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Basic and Advanced Multilevel Modeling with R and Stan

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M

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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

This course is designed to provide a practical guide to fitting advanced multilevel models. It is pitched for people from widely different backgrounds, so a significant amount of attention is paid to translating concepts across fields. My approach to the class combines work from econometrics, statistics/biostatistics, and psychometrics. The class is structured using a maximum likelihood framework with practical applied Bayesian extensions on different topics. R packages are selected specifically to make the transition from MLE to Bayesian multilevel models as straightforward and seamless as possible. This is a very applied course with annotated code provided and time in class for lab work. However, it is necessary to spend a class time working through theory and interpretation as well as the logic of mixed effects models. Specific topics include: • Random intercept and random slope models • Cross-classified and multiple membership models • Generalized linear mixed models • Special topics chosen by students The last day of class will have material chosen by the students from a predetermined list of possible topics. In order for your topic to be considered you must respond to the course survey by the end of lunch on Monday so that we can discuss updates during the afternoon. While you will not be an expert in multilevel modeling after one week—this takes years of practice—you will have the tools to go home and fit many advanced models in your own work. By the end of the week you will have practical experience fitting both Bayesian and likelihood versions of basic and advanced multilevel models with RStudio. You will be able to produce diagnostics and results and hopefully interpret them correctly. If you use the models in your own work and read the supplementary materials for the course, you will end up with a very high level of knowledge in multilevel modeling over time. While we do cover Bayesian extensions for multilevel models, this course is not a substitute for a fully-fledged course on Bayesian data analysis. However, it will leave you very well prepared for such a course or for reading a Bayesian analysis textbook.