UV9257 ¨C Multilevel and Longitudinal Modeling

Course content

This course introduces models for multilevel or clustered data, such as cross-sectional data with students nested in schools, or longitudinal data with repeated measures/panel waves nested in subjects. ?Models and concepts are introduced via examples from a variety of disciplines, equations and illustrative graphs, keeping the mathematics as simple as possible (i.e., avoiding matrix algebra and calculus). The handouts include Stata commands for all the results that are presented and the data are available online.

Part 1 of the course covers linear multilevel models for continuous responses, including random-intercept, random-coefficient, and three-level models. (Restricted) maximum likelihood estimation of model parameters and empirical Bayes prediction of random effects are introduced at a non-technical level.

Part 2 focuses on longitudinal data analysis, starting with application of random-coefficient models for growth, contrasting this approach with marginal modeling and giving a brief overview of methods from panel data econometrics.

Part 3 introduces multilevel logistic regression for binary responses.

Learning outcome

By the end of the course, students should have an understanding of model assumptions, be able to choose an appropriate model for a given problem and interpret parameter estimates.

Admission to the course

The course has been developed for PhD candidates affiliated with the Faculty of Educational Sciences (UV), but others may also apply to participate. As a minimum requirement, all participants must hold at least a Master's degree.

PhD candidates affiliated with the Faculty of Educational Sciences will be given priority and are to register through Studentweb. Others may apply through the web form on the semester page for the course.?

Registration deadline: Please see the applicable semester site for the upcoming registration deadline.

Formal prerequisite knowledge

Prior to the course, participants must be familiar with the theory and practice of linear and logistic regression.

Overlapping courses

• 1 credits overlap with UV9257U ¨C Multilevel and Longitudinal Modeling (discontinued).

Teaching

This course consists of on-site lectures and requires 80% approved attendance.?

You will find the timetable and the reading list on the semester site for this course.

The lectures are held by Professor Anders Skrondal,?CEMO and the Norwegian Institute of Public Health and Professor Sophia Rabe-Hesketh, University of California, Berkeley.?

Examination

To obtain 3 study points participants need to submit an approved? paper, as well as attain? 80%?participation in the lectures.?

A more specific description of the paper and timetable for exam will be given in 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
• 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.