UV9253 – Multilevel Models
Course description
Course content
In this course you will get acquainted with the fundamental theories and application of multilevel models. The focus will be on using these methods for applied research. You will also gain practical competency in statistical software for analyzing data.
The course covers the following key topics:
- Multilevel data structures, variance components, and ecological fallacy
- Random intercepts and random slopes
- Contextual and cross-level interaction models
- Latent covariate and multilevel path models
UV9253 Multilevel Models is the PhD-level version of MAE4112 Multilevel Models, a compulsory course in the master's program, Assessment Measurement and Evaluation. The content, schedule and reading list for UV9253 Multilevel Models are the same as for MAE4112 Multilevel Models.
Learning outcome
Knowledge:
- Recognize the general principles of multilevel models.
- Understand the key assumptions that underlie these models and methods.
- Understand the principles of model selection and associated inferences.
Skills:
- Select, apply, and interpret the results of a multilevel model that is appropriate for the data and the research question at hand.
- Test key assumptions and offer possible solutions to violations.
- Write up the results of an analysis in an appropriate way.
- Analyze data with help of existing statistical software packages.
Competencies:
- Demonstrate a facility with multilevel modeling to answer well-defined research questions.
- Interpret published scientific research that uses these models and methods.
- Evaluate the tenability of associated inferences and knowledge claims.
Admission to the course
There is a limited number of seats due to joint teaching with the master’s level version of the course.
PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course.
The deadline for registration is on the corresponding semester page for the course.?
Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV) can apply in?StudentWeb.
Other applicants can apply by filling out and sending in a electronic registration form, which is found on the corresponding semester page for the course.?
Formal prerequisite knowledge
MAE4000 Data Science or equivalent.
Overlapping courses
- 3 credits overlap with MAE4112 – Multilevel Models.
- 1 credits overlap with UV9253U.
Teaching
This course combines lectures and computer labs with data analysis tasks in statistical software environments.
The course has joint teaching with the master course MAE4112 Multilevel Models.
Lectures are held by Professor Ronny Scherer.
Obligatory course components:
- 80% attendance requirement for the lectures
- computer lab participation and completion of computer lab exercises
Schedule and literature: Please see the applicable semester page for the course.?
Examination
To obtain 3 credits, 80 % attendance, successful completion of the mandatory assignments and paper is required.
A more specific description of the mandatory assignments and paper will be given at the course.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
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.