevel toolkit to more complex and challenging data structures. The three-level multilevel model and the cross-classified model are discussed in greater detail.
Each of the modules combines a number of pedagogical tools and resources. You will be required to prepare for the module by watching pre-recorded short lectures (typically 3 lectures of 15 minutes each per module), process the essential readings, and prepare a short hands-on exercises analysing real data using R (or another software package of your choice). During a daily interactive session (3 hours), examples and additional topics are discussed in greater detail, and you will get ample opportunity to ask questions and receive feedback. The instructor and TA will organize office hours, so that you can seek advice for your personal research projects.
Familiarity with linear regression analysis is required.