In the biosciences, response variables are often observed more than once per individual. This enables the researcher to study the development of the variable of interest within individuals, thereby eliminating the variation among individuals, and thus increasing the power of the design. However, since observations on the same individual are almost always correlated, special methods are needed to deal with this dependence.
Another way in which data can be dependent is when there is a hierarchical (multilevel) structure in your data, e.g. patients within hospitals, horses within farms, pupils within classrooms, etc.
Mixed models are one way of analyzing this kind of data. This statistical technique allows for the dependency of measurements in hierarchically structured data, and separately examines the effects of variables at different levels. An important part of the course will be about the use (and theory) of linear mixed effects models (LME’s).
Starting with analysis of summary statistics on each individual's observations, this course will lead you to more advanced methods for analyzing multilevel and longitudinal data. Similarities between longitudinal data analysis and multilevel analysis will be clarified.
The theory will be presented during lectures; computer lab sessions using SPSS and R will give you the opportunity to practice your skills on real data sets.
- understand the difference between fixed and random effects
- know when to apply a mixed model in practice
- perform mixed model analyses using statistical software (R, SPSS)
- interpret the output of mixed model analyses in terms of the context of the research question(s)
- know the most commonly used methods for checking model appropriateness and model fit
- report the results of mixed model analyses to non-statistical investigators
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- Multilevel modelling
- Longitudinal data (modelling time)
- Technical issues in multilevel/longitudinal modelling
- Beyond the Linear Mixed Model
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- 42 days
- 7 hrs/week
Start dates & application deadlines
- Apply before
Enrollment deadline 20 Sep
DisciplinesBiomedicine Biology Human Medicine View 3 other Short Courses in Biomedicine in Netherlands
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We are not aware of any academic requirements for this programme.
We are not aware of any English requirements for this programme.
To enroll in this course, you need:
- A BSc degree
- Basic programming experience in R, e.g. the ability to read in data and run a simple linear model
- To have followed at least one course in basic statistical methods up to and including simple and multiple linear regression
- Familiarity with likelihood methods (Wald, score and likelihood ratio tests) will facilitate understanding of the theoretical background.
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International870 EUR/fullTuition FeeBased on the tuition of 870 EUR for the full programme during 42 days.
EU/EEA870 EUR/fullTuition FeeBased on the tuition of 870 EUR for the full programme during 42 days.
Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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Apply and win up to €10000 to cover your tuition fees.