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Regular version of the site
Master 2020/2021

Multilevel Modeling

Type: Elective course (Modern Social Analysis)
Area of studies: Sociology
When: 2 year, 2 module
Mode of studies: offline
Open to: students of one campus
Master’s programme: Modern Social Analysis
Language: English
ECTS credits: 4

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

Learning Objectives

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

Expected Learning Outcomes

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

Course Contents

  • Intro
  • Principles of multilevel modelling
  • Hierarchical OLS – model
  • Hierarchical binary logistic model
  • Diagnostics of multilevel model
  • Non-hierarchical multilevel model
Assessment Elements

Assessment Elements

  • non-blocking Mid-term presentation of the individual project proposal
  • non-blocking Midterm exam
  • non-blocking Final essay
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Final essay + 0.25 * Mid-term presentation of the individual project proposal + 0.25 * Midterm exam
Bibliography

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.