Only open to Master’s and Research Master’s students from Psychology. A basic understanding of the concepts underlying multiple regression analysis is recommended.
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 estimating the regression weights but adjusts standard errors to obtain valid inference; the second class 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. Also attention will be paid to how these models can be fitted to data by making use of the R software.
Upon completion of this course, students will:
1) Be able to distinguish between different types of nested data (longitudinal and non-longitudinal) and to determine the amount of dependency in the data (intra-class correlation);
2) Have acquired a basic understanding of the multilevel model, the process of building such a model (significance testing, Likelihood ratio test, AIC/BIC, checking assumptions) and the clustered bootstrap procedure; and
3) Learn R software for fitting the multilevel model and applying the clustered bootstrap procedure.
For the timetables of your lectures, work groups and exams, please select your study programme in:
Students need to enroll for lectures and work group sessions.
Master’s course registration
Mode of instruction
7 2-hour lectures
7 2-hour supervised work group sessions
2 2-hour Q&A sessions
The final grade is based on (each with a weight of 50%)
1) a written assignment (individual, half-way the course)
2) a written assignment (in groups of maximum 3 students, at the end of the course)
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.
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 http://joophox.net/mlbook2/MLbook.htm
M. de Rooij (2012). Standard regression models for repeated measures data.
Papers distributed on Blackboard
Dr. Tom F. Wilderjans