Многоуровневые модели

2024/2025

Статус: Маго-лего

Кто читает: Международная лаборатория прикладного сетевого анализа

Когда читается: 3 модуль

Охват аудитории: для своего кампуса

Язык: русский

Кредиты: 3

Полная версия программы учебной дисциплины

Аннотация

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.

Цель освоения дисциплины

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

Планируемые результаты обучения

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.

Содержание учебной дисциплины

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

Элементы контроля

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

Промежуточная аттестация

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

Список литературы

Авторы

Климов Иван Александрович
Павлова Ирина Анатольевна

Программа дисциплины