2026/2027
Многоуровневое моделирование
ID 1129811
Статус:
Маго-лего
Кто читает:
Департамент социологии
Где читается:
Санкт-Петербургская школа социальных наук
Охват аудитории:
для своего кампуса
Язык:
русский
Кредиты:
3
Контактные часы:
28
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
Элементы контроля
- Midterm exam
- Final essayThe final work for the course is an essay of about 3000-3500 words in English related to sociology or political science and conducted in a multilevel statistical paradigm. This text is intended to be a draft for an article that can be published in a peer-reviewed journal after some revisions. The essay is supposed to include an abstract, an introduction, a theoretical section and\or literature review, hypotheses derived from the theory, some methodological discussion, a model built on an appropriate dataset, and a results section. The discussion section should follow to wrap up and embed the empirical results into the existing discourse. The most important aspects to be graded are the creativity of the research idea, the operationalization, proper modeling, and clear understanding of the limits of research.
- Mid-term presentation of the individual project proposalProject proposal presentation.
Промежуточная аттестация
- 2026/2027 2nd module0.5 * Final essay + 0.25 * Midterm exam + 0.25 * Mid-term presentation of the individual project proposal
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.