LING2136 – Advanced Statistical Methods for Language Students

Schedule, syllabus and examination date

Course content

In this advanced course, students will deepen their understanding of the analysis, interpretation and presentation of quantitative linguistic data. The overarching goal is to teach students statistical analyses that are state-of-the-art, enabling them to publish their scientific work and generating insights from any form of data.??

The course will expand students’ understanding of hierarchical linear models ("linear mixed effects modelling") and introduce conceptual and practical frameworks for informative data visualization of hierarchical data. Moreover, the class will introduce inference and hypothesis testing within the Bayesian framework. The entire course will use the open source softwares R and RStudio.?

Learning outcome

Students will learn how to?

  • advance their knowledge using the R programming language?

  • run and interpret hierarchical linear models??

  • plan and create highly informative data visualizations??

  • make inference using the Bayesian framework, including?

  • specifying prior assumptions?

  • interpreting results?

  • defining smallest effect sizes of interest?

  • testing hypotheses using Bayes factor?

  • work, collaborate, and store data analyses in reproducible ways using GitHub.

Admission to the course

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for?in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures.

Formal prerequisite knowledge

To participate meaningfully, students must have

  • Successfully completed LING2135/LING4135 at UiO.
  • Or, if you have not completed the listed course, we ask that you contact the student advisor and include documentation that demonstrates your qualifications before the application deadline. Documentation must demonstrate both sufficient R programming and experience with generalized linear models. Examples of relevant documentation may include course certificates, code samples, project experience, or references from employers. Students are expected to bring their laptops to classes for doing exercises and ensure that they have installed recent versions of R and R-Studio on their laptops. Further, they are expected to prepare their machine for the use of