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Instructors: Prof. Dr. Michael Scharkow
Event type:
Seminar / exercise
Org-unit: Sociology, Politics & Economics
Displayed in timetable as:
AM AMM
Hours per week:
1,5
Credits:
3,0
Location:
Campus der Zeppelin Universität
Language of instruction:
German
Min. | Max. participants:
5 | 35
Course content:
Hierarchical data, in which lower level units are clustered within higher level units - pupils in classes, institutions in countries, news articles in outlets - are ubiquitous in the social sciences. This course offers a non-technical, pragmatic introduction to the analysis of hierarchical data using multilevel (or mixed-effects) regression models. Examples will be drawn from sociology, political science, education and communication research, although the models can be applied in many disciplinary contexts.
In the first part of the course, we discuss foundations of clustered data structures, as well as conceptual basics such as within- and between-cluster variation. In the second part, we cover the basic multilevel models for analyzing hierarchical data. In the third part, we discuss extensions such as cross-classified data structures or generalized linear mixed models.
The course contains many practical exercises using R and the lme4 package. Basic knowledge of statistics, especially linear regression, is required and expected. Prior knowledge of R is not strictly necessary, but will be helpful.
Educational objective:
The aim of the course is to enable the participants
- to find suitable statistical modeling strategies for a given research question and data structure
- estimate multilevels models for hierarchical data
- to interpret the results of model estimates in their own analyses and the research literature
Further information about the exams:
Students are required to conduct, document (in the form of a very short paper) and present a data analysis on a topic and data set of their choice. We will discuss suitable research questions and studies at the beginning of the course.
Mandatory literature:
- Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
- Hox, J. (2010). Multilevel analysis: Techniques and applications. Routledge.
- Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and applied multilevel analysis. Sage.
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