Week 7

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Psychology Quiz on Week 7, created by Katie Morbey on 19/05/2019.
Katie Morbey
Quiz by Katie Morbey, updated more than 1 year ago
Katie Morbey
Created by Katie Morbey over 5 years ago
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Question 1

Question
I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?
Answer
  • 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.

Question 2

Question
What information do we get from a factorial ANOVA?
Answer
  • 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.

Question 3

Question
Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?
Answer
  • 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.

Question 4

Question
I have two factorial IVs: age and gender. How do we look at the main effect of age?
Answer
  • 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

Question 5

Question
Following from the previous question. I have calculated SSage and SSgender. How do I calculate SSage*gender. What does this tell me?
Answer
  • 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.

Question 6

Question
What is an interaction?
Answer
  • 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.

Question 7

Question
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 _____________
Answer
  • 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.

Question 8

Question
After completing our factorial ANOVA – why do we need to test the simple effects?
Answer
  • 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.

Question 9

Question
Why can't we only interpret the F value from the SSM (i.e.“Corrected Model”) line of the output?
Answer
  • 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.
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