18166532
Descripción
Test por Katie Morbey, actualizado hace más de 1 año
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Creado por Katie Morbey
hace casi 5 años
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Pregunta 1
Pregunta
I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?
Respuesta
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That this experiment has 2 IVs. Two of them have 3 levels.
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That this experiment has 3 IVs. Two of them have 3 levels and one has two.
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That this experiment has 3 IVs. Three of them have 3 levels.
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That this experiment has 3 levels. Two of them have 3 IVs and one has two.
Pregunta 2
Pregunta
What information do we get from a factorial ANOVA?
Respuesta
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We can see the main effects of each IV and how they interact.
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We can see the main effects of each DV.
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We can see the main effects of each DV and how they interact.
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We can see the main effects of each IV.
Pregunta 3
Pregunta
Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?
Respuesta
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The variance explained by SSM is made up of the SS for each variable plus the SS for the interactions.
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You cannot further split the variance explained by SSM.
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The variance explained by SSM is made up of the MS for each variable plus the MS for the interactions.
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The variance explained by SSM is made up of only the SS for each variable.
Pregunta 4
Pregunta
I have two factorial IVs: age and gender. How do we look at the main effect of age?
Respuesta
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We average across all levels of gender and only look only at the differences in gender groups
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We average across all levels of age and only look only at the different levels of gender groups
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We average across all levels of gender and only look only at the differences in age groups
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We average across all levels of age and only look only at the differences in age group
Pregunta 5
Pregunta
Following from the previous question. I have calculated SSage and SSgender. How do I calculate SSage*gender. What does this tell me?
Respuesta
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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.
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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.
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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
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You do not get SSage*gender in independent samples factorial ANOVA.
Pregunta 6
Pregunta
What is an interaction?
Respuesta
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When the effect of one IV on the DV is dependent on another IV.
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When both IVs have a main effect.
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When both DVs have a main effect.
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When the effect of one DV on the IV is dependent on another DV.
Pregunta 7
Pregunta
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 _____________
Respuesta
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Sphericity; the Mauchly's test should not be significant
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Homogeneity of variance; the Levene's test should be significant.
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Homogeneity of variance; the Levene's test should not be significant
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Sphericity; the Mauchly's test should be significant.
Pregunta 8
Pregunta
After completing our factorial ANOVA – why do we need to test the simple effects?
Respuesta
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To understand the effects of the individual variables
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Because we want to examine the differences between the IVs.
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You don't need to do this as it shows the same as the main effects.
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Because this is the best way to explain an interaction, if an interaction exist.
Pregunta 9
Pregunta
Why can't we only interpret the F value from the SSM (i.e.“Corrected Model”) line of the output?
Respuesta
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Trick question - we only interpret the SSM line of output in factorial ANOVA.
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Because we need to know how much variance is explained by the SSR output, which is part of the variance explained by SSM.
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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.
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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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