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Multi-level Regression Analysis

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 4 модуль

Преподаватель

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 modeling. The workload of the course includes participation and preparation for classroom activities, use of open datasets for analyzing individual and country effects in a cross-country perspective, and an individual project in essay form that could be developed into a journal article.
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
    0.25 * Mid-term exam + 0.4 * Individual research project essay in English (final project) + 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