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Multilevel Modelling

Bart Meuleman
bart.meuleman@kuleuven.be

University of Leuven

Bart is Full Professor at the Centre for Sociological Research, KU Leuven

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 National Coordinator of ESS Belgium, co-supervisor of the Belgian National Elections Study and the Belgian Ethnic Minority Elections Study 2014 and 2019, and member of the Methodology Group of the European Values Study.

Twitter  @meuleb


Course Dates and Times

Monday 7 ꟷ Friday 11 February 2022
Minimum of 2 hours live teaching per day
09:00 ꟷ 12:00 CET

VIR: This is a virtual course

Prerequisite Knowledge

This online course is designed for people with a solid foundation in regression models that reaches beyond knowing what to click to run a regression, how to copy the output into the paper, and knowing where to find the stars to point to in the write-up.

Very basic knowledge of R is useful. At least know how to open a dataset and maybe run a linear regression, even if you are not yet ready to do all your data processing in R. If you have this much and are willing to work a bit more at it, we’re in business.


Short Outline

This online course provides a highly interactive teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.

Purpose of the course

The course offers an introduction to multilevel regression models for people who have a solid foundation in regression modelling. 

You will learn the foundations and application of multilevel, hierarchical linear or mixed-effects models, and alternative approaches to solve problems multilevel modelling was designed to solve.

ECTS Credits

3 credits Engage fully with class activities 
4 credits Complete a post-class assignment


Long Course Outline

Key topics covered

We start on Monday with a review of the problem multilevel modelling wishes to solve and what other approaches are out there in regression world to solve these problems, both in the regular regression world and among case studies approaches. Once we have established that multilevel modelling is the right tool for us, we will work to understand the structure and notation of these models. 

We continue on Tuesday by learning how to build and run these models (in R with other software examples also provided) applying flexibility that centering, when done correctly, offers for multilevel models (Tuesday ꟷ Wednesday). 

On Thursday, we continue by rethinking the multilevel structure and look at how these modelling approaches apply to within-person and longitudinal designs. 

Finally, on Friday we extend the multilevel approach into the general linear modelling framework, allowing us to model dichotomous or even more complex outcomes, and also beyond two levels into the world of three-level and cross-classified models.


How the course will work online

The course provides readings where we can discuss the materials ahead of the course. 

Readings will be supplemented by around four hours of pre-recorded lectures where attempts may even be made to sound entertaining (though admittedly this will be hard). 

During the course week, expect to be ‘in-class, live’ for over 10 hours in total. We will get to know each other and each other’s projects and work through these as examples of the multilevel models we learned about. We will also go through examples of how to run multilevel models collaboratively in R4


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.