Master
2026/2027
Multi-level Regression Analysis
ID 1120208
Type:
Elective course (Comparative Social Research)
Delivered by:
School of Sociology
Where:
Faculty of Social Sciences
When:
1 year, 4 module
Open to:
students of all HSE University campuses
Language:
English
ECTS credits:
3
Contact hours:
28
Course Syllabus
Abstract
Social researchers often have to address the influence of the social context on individual behavior and attitudes, which requires combining different levels of analysis. This course is devoted to multilevel regression, a method developed for analyzing such multilevel or "nested" data, where classical regression methods can lead to biased estimates. The course covers the basic principles of this method (fixed and random effects, cross-level interactions, model fit assessment, etc.), which are illustrated using examples from international comparative studies. The workload of the course includes participation in lectures and seminars, group work with open data from international surveys, independent readings, as well as an individual project in the form of an essay, which can later be developed into a research article. Successful completion of the course requires a basic understanding of the fundamentals of linear and logistic regression modeling, as well as proficiency in R.
Learning Objectives
- Develop a solid understanding of multilevel data and mixed effects.
- Сultivate hands-on skills in specifying, estimating, and selecting multilevel models using R
- Build skills in analysis, visualization, and presentation of multilevel modeling results to solve applied research questions.
Expected Learning Outcomes
- Students are able to access the results of multilevel modeling and interpret them statistically and sociologically.
- Students model individual cases within groups choosing the best model.
- Students understand the basic principles of multilevel modeling
Course Contents
- Topic 1. Introduction. The idea of hierarchical modeling. Pre-requisites for multilevel modeling. Alternatives to multilevel modeling.
- Topic 2. A basic (empty) multilevel model. Intra-class correlation coefficient. Individual-level predictors. Group - level predictors. Fixed intercept. Fixed slopes
- Topic 3. Varying intercepts. Varying slopes. Cross-level interaction in multilevel models
- Topic 4. Multilevel binary logistic regression
- Topic 5. Mid-term exam and research proposals Q&A
- Topic 6. Testing and model specification, model comparisons
- Topic 7. Class discussion of issues in individual projects prior to submission
Assessment Elements
- Literature search
- Individual research project essay in English (final project)
- Research Proposal
- Mid-term exam
- Seminar quizes
Interim Assessment
- 2026/2027 4th module0.2 * Research Proposal + 0.4 * Individual research project essay in English (final project) + 0.15 * Mid-term exam + 0.2 * Seminar quizes + 0.05 * Literature search
Bibliography
Recommended Core Bibliography
- Data analysis using regression and multilevel/hierarchical models, Gelman, A., 2009
- Handbook of multilevel analysis, , 2008
Recommended Additional Bibliography
- Multilevel analysis : an introduction to basic and advanced multilevel modeling, Snijders, T. A. B., 1999
- Multilevel modeling of social problems : a causal perspective, Smith, R. B., 2011