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Обычная версия сайта
2022/2023

Многоуровневый регрессионный анализ

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
Когда читается: 4 модуль
Охват аудитории: для всех кампусов НИУ ВШЭ
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 modeling. The workload of the course includes participation and preparation for classroom activities, use of open datasets for analyzing individual and country effects in a cross-country perspective, and an individual project in essay form that could be developed into a journal article.
Цель освоения дисциплины

Цель освоения дисциплины

  • The aim of the course is to develop a solid understanding of multilevel modeling as well as skills to apply the method in real-life research.
Планируемые результаты обучения

Планируемые результаты обучения

  • Students are able to access the results of multilevel modeling and interpret them statistically and sociologically.
  • Students model individual cases within groups choosing the best model.
  • Students understand the basic principles of multilevel modeling
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Topic 1. Introduction. The idea of hierarchical modeling. Pre-requisites for multilevel modeling. Alternatives to multilevel modeling.
  • Topic 2. A basic (empty) multilevel model. Intra-class correlation coefficient. Individual-level predictors. Group - level predictors. Fixed intercept. Fixed slopes
  • Topic 3. Varying intercepts. Varying slopes. Cross-level interaction in multilevel models
  • Topic 4. Multilevel binary logistic regression
  • Topic 7. Class discussion of issues in individual projects prior to submission
  • Topic 6. Testing and model specification, model comparisons
  • Topic 5. Mid-term exam and research proposals Q&A
Элементы контроля

Элементы контроля

  • неблокирующий Individual research project essay in English (final project)
  • неблокирующий Homework Assignments
  • неблокирующий Mid-term exam
  • неблокирующий Seminar quizes
Промежуточная аттестация

Промежуточная аттестация

  • 2022/2023 4th module
    0.25 * Mid-term exam + 0.25 * Homework Assignments + 0.1 * Seminar quizes + 0.4 * Individual research project essay in English (final project)
Список литературы

Список литературы

Рекомендуемая основная литература

  • 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
  • Bickel, R. (2007). Multilevel Analysis for Applied Research : It’s Just Regression! New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=262458
  • Bradford S. Jones, & Marco R. Steenbergen. (1997). Modeling Multilevel Data Structures. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.F4700E2E
  • Gelman, A. B., & Hill, J. (2015). Data analysis using regression and multilevel/hierarchical models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E4FBAE7
  • 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

Рекомендуемая дополнительная литература

  • 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