LING698D

Hierarchical models, also known as mixed-effects or multi-level models, are flexible tools for data analysis. They are useful for many types of grouped or clustered data, such as experimental designs with repeated measures or observational/survey designs with grouping factors. In this course students will apply hierarchical models for data from their fields, use data simulation techniques to better understand the models and interrogate their results, and learn strategies for managing common challenges with fitting and interpretation. This course assumes basic knowledge of R and is best suited for students who have some experience with regression or ANOVA models. No prior knowledge of hierarchical models is required.