Multilevel Modeling

2025/2026

Учебный год

ENG

Обучение ведется на английском языке

Кто читает:

Департамент социологии

Статус:

Курс по выбору

Когда читается:

2-й курс, 2 модуль

Преподаватель

Понарин Эдуард Дмитриевич

Course Syllabus

Abstract

Analysts have to deal with hierarchical data structures increasingly more often. In particular, one encounters them in the context of cross - country comparisons. Classic regression methods applied to such data result in biased estimates. There are several ways to deal with this problem. One popular method is the multilevel regression. This course covers the basic tenets of this method with applications to international survey research data. The course assumes the student's knowledge of linear and binary logistic regression modelling.

Learning Objectives

• The aim of the course is to show how to work with hierarchical data structures using R.

Expected Learning Outcomes

• Being able to access the results of multilevel modeling and interpret them statistically and sociologically
• To apply multilevel modeling techniques in practical research
• To model individual cases within groups choosing the best model

Course Contents

• Introduction. The idea of hierarchical modeling. Pre-requisites for multilevel modeling. Alternatives to multilevel modeling.
• A basic (empty) multilevel model. Intra-class correlation coefficient. Individual-level predictors. Random intercept.
• Random slopes. Cross-level interaction in multilevel models
• Multilevel binary logistic model
• Research proposals presentation
• Diagnostics of multilevel model
• Non-hierarchical multilevel model and Q&A

Assessment Elements

• Mid-term presentation of the individual project proposal
  Project proposal presentation.
• Midterm exam
• Final essay
  The final work for the course is an essay of about 3000-3500 words in English related to sociology or political science and conducted in a multilevel statistical paradigm. This text is intended to be a draft for an article that can be published in a peer-reviewed journal after some revisions. The essay is supposed to include an abstract, an introduction, a theoretical section and\or literature review, hypotheses derived from the theory, some methodological discussion, a model built on an appropriate dataset, and a results section. The discussion section should follow to wrap up and embed the empirical results into the existing discourse. The most important aspects to be graded are the creativity of the research idea, the operationalization, proper modeling, and clear understanding of the limits of research.

Interim Assessment

• 2025/2026 2nd module
  0.5 * Final essay + 0.25 * Mid-term presentation of the individual project proposal + 0.25 * Midterm exam

Bibliography

Recommended Core Bibliography

• Multilevel analysis: An introduction to basic and advanced multilevel modeling. (1999). SAGE Publications.

Recommended Additional Bibliography

• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

Authors

• VOLCHENKO OLESYA VIKTOROVNA
• PONARIN EDUARD DMITRIEVICH
• NASTINA EKATERINA ALEKSANDROVNA
• MOREVA IULIIA EVGENEVNA