LING4136 – Advanced Statistical Methods for Language Students
Course description
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??
reproduce published findings using the original code
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.
This course is for students admitted to Linguistics (master) and Multilingualism (master).?Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
If you are not already enrolled as a student at UiO, please see our information about?admission requirements and procedures.
PhD candidates can be admitted to this course (and get 5 ECTS) after approval from the PhD leader at their Department. Candidates from Department of Linguistics and Scandinavian Studies apply for pre-approval using this online form:?https://nettskjema.no/a/529976
Candidates from other Departments should contact their PhD leader and PhD coordinator for pre-approval.?
Formal prerequisite knowledge
To participate meaningfully, students must have successfully completed LING2135/LING4135 at UiO. 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 STAN.?
Overlapping courses
- 10 credits overlap with LING2136 – Advanced Statistical Methods for Language Students.
Teaching
2-hour classes for 14 weeks. Classes will involve practical exercises with a computer to apply the knowledge acquired from the readings and self-study. One obligatory assignment will be offered for weeks 2-11. To be eligible to take the final exam,?8 assignments out of 10 must be passed alongside the submission of a case report midway through the semester.?
Approved compulsory activities are valid for the next two terms in which the course is offered.?
Examination
The final exam is a 3-day (72hrs) home exam.?
Language of examination
The examination questions are given in English, and you submit your response in English.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F?is a fail. Read more about?the grading system.
Resit an examination
A term paper or equivalent that is passed may not be resubmitted in revised form.
If you?withdraw from the exam?after the deadline, this will be counted as an examination attempt.
More about examinations at UiO
- Use of sources and citations
- How to use AI as a student
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.