Advanced Methods: Multilevel Modeling

Credits: 
2.0
Course Description: 

The course is designed to provide scholars with a basic understanding of multilevel (a.k.a. hierarchical or mixed) models. Upon completion the students will have a basic conceptual understanding of multilevel modeling and its statistical foundations. Students will be able to critically assess the appropriateness of such techniques in their own and other people’s research. Special attention is given to the translation of theoretical expectations into statistical models, the interpretation of results in multilevel analyses and the general use and misuse of multilevel models in the social sciences. Lab sessions will provide basic application of multilevel models with continuous and limited dependent variables in hierarchical, longitudinal and cross-classified nesting situations. The goal of the course is to offer a basic introduction and the foundation for students to start using and critically assessing multilevel models and also have the ability to independently discover and master advanced multilevel statistical topics. Pre-requisites for the course: basic R knowledge, solid understanding of linear and logistic regression models and their assumptions. (At the doctoral school this means the completion (or parallel attendance) at all MA level stats courses. You are welcomed to join all these courses if you wish to develop a good understanding of statistical methods in the social and political sciences.)

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