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This seminar covers techniques for multi-level modeling and panel data analysis using survey data. It will also address methods for managing missing data. The statistical software utilized in the seminar is R, with R-Studio serving as the editor. To facilitate a smooth start, the seminar opens with an introduction to R specifically tailored for social sciences applications. It is designed for students who already possess foundational knowledge in basic statistics, including descriptive statistics, linear regression, ANOVA, and statistical testing.
Either 5 or 10 ECTS points can be achieved in the course.
For 5 points, a small assignment on the methods learned must be completed at the end of the course.
For 10 points, the written elaboration of a research project (max. 12 pages) is necessary, for which the student already brings a topic, a question and an associated data set as a prerequisite.
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| Literatur |
Some literature for the course:
- Kabacoff, R. (2022). R in action: data analysis and graphics with R and Tidyverse. Simon and Schuster.
- Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.
- Greene, W. H. (2000). Econometric analysis 4th edition. International edition, New Jersey: Prentice Hall, 201-215.
- Van Buuren, S. (2018). Flexible imputation of missing data. CRC press.
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