Course description

In empirical research we often have nested data. Examples of nested data are when we have measurements of children from different classes or school, measurements of employees in firms, or measurements of members of different organizations such as parties. 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, known as repeated measures ANOVA, 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 (also known as hierarchical model or mixed model).

In this course the multilevel regression model will be discussed and explained in much detail. Also attention will be paid to how it can be fitted to data by making use of the R software. In this course we will deal with more advanced multilevel modeling topics. First, we will discuss generalized multilevel models which can be used when the dependent variable is not continuous but, for example, binary (logistic multilevel regression). Second, we will also discuss non-linear multilevel models. For example, polynomial (curvilinear, cubic, …) growth models. Third, more complex hierarchical data structures will be touched upon, like multiple membership models (a student having multiple teachers) and three-level models (students nested in classes nested in schools). Finally, we will very briefly sketch further modeling possibilities, like timeseries analysis (VAR, ARIMA) and SEM.

Mode of instructions

 • The course will be on campus
• Interactive lecture (theory and illustrative examples in R)
• Practical sessions with exercises in R (students work on the
exercises and solutions are discussed at the end)
• Students can use (and should bring) their own laptop with R
(and Rstudio) installed
• Students will be asked to do some preparations in advance
(e.g., read parts of a book, watch a video, prepare an
exercise). Instructions follow in the beginning of January
2026 (for those enrolled in the course).

Reading List

• Luke, D. A. (2004). Multilevel modeling. Sage University 
Paper Series on Quantitative Applications in the Social 
Sciences, 07-143. Thousand Oaks, CA: Sage.
• 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 
6 & 9, see joophox.net/mlbook2/MLbook.htm)
• Raudenbush, S. W., & Bryk, A. S. (2001). Hierarchical linear 
models: Applications and data analysis methods (2nd ed.). 
Thousand Oaks, CA: Sage.

Entry requirements

• Open to doctoral students and staff members of the Faculty 
of Social and Behavioral Sciences, ASCL and ICLON. Also 
externals can participate in the course.
• Basic knowledge of multilevel modeling (basic course) and 
R required (a list of materials that one can go through to 
reach the required R level can be obtained upon request 
from the coordinator).