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Multilevel and Longitudinal Data Analysis


Entry requirements

Master’s students in Psychology with a basic understanding of the concepts underlying multiple regression analysis.


In empirical research we often have nested data. Examples of nested data are when we have measurements of children from different classes or school, and measurements of employees in firms. One important class of nested data is longitudinal data, where there are measurements at different time points nested within an individual.

Nested data create dependent observations, i.e. children in one class are more alike than children from different classes or measurements of one subject are more alike than measurements of different subjects. The statistical analysis needs to take into account this dependency. Two classes of regression models exist that deal with this dependency: the first class ignores the dependency when etsimating the regression weights but adjusts standard errors to obtain valid inference; the second class include includes specific parameters in the regression model that account for the dependency. The latter model is the so-called multilevel regression model. In this course these 2 types of regression models will be introduced and explained in much detail.

Course objectives

Upon completion of this course, students will:

  • Be able to distinguish between nested and non-nested data;

  • Know how to determine the degree of dependency by means of the intraclass correlation;

  • Understand the bootstrap and knows how to use it in longitudinal data analysis;

  • Learn R software for applying the bootstrap method in longitudinal data analysis;

  • Acquire a basic understanding of the multilevel model (and the process of building such a model);

  • Get some insights into how multilevel modelling can be used for three-level and cross-classified data and for data with a dependent variable that is not normally-distributed (e.g., logistic mixed regression model);

  • Understand how multilevel models deal with dependency; and

  • Learn R software for fitting multilevel models.


For the timetables of your lectures, work groups and exams, please select your study programme in:
Psychology timetables



Students need to enroll for lectures and work group sessions.
Master’s course registration


Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date. Students who are not registered will not be permitted to take the examination.
Registering for exams

Mode of instruction

  • 7 lectures

  • Supervised computer practicals

Assessment method

2 take-home assignments.

The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.

Reading list

  • Singer, J. D. and Willett, J. B. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press, Inc. (Chapters 1-8)

  • Hox. J (2010). Multilevel analysis. Techniques and applications (2nd ed.). New York, NY: Routledge. (Chapters 1-6, see

  • Papers distributed on Blackboard

  • M. de Rooij (2012). Standard regression models for repeated measures data.

  • Chapter 2 (The basic two level regression model) from the book of Joop Hox.

Contact information

Dr. Tom F. Wilderjans