Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives
2015
Background
In neuroscience, experimental designs in which multiple measurements are collected in the same research object or treatment facility are common. Such designs result in clustered or nested data. When clusters include measurements from different experimental conditions, both the mean of the dependent variable and the effect of the experimental manipulation may vary over clusters. In practice, this type of cluster-related variation is often overlooked. Not accommodating cluster-related variation can result in inferential errors concerning the overall experimental effect.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
26
References
42
Citations
NaN
KQI