Introduction
Multilevel modelling is an important and valuable method that can be used to analyse ‘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).
Course information
ECTS: 2.5
Number of sessions: 4
Hours per session: 3
Key Facts & Figures
- Type
- Course
- Instruction language
- English
- Mode of instruction
- Offline
Start dates for: Multilevel modelling
Session 1
April 1 (Tuesday) 2025
09:30-12:30 hrs
Mandeville building (campus mapOpens external), room T19-01
Session 2
April 8 (Tuesday) 2025
09:30-12:30 hrs
Mandeville building (campus mapOpens external), room T19-01
Session 3
April 15 (Tuesday) 2025
09:30-12:30 hrs
Mandeville building (campus mapOpens external), room T19-01
Session 4
April 22 (Tuesday) 2025
09:30-12:30 hrs
Mandeville building (campus mapOpens external), room T19-01
What will you achieve?
- After this course you will understand the theoretical aspects of multilevel modelling.
- After this course you will know under which conditions and for which problems and data multilevel modelling can be used.
- After this course you will know how to do multilevel modelling (on two levels) with the software program R.
- After this course you will have a first general indication of whether and how multilevel modelling can be applied on their own research.
Aims and working method
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.
Entry level
To attend this course properly participants should ideally have basic knowledge of the program R. If you do not have such knowledge yet, you can first follow our course on Data Analysis with R. If you doubt whether you have sufficient knowledge about R, please contact the lecturer, Marleen de Moor.
Session descriptions
- 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.
- 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.
- Prepare questions on your own research.
- Bring your laptop to class.
- Before class, send in questions about your own research. You will receive personal feedback during class.
- Bring your laptop to class.
Instructor
Marleen de Moor is an Associate Professor at the EUR Department of Psychology, Education and Child Studies, where she gives courses in research methodology and statistics. In her research she develops and applies advanced data analysis techniques such as Multilevel analysis, Structural Equation Modelling, Factor Analysis and Time Series Analysis.Email address
Contact
- Enrolment-related questions: enrolment@egsh.eur.nl
- Course-related questions: demoor@essb.eur.nl
- Telephone: +31 (0)10 4082607 (Graduate School)
Facts & Figures
- Fee
- free for PhD candidates of the Graduate School
- €575,- for non-members
- consult our enrolment policy for more information
- Tax
- Not applicable
- Offered by
- Erasmus Graduate School of Social Sciences and the Humanities
- Course type
- Course
- Instruction language
- English
- Mode of instruction
- Offline