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Unformatted text preview: 4-12-11Factorial ANOVA•With factorial designs, the sources of treatment variability increase•Instead of having one IV as the sole source of treatment variability, factorial designs have multiple IVs and their interactions as sources of treatment variability•The actual distribution of the variance among the factors would depend, of course, on which effects were significant•For a two-IV factorial design, we use the following equations:oFA = IV A variability/ error variabilityoFB = IV B variability/ error variabilityoFAxB = interaction variability/ error variabilityUnderstanding Interactions•When two variables interact, their joint effect may not be obvious or predictable from examining their separate effects.oFor example, drinking a glass or two of wine may be a pleasurable and relaxing experience and driving may be a pleasurable and relaxing experience but is drinking wine and driving an extremely pleasurable and relaxing experience?•A significant interaction means that the effects of the various IV’s are not straightforward and simple•For this reason, we virtually ignore our IV main effects when we find a significant interaction•Sometimes interactions are difficult to interpret, particularly when we have more...
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This note was uploaded on 04/05/2012 for the course PSY 3213 taught by Professor Staff during the Spring '11 term at FSU.
- Spring '11