In this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. In addition, participants will learn how to run basic two-level model in R. The objective of this course is to get participants acquainted with multilevel models. These models are often used for the analysis of ‘hierarchical’ data, in which observations are nested within higher level units (e.g. repeated measures nested within individuals, or pupils nested within schools).
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In this type of data causes of outcomes (e.g. the performance of pupils in schools) are located both at the level of the individual (e.g., own and parental resources), and at a higher, contextual, level shared by some of the individuals (e.g. characteristics of the class and of the teacher). Because of this, the assumption of 'independent observations' is violated with hierarchical data, but multilevel modelling can easily account for that. Moreover, multilevel modelling can easily deal with missing data (in most circumstances).
Syllabus
WEEK 1: Errata
WEEK 2: Introduction to Multilevel Modeling (MLM)
WEEK 3: Random Slopes and Cross-Level Interactions
WEEK 4: Putting it all Together
