
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.
By the end of the Mixed Models course from Utrecht University , you will be able to:
Starting in
You can apply until:
Everyone
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Enrollment deadline 20 Sep
6 weeks
1.5 EC
Courses include:
This programme may require students to demonstrate proficiency in English.
To enroll in this course, you need:
Check the programme website for information about funding options.
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