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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 the last calendar year, you qualify for £45 off your course fee.
Monday 3 ꟷ Friday 7 August 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements
This course provides a highly interactive online 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 (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.
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.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Levente Littvay researches survey and quantitative methodology, twin and family studies and the psychology of radicalism and populism.
He is an award-winning teacher of graduate courses in applied statistics with a topical emphasis in electoral politics, voting behaviour, political psychology and American politics.
He is one of the Academic Convenors of ECPR’s Methods School, and is Associate Editor of Twin Research and Human Genetics and head of the survey team at Team Populism.
@littvayWe 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 (TuesdayꟷWednesday).
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.
The course provides annotated and interactive course readings in Perusall 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).
We will also set up an online community for our class on Slack, where everyone will be free to discuss anything course related or otherwise as we would if our event took place in-person.
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 using the Google Colab platform. Please set up a Google account if you do not have one already, because we will need Google Drive for this.
The synchronous component of the course will also include Q&A sessions with the Instructor/TA, social (and actual) breaks and a SHOW YOUR BEVERAGE Zoom event.
During office hours, you’ll also be able to sign up for quick personal consultations with the Instructor/TA duo.
This 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.