Belegung und aktuelle Informationen über AGNES: https://agnes.hu-berlin.de/lupo/rds?state=verpublish&status=init&vmfile=no&publishid=202837&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung Schließen

The seminar is a three-day block seminar at the end of the semester (5.-7.7.2023) and is planned as a face-to-face event. In this seminar, methods of multi-level modelling (day 1), longitudinal and panel data modelling (with survey data, day 2) are taught. In this context, dealing with missing data will also be discussed (day 3). As statistical software R (with R-Studio as editor) is used. The seminar is aimed at students who are either in a higher Master's degree year (about to write their Master's thesis) or doctoral students. The seminar is not suitable for Bachelor's students or Master's students at the beginning of their Master's studies, as it already presupposes extensive methodological knowledge. 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 prerequisit. Schließen

In this seminar, methods of advanced quantitative methods for cross-sectional analysis are taught. Topics of the seminar are: A. Linear regression (advanced level) B. Nonlinear regression models / generalized linear models - Poisson regression - Logit and probit model - Ordered response models (ordered probit / logit model) C. Survey design and survey weights (and how to use them) In this context, we will also talk about model selection, transformation of variables, heteroscedasticity, and endogeneity. We will use the statistical software R (with R-Studio as editor) in the seminar. Schließen

Some literature for the course: Multilevel modelling: - Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage. - Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press. Panel regression modelling - Frees, E. W. (2004). Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press. - Greene, W. H. (2000). Econometric analysis 4th edition. International edition, New Jersey: Prentice Hall, 201-215. Missing data - Van Buuren, S. (2018). Flexible imputation of missing data. CRC press. - Enders, C. K. (2010). Applied missing data analysis. Guilford press. Schließen