Description
The multilevel regression model – introduction
Social research often involves problems that investigate the relationship between individual and society. The general concept is that individuals interact with the social contexts to which
they belong, meaning that individual persons are influenced by the social groups or contexts to which they belong, and that the properties of those groups are in turn influenced by the individuals who make up that group. Generally, the individuals and the social groups are conceptualized as a hierarchical system of individuals and groups, with individuals and groups defined at separate levels of this hierarchical system. The appropriate analysis technique for such a hierarchical system is multilevel analysis. The benefits of multilevel analysis are discussed both in theory as with empirical examples.
The basic two-level multilevel regression model is introduced. Starting with a model without explanatory variables, the intercept-only model, the intraclass correlation is estimated. In the second and third step, explanatory variables of the individual and the group level, are added to the model. In the next step random regression slopes of the individual variables can be added, answering the question of the relations between the dependent and independent variables are the same for every group. When this is not the case, cross-level interactions can be added to explain
these differences.
We also touch on assumptions within the multilevel context and how to check these.
Longitudinal data
Longitudinal data, or repeated measures data, can be viewed as multilevel data, with repeated measurements nested within individuals. In its simplest form, this leads to a two-level model, whit the series of repeated measures at the lowest level, and the individual persons at the highest level. Longitudinal measures can be taken at fixed or at varying occasions. Multilevel analysis
for longitudinal data can handle both situations. Since multilevel modeling does not require balanced data, it is not a problem if the number of available measurements is not the same for all individuals. Today we learn how to analyze longitudinal data with both fixed and variable occasions. Furthermore, we learn what the advantages of multilevel analysis for repeated measurements are.
Contextual effects
Another opportunity within the multilevel framework is to separate the within effect (i.e., in longitudinal data a process that occurs within a person, or in static data a process that occurs within a cluster) from the between effect (i.e., a process that occurs between persons or between clusters). In the standard multilevel framework, we assume that the within effect is equal to the between effect, which is not necessarily the case. Using contextual effects, we are able to tease the within and between effect apart. Hence, today we also learn what contextual effects are, and how to analyze data that (might) contain contextual effects.
Course prerequisites
The course is open to all interested persons.
Exam / Certificate
There is no exam connected to this module. If you attend all four classes you will receive a certificate of attendance via e-mail at the end of the course.
Type of course
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.
Schedule
- 25/03/2026: from 9am until 4 pm
- 26/03/2026: from 9am until 4 pm
- 27/03/2026: from 9am until 12 am
Venue
Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent,
Course material
Access to slides and data files.
Fees
The participation fee is 975 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations.
- Industry, private sector, profession*: € 975
Non profit, government, higher education staff, (Doctoral) students, unemployed: € 495
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
Registration
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UGent PhD students
Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.
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KMO-portefeuille
Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Organisation
Science Academy
Faculty of Sciences
science-academy@ugent.be
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