Advanced Regression Methods for Linguistics

Language and Computation Courses

Introductory Course

Advanced Regression Methods for Linguistics,
Martijn Wieling (University of Groningen, The Netherlands)

This course will introduce students to advanced regression methods in R. While many people have learned about multiple regression, interpreting the output of a regression model, especially with interactions present is something which is often found difficult. The course will therefore start with one lecture explaining multiple regression. Subsequently, two lectures of the course will cover (Gaussian and logistic) mixed-effects regression in order to enable students to take into account structural variability present in the data. For example, experiments in linguistics frequently involve participants responding to multiple items. This structure needs to be brought into the model in order to prevent overconfident (i.e. too low) p-values. The final two lectures of this course provide a thorough introduction to generalized additive modeling, which is a powerful method to analyze non-linear patterns in data. This approach is especially useful when time-series data (such as EEG, eye-tracking, or articulatory data) need to be analyzed.