I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?
That this experiment has 2 IVs. Two of them have 3 levels.
That this experiment has 3 IVs. Two of them have 3 levels and one has two.
That this experiment has 3 IVs. Three of them have 3 levels.
That this experiment has 3 levels. Two of them have 3 IVs and one has two.
What information do we get from a factorial ANOVA?
We can see the main effects of each IV and how they interact.
We can see the main effects of each DV.
We can see the main effects of each DV and how they interact.
We can see the main effects of each IV.
Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?
The variance explained by SSM is made up of the SS for each variable plus the SS for the interactions.
You cannot further split the variance explained by SSM.
The variance explained by SSM is made up of the MS for each variable plus the MS for the interactions.
The variance explained by SSM is made up of only the SS for each variable.
I have two factorial IVs: age and gender. How do we look at the main effect of age?
We average across all levels of gender and only look only at the differences in gender groups
We average across all levels of age and only look only at the different levels of gender groups
We average across all levels of gender and only look only at the differences in age groups
We average across all levels of age and only look only at the differences in age group
Following from the previous question. I have calculated SSage and SSgender. How do I calculate SSage*gender. What does this tell me?
After calculating SSage and SSgender then the remaining variance accounted for by SSM is the variance from SSAge*gender. This is the main effect of the two variables.
After calculating SSage and SSgender then the remaining variance accounted for by SSM is the variance from SSage*gender This is the interaction between the two variables.
After calculating SSage and SSgender then the remaining variance accounted for by SST is the variance from SSage*gender. This is the interaction between the two variables
You do not get SSage*gender in independent samples factorial ANOVA.
What is an interaction?
When the effect of one IV on the DV is dependent on another IV.
When both IVs have a main effect.
When both DVs have a main effect.
When the effect of one DV on the IV is dependent on another DV.
As my study is a factorial between subjects design, the relevant assumption I should be concerned about is _____________. If this assumption is met, I would expect to see that _____________
Sphericity; the Mauchly's test should not be significant
Homogeneity of variance; the Levene's test should be significant.
Homogeneity of variance; the Levene's test should not be significant
Sphericity; the Mauchly's test should be significant.
After completing our factorial ANOVA – why do we need to test the simple effects?
To understand the effects of the individual variables
Because we want to examine the differences between the IVs.
You don't need to do this as it shows the same as the main effects.
Because this is the best way to explain an interaction, if an interaction exist.
Why can't we only interpret the F value from the SSM (i.e.“Corrected Model”) line of the output?
Trick question - we only interpret the SSM line of output in factorial ANOVA.
Because we need to know how much variance is explained by the SSR output, which is part of the variance explained by SSM.
Because we don't just need to know how much variance is explained by the model but whether each individual variable is a explaining a significant amount of variance.
Because we don't just need to know how much variance is explained by the model but whether each individual variable and their interactions is a explaining a significant amount of variance.
