Participants select either one module or two modules provided that the timeslots do not overlap (see Programme).
Froms 2024 onwards, participants have the option to follow a whole “track” of either introductory-level or advanced statistical modules as a full curriculum. If you are interested in statistical training, then you can select the track which best suits your background knowledge:
- Novice track = Module 1 Introduction to R + Module 12 Introduction to statistics with R
- Expert track = Module 2 Avanced data analysis with R + Module 10 Multivariate data analysis with R
Module 1: Introduction to R
R is a widely-used programming language for statistical data analysis. This beginner-friendly module aims to provide participants with a solid foundation in R, empowering them to explore, analyze, and visualize data efficiently. Emphasis is placed on hands-on practical exercises and real-world examples, enabling students to immediately apply their knowledge.
Module 2: Advanced data analysis with R
This module offers an introduction to the workhorse of quantitative data analysis – the general linear model.
Module 3: Natural Language Processing with Python
Natural Language Processing or NLP is a discipline that focuses on the interaction between data science and human language and gives the machines the ability to read, understand and derive meaning from human languages. The participants of this course will learn about different NLP techniques and basic programming skills.
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Module 4: PRAAT
This course will introduce Praat scripting. By using scripts it will be much easier to replicate your analyses on speech files and to communicate with others about what you have done and how you have done it.
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Module 5: ELAN and FLEx
This course introduces participants to two pieces of software that are widely used in language documentation, offers hands-on training in each software application, and then goes further to demonstrate how to use the two of them together: ELAN and FLEx.
Module 6: Eye-tracking
Over the last decades, eye tracking has become a wide-spread technique to understand how people process and learn language. In this course, we will cover a basic introduction to eye-tracking techniques in language science.
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Module 7: Survey design
This course provides a comprehensive introduction to survey research, covering the entire process from designing questionnaires to preparing data for analysis.
Module 8: Linguistic ethnography
In this course, participants will be introduced to the basic ideas behind qualitative ethnographic research methods in the context of linguistics, focusing on the ingredients required for this process, i.e. data collection and analysis. We will discuss the most important concepts in ethnographic research, what counts as valid data, what is required in data collection, good practices, ethics, mixing methods, and how to describe, analyse and interpret data.
Module 9: Bayesian data analysis
This course introduces modern Bayesian statistics using the probabilistic programming language Stan and the front-end brms, used with R.
Module 10: Multivariate data analysis with R
This module offers an overview of the most important techniques for analyzing multivariate data, i.e. data involving several (correlated) variables. Such multivariate data arise often in studies involving language, e.g. research into language attitudes, reaction times to stimuli or cooccurrence frequencies in corpora. In addition, word embeddings in NLP share various ideas with multivariate statistical techniques so these similarities will also be touched upon.
Module 11: Research data management
This module is not offered in 2025.
Module 12: Introduction to statistics with R
In this module, you will learn how to analyse linguistic data with R, Rstudio, and the tidyverse. We will cover descriptive and inferential statistics as well as linear modelling. Each lesson consists of a theoretical part followed by hands-on exercises in R.