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Quiz

Week 7

Gelöschter Nutzer 9 Fragen Aktualisiert am 19 Mai 2019

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Fragen in diesem Quiz (9)

  1. 1I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?
    1. AThat this experiment has 2 IVs. Two of them have 3 levels.
    2. BThat this experiment has 3 IVs. Two of them have 3 levels and one has two.
    3. CThat this experiment has 3 IVs. Three of them have 3 levels.
    4. DThat this experiment has 3 levels. Two of them have 3 IVs and one has two.
  2. 2What information do we get from a factorial ANOVA?
    1. AWe can see the main effects of each IV and how they interact.
    2. BWe can see the main effects of each DV.
    3. CWe can see the main effects of each DV and how they interact.
    4. DWe can see the main effects of each IV.
  3. 3Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?
    1. AThe variance explained by SSM is made up of the SS for each variable plus the SS for the interactions.
    2. BYou cannot further split the variance explained by SSM.
    3. CThe variance explained by SSM is made up of the MS for each variable plus the MS for the interactions.
    4. DThe variance explained by SSM is made up of only the SS for each variable.
  4. 4I have two factorial IVs: age and gender. How do we look at the main effect of age?
    1. AWe average across all levels of gender and only look only at the differences in gender groups
    2. BWe average across all levels of age and only look only at the different levels of gender groups
    3. CWe average across all levels of gender and only look only at the differences in age groups
    4. DWe average across all levels of age and only look only at the differences in age group
  5. 5Following from the previous question. I have calculated SSage and SSgender. How do I calculate SSage*gender. What does this tell me?
    1. AAfter 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.
    2. BAfter 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.
    3. CAfter 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
    4. DYou do not get SSage*gender in independent samples factorial ANOVA.
  6. 6What is an interaction?
    1. AWhen the effect of one IV on the DV is dependent on another IV.
    2. BWhen both IVs have a main effect.
    3. CWhen both DVs have a main effect.
    4. DWhen the effect of one DV on the IV is dependent on another DV.
  7. 7As 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 _____________
    1. ASphericity; the Mauchly's test should not be significant
    2. BHomogeneity of variance; the Levene's test should be significant.
    3. CHomogeneity of variance; the Levene's test should not be significant
    4. DSphericity; the Mauchly's test should be significant.
  8. 8After completing our factorial ANOVA – why do we need to test the simple effects?
    1. ATo understand the effects of the individual variables
    2. BBecause we want to examine the differences between the IVs.
    3. CYou don't need to do this as it shows the same as the main effects.
    4. DBecause this is the best way to explain an interaction, if an interaction exist.
  9. 9Why can't we only interpret the F value from the SSM (i.e.“Corrected Model”) line of the output?
    1. ATrick question - we only interpret the SSM line of output in factorial ANOVA.
    2. BBecause we need to know how much variance is explained by the SSR output, which is part of the variance explained by SSM.
    3. CBecause 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.
    4. DBecause 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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Beschreibung
Der Nutzer hat seine Fachbereichs-Information gelöscht. Quiz am Week 7, erstellt von Gelöschter Nutzer am 19/05/2019.
Autor
Gelöschter Nutzer
Sprache
English
Aufrufe
18
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Erstellt am 19 Mai 2019 · aktualisiert am 19 Mai 2019

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