Multilevel Modeling

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс по выбору

Когда читается:

2-й курс, 2 модуль

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
• Midterm exam
• Final essay

Interim Assessment

• 2024/2025 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

• PONARIN EDUARD DMITRIEVICH
• Ильина Мария Ивановна