This course explores methods for the analysis of longitudinal data and latent variable methods for linear models, generalized linear models, and nonlinear models.  Focusing on applications, this course explores: the analysis of repeated measures ANOVA, multivariate approaches, random-effects regression, covariance-pattern models, generalized-estimating equations and generalizations, latent variable methods including finite mixture modelling, and likelihood methods.  Students will develop expertise using the SAS and R computer packages, although no previous programming experience will be assumed.  Grading is based on homework and computer assignments and a project, as well as several exams.

• intermediate level 40%
• advanced level 60%
• doctoral level 60% and coursework based on a research article

Course begins with a lecture on Monday 17th of September at 2 pm.

There are three exams during the course.

Recommended preceding studies:

Basic courses of statistics and Regression analysis