2022/2023
Многоуровневое моделирование
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Маго-лего
Кто читает:
Департамент социологии
Когда читается:
2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
3
Контактные часы:
28
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
- Principles of multilevel modelling
- Hierarchical OLS – model
- Hierarchical binary logistic model
- Non-hierarchical multilevel model
Interim Assessment
- 2022/2023 2nd module0.25 * Mid-term presentation of the individual project proposal + 0.5 * Final essay + 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.