Research Methods IV - Week 7 Factorial ANOVA I

Frage 1 von 10

1

I'm doing a three way ANOVA with a 3x3x2 design, what does this tell you?

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That this experiment has 2 IV's. Two of them have three levels
That this experiment has 3 levels. Two of them have 3 IV's and one has two
That this experiment has 3 IV's. Three of them have 3 levels
That this experiment has 3 IV's. Two of them has 3 levels and one has two.

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Frage 2 von 10

1

What information do we get from a factorial ANOVA?

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We can see the main effects of each DV
We can see the main effects of each IV, and how they interact
We can see the main effects of each DV, and how they interact
We can see the main effects of each IV

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Frage 3 von 10

1

Within the variability explained by SSm, how can we further split the variance in an independent measures factorial ANOVA?

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You cannot further split the variance explained by SSm
The variance explained by SSm is made up of only the SS for each variable
The variance explained by SSm is made up of the SS for each variable plus the SS for the interactions
The variance explained by SSm is made up of the MS for each variable plus the MS for the interactions

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Frage 4 von 10

1

I have two factorial IV's: Age and gender. How do we look at the main effect of age?

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We average across all levels of gender and only look at the differences in age groups
We average across all levels of age and only look at the differences in age group
We average across all levels of age and only look at the different levels of gender groups
We average across all levels of gender and only look at the differences in gender

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Frage 5 von 10

1

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

Wähle eine der folgenden:

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
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
You do not get SSage*gender in independent samples factorial ANOVA

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Frage 6 von 10

1

What is an interaction?

Wähle eine der folgenden:

When both IV's have a main effect
When the effect of one DV on the IV is dependent on another DV
When the effect of one IV on the DV is dependent on another IV
When both DV's have a main effect

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Frage 7 von 10

1

The following graph summarises the interaction effect of age and gender on colour perception test scores (DV). What does this interaction show?

Wähle eine der folgenden:

Colour perception improvement with age did not differ both boys and girls. For both genders, colour perception was better for 11 year olds compared to five year olds.
Colour perception improvement with age differed between boys and girls. For boys, no difference in colour perception was found between 5 year olds and 11 year olds. However, for girls there was an effect of age on colour perception.
Colour perception improvement with age did not differ both boys and girls across the ages. For both genders, colour perception was better for 5 year olds compared to 11 year olds.
Colour perception improvement with age differed between boys and girls. For girls, no differences in colour perception were found between 5 year olds and 11 year olds. However, for boys there was an effect of age on colour perception.

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Frage 8 von 10

1

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 _.

Wähle eine der folgenden:

Homogeneity of variance; the Levene's test should not be significant.
Sphericity; the Mauchly's test should be significant.
Sphericity; the Mauchly's test should not be significant.
Homogeneity of variance; the Levene's test should be significant.

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Frage 9 von 10

1

After completing our factorial ANOVA, why do we need to test the simple effects?

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Because we want to examine the differences between the IV's
To understand the effects of the individual variables
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 the interaction exists.

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Frage 10 von 10

1

Why can't we only interpret the F-value from the SSm (i.e. "Corrected Model) line of output?

Wähle eine der folgenden:

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 explaining a significant amount of the variance
Because we need to know how much variance is explained by the SSr output, which is part of the variance explained by SSm
Trick question - we only interpret the SSm line of output in factorial ANOVA
Because we don't just need to know how much variance is explained by the model, but whether each individual variable is explaining a significant amount of the variance

	Erstellt von Brage Haavik vor etwa 5 Jahre

Quiz am Research Methods IV - Week 7 Factorial ANOVA I, erstellt von Brage Haavik am 15/04/2019.

Research Methods IV - Week 7 Factorial ANOVA I

Frage 1 von 10

I'm doing a three way ANOVA with a 3x3x2 design, what does this tell you?

Wähle eine der folgenden:

Erklärung

Frage 2 von 10

What information do we get from a factorial ANOVA?

Wähle eine der folgenden:

Erklärung

Frage 3 von 10

Within the variability explained by SSm, how can we further split the variance in an independent measures factorial ANOVA?

Wähle eine der folgenden:

Erklärung

Frage 4 von 10

I have two factorial IV's: Age and gender. How do we look at the main effect of age?

Wähle eine der folgenden:

Erklärung

Frage 5 von 10

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

Wähle eine der folgenden:

Erklärung

Frage 6 von 10

What is an interaction?

Wähle eine der folgenden:

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Frage 7 von 10

The following graph summarises the interaction effect of age and gender on colour perception test scores (DV). What does this interaction show?

Wähle eine der folgenden:

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Frage 8 von 10

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 ___________.

Wähle eine der folgenden:

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Frage 9 von 10

After completing our factorial ANOVA, why do we need to test the simple effects?

Wähle eine der folgenden:

Erklärung

Frage 10 von 10

Why can't we only interpret the F-value from the SSm (i.e. "Corrected Model) line of output?

Wähle eine der folgenden:

Erklärung

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 _.