Multilevel modelling I: An introduction to multilevel modellingMethodology courses and philosophy of science

Course information

ECTS: 2.5 
Number of sessions: 4
Hours per session: 3
Entry level: Introduction
Course fee:

  • free for PhD candidates of the Graduate School
  • €440,- for non-members
  • consult our enrolment policy for more information


Telephone: +31 (0)10 4082607 (Graduate School)

Session 1
March 21 (Wednesday) 2018
Mandeville building (directions) room T17-01

Session 2
March 28 (Wednesday) 2018
Mandeville building (directions) room T17-01

Session 3
April 4 (Wednesday) 2018
Mandeville building (directions) room T17-01

Session 4
April 11 (Wednesday) 2018
Mandeville building (directions) room T17-01

Aims and working method

In this course, PhD candidates will get an introduction into the theory of multilevel modelling, while focussing on two level multilevel models with a 'continuous' response variable.

In addition, during the final two sessions, participants will focus on the methodology (needs and questions) of their own research. During these sessions, you have the opportunity to get into depths with planning your approach, or with further analysing your data.

There are four sessions consisting of workshops with mini-lectures and practical training directly applied to participant work. Participants will be informed well in advance by email on how to prepare for the sessions.

Learning objectives

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).

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).

Session descriptions

Session 1: 
Introduction to multilevel modelling

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

  • Preparation
  • 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

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

Session 4:
Methodological and statistical issue and own research

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

    About the instructor

    Joran Jongerling is a lecturer at the EUR Department of Pedagogical and Education Sciences, where he convenes courses in research methodology, SPSS skills and applied multivariable data analysis. In addition, he is in the final stages of his PhD project at Utrecht University on 'Modelling individual differences in intra-individual change and variability'. In his research he applies and analyses principles from Bayesian statistics, Multilevel analysis, and Structural Equation Modelling.