General information
| Course name | Exercises: 10.33.346 Applied Linguistic Analysis Skills with R |
| Subtitle | |
| Course number | 10.33.346 |
| Semester | SoSe2026 |
| Current number of participants | 11 |
| Home institute | Institute of Dutch Studies |
| Courses type | Exercises in category Teaching |
| First date | Monday, 13.04.26, 08:00 - 10:00 o'clock V03 0-M018 |
| Type/Form | Ü |
| Pre-requisites |
Do you already have some basic knowledge of R and want to take your analytical skills further? Are you working with real linguistic datasets and looking for better analysis methods? If so, this course is for you! Building on foundational R skills, this course focuses on applying statistical and computational methods to linguistic datasets. You will move beyond basic techniques to address real-world analytical challenges such as multicollinearity, non-normal outcome variables, and complex predictor structures. You will learn how to choose appropriate models for different types of linguistic data, evaluate model assumptions, compare models, and interpret results in linguistically meaningful ways. Throughout the course, you will work hands-on with R to implement intermediate-to-advanced analytical approaches, explore modeling strategies, and apply methods for variable selection. |
| Learning organisation |
Potential topics include (but are not limited to): - Linear regression and diagnostics - Mixed-effects modeling - Generalized linear models (e.g., logistic regression) - Beta regression for proportional data - Quantile regression for non-normal distributions - Regularization methods for variable selection (e.g., lasso, ridge, elastic net) - Random forest models for variable importance - Non-linear regression techniques (Generalized Additive Models, GAMs) By the end of the course, you will be able to select and apply appropriate analytical modeling techniques to diverse linguistic research question. |
| Performance record |
Each session will include hands-on practice exercises and a short assignment. The grading structure is as follows: Requirements for 3 CPs: - Script submission after each session (50%) - Short assignments (50%) Requirements for 6 CPs: - Script submission after each session (30%) - Short assignments (30%) - A final assignment at the end of the course (40%) The final assignment involves analysis and interpretation of a linguistic dataset using R, in which you determine the appropriate analytical methods, conduct the analysis, and summarize the results. The submission deadline is currently planned for the end of August. You can also participate in the course without pursuing grades or CPs. [IMPORTANT]: You must bring your own laptop with internet access, with R already installed and functioning. Please make sure R is ready to use before the first session. You may use R through any interface you prefer, such as RStudio, Jupyter Notebook/Lab, or VS Code. I am looking forward to seeing you in the course! |
| Lehrsprache | deutsch |
| ECTS points | 3 |