Understanding Interactions in Multi-Factor Analyses
Explore the importance of modeling interactions in multi-factor analyses, how they allow effects of one factor to vary across levels of another factor, and when to include or exclude interactions in your model based on research questions and experimental design.
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Presentation Transcript
Multi-factor analyses Introductory example When to model interactions 1
I have simulated data to introduce multi-Factor analyses Treatment Sex Female Control XXXXXXXXX XXXXXXXXX XX (20 X s) XXXXXXXXX XXXXXXXXX XX (20 X s) Drug XXXXXXXXX XXXXXXXXX XX (20 X s) XXXXXXXXX XXXXXXXXX XX (20 X s) Male 2
What do interactions do in a multi-factor model? Interactions allow any effects of one factor to differ among levels of another factor If a model omits an interaction, we must assume that effects of one factor do not depend upon another factor 4
When can a model include interactions? Modeling an interaction requires appropriate replication Treatment Treatment High XX XX Low XX XX High X X Low X X Female Male Female Male Model interaction? Yes! Can also model Sex and Treatment. Model interaction? No! But can model Sex and Treatment. 5
When should a model include interactions? Your research question is about an interaction (e.g., does one sex respond more to a change in drug concentration?) Modeling the data well Experimental design includes interaction Remove non-significant interactions from a model? Debatable. 6
