MAE4112 – Multilevel Models
Course description
Schedule, syllabus and examination date
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
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
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.
All students at the Master's Programme in Assessment, Measurement and Evaluation have equal access to the course. Qualified exchange students or students from other master's programmes at UiO may be considered based on capacity.?
Contact us if you want to apply for the course. If you are unsure of whether or not you have sufficient prior knowledge, please send us documentation of previous relevant courses you have taken.?
PhD candidates can take the PhD version of the course: UV9253
Formal prerequisite knowledge
MAE4000 Data Science or equivalent.
Overlapping courses
- 3 credits overlap with UV9253 – Multilevel Models.
- 1 credits overlap with UV9253U.
Teaching
This course combines lectures and computer labs with data analysis tasks in statistical software environments.
Obligatory course components:
- 80% attendance requirement for the lectures
- computer lab participation and completion of computer lab exercises
Examination
The exam consists of a 4 hour individual written examination covering all course material and topics.
You need to have successfully completed the obligatory course components before being allowed to sit the exam. If you do not fulfill these requirements, you must submit a written request to apply for an additional assignment prior to sitting the exam. The application must document stated reasons for absence beyond your control.
Previously given exams and grading guides
Examination support material
No examination support material is allowed.
Language of examination
The examination text is 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
More about examinations at UiO
- Use of sources and citations
- 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.