Introduction
Key terms: quantitative research, multilevel modelling, hierarchical data, correlational analysis, causality, R software package, introductory course, relevant for students in any PhD phase.
ECTS: 2.5
Number of sessions: 4
Hours per session: 3
Multilevel modelling is an important and valuable statistical 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).
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.
Please note that for making notes and completing assignments, participants need to bring a laptop to each class meeting.
Entry level and relevance
This course is relevant for students in any PhD phase who conduct quantitative research on relationships among variables assessed at different levels.
To attend the course properly, however, participants should ideally have basic knowledge of the program R. If you do not have such knowledge yet, you can first follow the EGSH course Data literacy through R . If you doubt whether you have sufficient knowledge about R, please contact the lecturer, Marleen de Moor.
Relations with other courses
This course builds on the EGSH course Data literacy through R and serves as preparation for the course Experience sampling: Opening the black box of daily life.
Further, multilevel techniques can be combined with structural equation modelling (SEM) techniques, so following this course together with the EGSH course Structural Equation Modelling course is useful if students are interested in multilevel SEM . These courses can be followed independently from each other, and it does not matter in which order they are attended.
Key Facts & Figures
- Type
- Course
- Instruction language
- English
- Mode of instruction
- Offline
Start dates for: Multilevel modelling
Edition 1
Session 1: November 26 (Wednesday) 2025 | 10.00-13.00 hrs | Offline (Mandeville building, room T19-01)
Session 2: December 3 (Wednesday) 2025 | 10.00-13.00 hrs | Offline (Mandeville building, room T19-01)
Session 3: December 10 (Wednesday) 2025 | 10.00-13.00 hrs | Offline (Mandeville building, room T19-01)
Session 4: December 17 (Wednesday) 2025 | 10.00-13.00 hrs | Offline (Mandeville building, room T19-01)
Sessions and preparations
Session 1: Introduction to multilevel modelling
In this session you will be introduced to the concept of hierarchical data and you will learn to evaluate when multilevel analysis is appropriate.
Preparation:
• Read chapter 1 (Introduction to multilevel analysis) of Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2002). Multilevel analysis: Techniques and applications. Lawrence Erlbaum Associates. Available online (PDF)
• Download and install the free and open source programs R and Rstudio
Session 2: The basic two-level regression model and the R program
In this session you will learn how to build your multilevel model, starting with an intercept only up to a model with random effects and cross-level interactions.
Preparation: Read chapter 2 (The basic two-level regression model) of Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2002). Multilevel analysis: Techniques and applications. Lawrence Erlbaum Associates.
Session 3: Multilevel modelling for longitudinal data
In this session you will learn how multilevel analysis can be applied to longitudinal data, which is a specific case of hierarchical data.
Preparation: Prepare questions on your own research.
Session 4: Methodological and statistical issues and your own research
In this session we will discuss methodological and statistical issues like sample size and power. Further, you will apply what you have learnt to your own dataset or PhD topic. There will be plenty of time for remaining questions.
Preparation: Before class, send in question about your own research. You will receive personal feedback during 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