Multilevel Models

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Международная лаборатория прикладного сетевого анализа

Статус:

Курс по выбору

Когда читается:

2-й курс, 3 модуль

Course Syllabus

Abstract

Many data structures are nested: students nested within classrooms, workers nested within business units, observations nested within individuals, et cetera. Until recently, dealing with nested data structures has been difficult both conceptually and computationally. New models that have been termed multilevel models (also known as hierarchical [non]linear models, mixed effects models, or random coefficient models) lead to separating the lower level effects and the higher level effects explicitly into different parts (e.g., Level 1, Level 2, etc.) of the same overarching model. Such models are designed to avoid “aggregation bias” and to solve the “unit of analysis” problem, all while appropriately accounting for the correlated nature of the “within unit” observations. This course will introduce students to the general multilevel model with an emphasis on applications. We will discuss how such models are conceptualized, the meaning and interpretation of the parameter estimates, and finally how to implement them in computer programs. A major emphasis throughout the course will be on how to choose the appropriate model so that specific questions of interest can be addressed in a methodologically sound way.

Learning Objectives

• The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Expected Learning Outcomes

• Have the skill to work with statistical software, required to analyze the data.
• Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
• Have the skill to meaningfully develop an appropriate model for the research question.
• Be able to criticize constructively and determine existing issues with applied linear models in published work.
• Be able to work with major linear modeling programs, especially R, so that they can use them and interpret their output.
• Be able to explore the advantages and disadvantages of various hierarchical modeling instruments, and demonstrate how they relate to other methods of analysis.
• Have an understanding of the basic principles of hierarchical models and lay the foundation for future learning in the area.
• Know modern extensions to hierarchical modeling.
• Know the basic principles behind working with all types of data for building multilevel models.
• Know the theoretical foundation of multilevel modeling.

Course Contents

• Introduction to the Framework of Hierarchical Modeling
• Random effects
• Parameter interpretation
• Nesting I
• Nesting II
• Multiple levels
• Issues in multilevel modeling
• Extensions I
• Extensions II

Assessment Elements

• Final In-Class or Take-home exam (at the discretion of the instructor)
• Homework Assignments
• In-Class Labs
• Quizzes

Interim Assessment

• 2024/2025 3rd module
  0.2 * In-Class Labs + 0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.2 * Homework Assignments + 0.1 * Quizzes

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
• Little, T. D. (2013). Longitudinal Structural Equation Modeling. New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=544777
• 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
• 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

Recommended Additional Bibliography

• Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=941245
• Lindsey, J. K. (1997). Applying Generalized Linear Models. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=104525

Authors

• Klimov Ivan Aleksandrovich
• Pavlova Irina Anatolevna