Multilevel modelling

In the academic year 2022-2023 this course will take place offline. Location: T19-01.

Multilevel modelling is an important and valuable method that can be used to analyze ‘hierarchical’ data. In such data observations are nested within higher level units. For instance, observations about pupils are nested within schools.

In this type of data outcomes (e.g., the performance of pupils in schools) are caused by factors at both the individual level (e.g., the pupil’s skills), and at a higher, contextual, level shared by some of the individuals (e.g., the  characteristics of the teacher).

We will look into both the theory and practice of multilevel modelling. Participants will learn how to run basic two-level models in the software program R, using both exercise data and their own data.

Before each meeting, participants will have to (individually) follow the assigned parts of our Massive Open Online Course (MOOC) on Coursera.org. During the meetings the theory presented in the MOOC will be discussed in more detail, and any remaining questions will be answered.  

After this course participants will:

• Understand the theoretical aspects of multilevel modelling;
• Know under which conditions and for which problems and data multilevel modelling can be used;
• Know how to do multilevel modelling (on two levels) with the software program R;
• Have a first general indication of whether and how multilevel modelling can be applied on their own research.

Session 1: Introduction to multilevel modelling

• Read Chapter 1:  "Introduction to multilevel analysis" Hox, J. (2002) Multilevel Analysis. Techniques and Applications. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Download and install the free and open source programs R and Rstudio.
• Bring your laptop to class.

Session 2: The Basic Two-Level Regression Model and the R program.

• Read Chapter 2:  The basic two-level regression model: introduction” Hox, J. (2002) Multilevel Analysis. Techniques and Applications. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (pdf).
• Bring your laptop to class.

Session 3: Longitudinal data

• Prepare questions on your own research.
• Bring your laptop to class.

Session 4: Methodological and statistical issue and own research

• Before class, send in question about your own research. You will receive personal feedback during class.
• Bring your laptop to class.