Multilevel Modeling

19-21 May 2026

Dre Elisa Volpi, coordinatrice CUSO

Prof. Levente Littvay

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 linear or mixed effects) regression 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 troubleshooting there you are on your own with the various AI tools.)

Applications will include models with continuous and limited dependent variables in hierarchical, longitudinal, and cross-classified nesting situations, and, if time allows, we will introduce multilevel structural equation models. The goal of the course is to offer a basic introduction and a foundation for participants to start using and critically assessing multilevel models, and to develop 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.

Prerequisites to the course: a solid foundation in linear regression. (Knowing what to click in SPSS and how to copy and paste the table does not constitute a solid foundation. Knowing the assumptions of regression models like homoskedasticity and no autocorrelation does.)

Tentative schedule: from 10:00 to 18:00h each day, including lunch and coffee breaks. 

Geneva

Levente Littvay (Levi) is Research Professor at ELTE Centre for Social Sciences, Hungarian Academy of Sciences Centre of Excellence and a Senior Visiting Researcher at the Democracy Institute of Central European University, where he also used to be Professor of Political Science (2007-2023) and taught graduate courses in research design, applied statistics, and political science, received the university's Teaching Award (2015 for methods-, and 2021 for online teaching). PhD in Political Science and an MS in Survey Research and Methodology from the University of Nebraska-Lincoln. Taught numerous research methods workshops globally and online, including introductory, advanced, and multilevel regression and structural equation modelling, experiments, causal inference, impact evaluation, latent variable models, measurement theory, missing data, introductory stats, survey design, R, research design and AI in research courses, oversaw the training of over 10,000 methods school participants as advisory board member and academic convenor of various methods schools. Founder of MethodsNET, and head of Team Survey in Team Populism where he helped spawn the Leader Profile Series and the New Populism series with The Guardian. He was a member of the European Social Survey's (ESS) Round 10 (2020-21) democracy and COVID19 module questionnaire design teams, contributor to ESS-CRONOS2, and Principal Investigator for the Comparative Study of Election Surveys (CSES) for Hungary and Tunisia. Other awards include the International Society for Political Psychology's John L. Sullivan Mentor Award, Hungarian National Research, Development, and Innovation Office's Excellence_25 grant (given to ERC Advanced grant finalists), the European University Institute's Fernand Braudel Senior Research Fellowship (2019-20), the 2022 Giovanni Sartori Prize for best paper in the Italian Political Science Review / Rivista Italiana di Scienza Politica, and 2017 Morton Deutsch Award for best article in Social Justice Research. He is published in Political Analysis, The Journal of Politics, BMC Medical Research Methodology, and others. Books include Multilevel Structural Equation Modeling with Bruno Castanho Silva and Constantin Manuel Bosancianu in SAGE's QASS (little green book) series, which was also published in Mandarin Chinese.

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