In this module we discuss how to analyse dependent data, that is, data for which the assumption of independence needed in Linear Models is violated. So: Do you have a nested experimental set-up? 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 analyse 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 we will start with a refresher of the Linear Model (ANOVA, regression, ANCOVA), because it is the starting point of the Mixed Model. There will be attention for the matrix formulation of the Linear Model. After this we will gradually introduce the Mixed Model (also about the formulation in matrix notation, especially with respect to covariance matrices), the way to fit them to your data using software, and 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. You are encouraged to bring along your own data if you have any. The main statistical software used in this course is R.
Target Group | The course is aimed at PhD candidates and other academics |
Group Size | 24 participants |
Course duration | 3 days |
Language of instruction | English |
Frequency of recurrence | Once a year (Summer) |
Number of credits | 0.9 ECTS |
Lecturers | Dr. Gerrit Gort (Biometris, Wageningen University) |
Prior knowledge | To participate in this course one must have knowledge of Basic Statistics and some knowledge of Linear Models. Some experience with the software package R is advisable. |
Location | Wageningen University Campus |
Accommodation | Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For non-WUR PE&RC members 50% of the accommodation costs can be reimbursed with a maximum of €30,- per night, please contact the PE&RC Office (office.pe@wur.nl) for more information. For information on B&B's and hotels in Wageningen please visit Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Finally, there are a number of groups on Facebook where students announce subrent possibilities and things like that. Examples include: Wageningen Room Subrent, Wageningen Room Sublets, Room Rent Wageningen, and Wageningen Student Plaza. Note that besides the restaurants in Wageningen, there are also options to have dinner on Wageningen Campus. |
EARLY-BIRD FEE 2 | REGULAR FEE 2 | |
PE&RC / WIMEK / VLAG / WIAS / WASS / EPS PhD candidates with an approved TSP and WU EngD candidates | € 150,- | € 200- |
PE&RC postdocs and staff | € 300,- | € 350,- |
All other academic participants | € 340,- | € 390,- |
Non-academic participants | € 640,- | € 690,- |
1 The course fee includes a digital reader.
2 The Early-Bird Fee applies to anyone who REGISTERS BEFORE 7 April 2025
Note:
IMPORTANT: ALWAYS read the Cancellation conditions for PE&RC courses and activities.
Dr. Gerrit Gort
Phone: +31 (0) 317 483570
Email: gerrit.gort@wur.nl
Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116
Email: claudius.vandevijver@wur.nl
To register, please enter your details below and click "Register".