Multilevel Modeling

9-11 October 2023

Dr. Elisa Volpi, coordinatrice CUSO

Levente Littvay, Research Professor at the Centre for Social Sciences, Hungarian Research Network, and Academic Coordinator at MethodsNET.org

Multilevel data structures are all over the social sciences: observations within municipalities, cantons, districts, or countries. Students within schools. Patients within hospitals. Multiple observations taken from the same person or any other unit of analysis. In quantitative studies, relevant predictors appear on all these levels of analysis. But how do we deal with them? 

The course is designed to provide scholars with a basic understanding of multilevel (a.k.a. hierarchical or mixed) models designed to solve these problems. Special attention is given to the translation of theoretical expectations into statistical models, the interpretation of results in multilevel analyses, and the general use and misuse of multilevel models in the social sciences. While the course is predominantly designed to give you the knowledge of multilevel regression modeling, it does also arm you with the basic tools to run multilevel models in R. (I also have Stata code for most of the models presented thanks to a former teaching assistant, but I am not a Stata user so there you are on your own.) Applications will include models with continuous and limited dependent variables in hierarchical, longitudinal, and cross-classified nesting situations and, if time allows, multilevel structural equation models. The goal of the course is to offer a basic introduction and the foundation for participants to start using and critically assessing multilevel models and also have the ability to independently discover and master advanced multilevel statistical topics. Upon completion, the participants will have a basic conceptual understanding of multilevel modeling and its statistical foundations. Participants will be able to critically assess the appropriateness of such techniques in their own and other people's research and conduct multilevel modeling themselves to the highest academic standards.

More details about the schedule and the topics covered will be available soon. For any question contact: [email protected]

UNIGE

Prerequisites for the workshop: The workshop aims at the level of researchers with prior statistical training. Anyone registering should be an experienced user of regression, and know the basics of inferential statistics. It would be extremely helpful to have basic functional knowledge of R. At minimum you should know how to manage files in R, install and load packages, load data, and run basic analytical commands. I am not proud of it but for years I did my data cleaning in SPSS and when I needed to, I loaded the clean data in R and ran whatever I needed to run. If you can install R and RStudio, know how to load data and run basic things like the lm command for linear regression, even if you do a Google search before every line of code, you are ready for this workshop. If not, please get there before we begin. It is not much. You can do it. We don't necessarily need laptops in class, but if you bring one or want to discuss specific things you tried at home, definitely have R installed. RStudio is highly recommended unless you have another Integrated Development Environment (IDE) you prefer.

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