Mixed Models

Introduction to multilevel modelling

Students will be introduced to mixed and multi-level models, starting with random and fixed effects in a two-level model. The reason for applying mixed models will be explained, and students will apply their first mixed model in R.

Longitudinal data (modelling time)

Students are introduced to the characteristics of longitudinal data, e.g. how repeated measures for the same subject over time are correlated. The students will learn why it is important to take the correlation over time into account. Longitudinal data will be modeled as multi-level data (modeling time); correlation structures in longitudinal data are discussed.

Technical issues in multilevel/longitudinal modelling

Students learn (and apply) the methods for model reduction/selection/ testing and REML estimation in linear mixed models (LMM). Students are also introduced to checking model assumptions / model diagnostics in LMM. Centering explanatory variables and adding polynomial terms to the model will be discussed and are consequently applied by the student in R.

Beyond the Linear Mixed Model

Students are introduced to multi-level models for binomial, Poisson and survival outcomes. Different estimation procedures used by software to apply mixed models are touched upon.

Assessment

To successfully complete this course, you need to actively participate in the discussion forums and complete the learning unit assignments, including:

• Individual and group assignments
• The completion of a quiz at the end of the first four learning units
• A final assignment: this will be a presentation of a case study by the student. The submission deadline and the date for the redo session will be announced as soon as possible.