2024/2025
Многоуровневое моделирование
Статус:
Маго-лего
Кто читает:
Департамент социологии
Когда читается:
2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
русский
Кредиты:
3
Программа дисциплины
Аннотация
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.
Цель освоения дисциплины
- The aim of the course is to show how to work with hierarchical data structures using R.
Планируемые результаты обучения
- 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
Содержание учебной дисциплины
- Introduction. The idea of hierarchical modeling. Pre-requisites for multilevel modeling. Alternatives to multilevel modeling.
- A basic (empty) multilevel model. Intra-class correlation coefficient. Individual-level predictors. Random intercept.
- Random slopes. Cross-level interaction in multilevel models
- Multilevel binary logistic model
- Research proposals presentation
- Diagnostics of multilevel model
- Non-hierarchical multilevel model and Q&A
Промежуточная аттестация
- 2024/2025 2nd module0.5 * Final essay + 0.25 * Mid-term presentation of the individual project proposal + 0.25 * Midterm exam
Список литературы
Рекомендуемая основная литература
- Multilevel analysis: An introduction to basic and advanced multilevel modeling. (1999). SAGE Publications.
Рекомендуемая дополнительная литература
- Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.