LMM: Random factors
5 important questions on LMM: Random factors
What does a randomized block design look like?
- Take out the variation that can be explained by the river (SSriver)
- add block as random factor
- N-application is carried out on 'SStotal-SSriver'
- remaining analysis is more sensitive to N-appl
- Mixed model: fixed and random factors/effects
What is a latin square design?
What are solutions for pseudoreplication?
- Independent samplig
- work with the means
- (G)LMM + random factor
- Repeated measurs
- Higher grades + faster learning
- Never study anything twice
- 100% sure, 100% understanding
What is the main difference between the randomized block design and the latin square?
Why is a G(z)LMM prefered over a paired t-test (when you have paired data)?
- the possibilities to use different data distributions,
- the relative insensitivity to deviations from normality
- the possibility to have variances that are dissimilar
- you can analyse incomplete designs
- you can include one or more random factors
- you can better analyse datasets with missing data
- you can accommodate a repeated structure in your dataset
The question on the page originate from the summary of the following study material:
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- Never study anything twice again
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