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Course Syllabus

Master 2021/2022

Multi-level Regression Analysis

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Comparative Social Research)
Area of studies: Sociology
Delivered by: School of Sociology
Where: Faculty of Social Sciences
When: 1 year, 4 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Ekaterina Nastina
Master’s programme: Comparative Soсial Research
Language: English
ECTS credits: 5
Contact hours: 28

Course Syllabus

Full SyllabusAsk Question

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

Learning Objectives

  • The aim of the course is to develop a solid understanding of multilevel modeling as well as skills to apply the method in real-life research.
Expected Learning Outcomes

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

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 7. Class discussion of issues in individual projects prior to submission
  • Topic 6. Testing and model specification, model comparisons
  • Topic 5. Mid-term exam and research proposals Q&A
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
  • non-blocking Mid-term exam
  • non-blocking Individual research project essay in English (final project)
  • non-blocking Seminar quizes
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.4 * Individual research project essay in English (final project) + 0.25 * Mid-term exam + 0.1 * Seminar quizes
Bibliography

Bibliography

Recommended Core Bibliography

  • Antony, J. S., & Lott, J. L. (2012). Multilevel Modeling Techniques and Applications in Institutional Research : New Directions in Institutional Research, Number 154. San Francisco: Jossey-Bass. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=464973
  • Bickel, R. (2007). Multilevel Analysis for Applied Research : It’s Just Regression! New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=262458
  • Bradford S. Jones, & Marco R. Steenbergen. (1997). Modeling Multilevel Data Structures. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.F4700E2E
  • Gelman, A. B., & Hill, J. (2015). Data analysis using regression and multilevel/hierarchical models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E4FBAE7
  • Meijer, E., & Leeuw, J. de. (2008). Handbook of Multilevel Analysis. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=261439

Recommended Additional Bibliography

  • Smith, R. B. (2011). Multilevel Modeling of Social Problems : A Causal Perspective. Dordrecht: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=371921

Authors

  • VOLCHENKO OLESYA VIKTOROVNA
  • NASTINA EKATERINA ALEKSANDROVNA