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In this module we discuss how to analyze dependent data, that is, data for which the assumption of independence needed in Linear Models is violated. So: Do you have a nested experimental setup? Like measurements on large plots, but also on smaller plots within the larger plots? Do you have repeated measurements? Like measurements on height of the same plant over time? Or weight of the same animal over time? Do you have pseudo-replication? Like measuring 3 plants from the same pot? In this sort of situations it is not reasonable to use ordinary ANOVA or regression to analyze your data. These methods are likely too optimistic, and you will get erroneous significant results. And your paper will be returned for, hopefully, a major revision! With mixed linear models a more appropriate model, allowing for dependence between observations, can be specified, which will lead to more reasonable conclusions.
In this module, you will learn about these models (also about the formulation in matrix notation, covariance matrices included), about the way to fit them to your data using software, and about the output produced by the software. In computer sessions participants can practice fitting models of this type, and gain an understanding of the output created by the software. Please bring along your own data if you have any!
|Course Frequency||once per year|
|Prior knowledge||Knowledge level obtained from the Linear Models Module|
|Intended credits||0.6 ECTS|
|Course organisation||The C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC)|
|More information||Course website|