10. BCN Advanced (non)linear regression techniques in R
PhD students. Note that this course may also be taken instead of the compulsory BCN Statistics Course. However, in that case it is required that the student has an excellent grasp of how to conduct and interpret the results of a multiple regression analysis.
Advanced statistics (multilevel modeling and non-linear regression).
Content and objectives
Many PhD students have to analyze complex datasets which involve repeated measurements, which are sometimes collected over time. In this course you will learn how to take into account this structural variability in your data using mixed-effects regression (also known as hierarchical regression or multilevel modeling). In contrast to repeated-measures ANOVA, the advantage of this approach is that no (perfectly) balanced dataset is required. Furthermore, as a regression approach, covariates can be easily included. Besides focusing on linear relationships, we will also focus on non-linear relationships between (combinations of) predictors and the dependent variable. To model these non-linear patterns, we will use generalized additive modeling. This approach in particular is very useful for analyzing any type of time series analysis (such as reaction time data, EEG data, eye tracking data, etc.).
Given that the teacher has a background in linguistics, the examples used in this course come from that field. However, the methods are easily applicable to data from other fields, and students are encouraged to bring their own data to work on (or ask questions about) during the course sessions.
Instead of live lectures, prerecorded online lectures are made available which can be watched online (via Nestor), during the scheduled sessions or (ideally) beforehand. During the five scheduled sessions, the lecturer is available online to answer any questions you have about the prerecorded lecture, or the associated lab session of that day. The course consists of five sessions of four hours each:
1. Linear mixed-effects regression using reaction time data
2. Generalized linear mixed-effects regression using eye tracking data
3. Generalized additive modeling (1D) using articulatory data
4. Generalized additive modeling (2D) using EEG data
5. Generalized additive modeling (nD) using geolinguistic data
Please make sure you have the latest version of R (and optionally RStudio) installed on your computer. Credits are assigned to students who viewed all lectures and attended at least 4 sessions (until the end of each session, or until the lab exercises have been made and checked, in case the student watched the recorded lecture at an earlier earlier). Lectures 1 and 3 are also included in the BCN Statistics Course taught since 2016, so if students have followed this course, attending these lectures is not required (though still recommended as a refresher).
Monday June 7, Wednesday June 9, Thursday June 10, Monday June 14, and Wednesday June 16, from 1 PM – 5 PM. The link to the online sessions will be provided via Nestor before the start of the course.
Required Entrance Knowledge
Being able to use R, and knowledge about basic statistics (most importantly being able to conduct linear regression).
Final attainment level
After the course, you are able to appropriately analyze multilevel data, while you are also able to identify any non-linear patterns therein.
Please check the online registration system
Martijn Wieling ( email@example.com )
|Last modified:||26 February 2021 1.48 p.m.|