Factorial ANOVA Flashcards

1
Q

Define

Factorial ANOVA

A

an Analysis of Variance test with more than one independent variable, or “factor“

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2
Q

Define

Main effects

A

the effect of an independent variable on a dependent variable averaged across the levels of any other independent variables

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3
Q

Define

Interaction effects

A

when the effect of one variable depends on the value of another variable

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4
Q

Define

Orthogonal

A

statistically independent

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5
Q

Definition

an Analysis of Variance test with more than one independent variable, or “factor“

A

Factorial ANOVA

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6
Q

Definition

the effect of an independent variable on a dependent variable averaged across the levels of any other independent variables

A

Main effects

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7
Q

Definition

when the effect of one variable depends on the value of another variable

A

Interaction effects

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8
Q

Definition

statistically independent

A

Orthogonal

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9
Q

Factorial ANOVAs can be independent-measures, repeated-measures and what?

A

Mixed-model

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10
Q

What does a 2 x 3 ANOVA mean?

A

Factor A has 2 levels (i.e. biological sex)

Factor B has 3 levels (i.e. SES)

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11
Q

What does a factorial ANOVA tells us?

A

Examines the effects of each factor (IV) on its own, as well as the combined effects of the factors…

A main effect is the effect of one factor (IV) on its own.

An interaction examines two or more factors at the same time – that is, their combined effect, which may not be predictable based on the effects of either factor on their own.

Thus, factorial ANOVAs produce multiple F ratios – one for each main effect and interaction term

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12
Q

How many F-ratios will be in a two-, three- and four-way ANOVA?

A

Two-way: 3

Three-way: 7

Four-way: 15

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13
Q

What do the three F-ratios of the two-way ANOVA represent?

A

Main effect of A

Main effect of B

A x B interaction

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14
Q

What is a main effect?

A

Mains effects are the mean differences across levels of one factor, collapsing (averaging, or sometimes called marginalizing) the other factor(s).

Effectively main effects examine one factor’s effects, ignoring other factors

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15
Q

What is a marginal effect?

A

The average for everyone in that given factor

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16
Q

When does an interaction occur?

A

An interaction between two factors occurs whenever the mean differences between individual treatment conditions, or cells, are different from what would be predicted from the overall main effects of the factors

17
Q

Based on this graph, what can we conclude?

A

NO EFFECT OF A, NO EFFECT OF B, NO INTERACTION EFFECT

18
Q

Based on this graph, what can we conclude?

A

MAIN EFFECT OF A, NO EFFECT OF B, NO INTERACTION EFFECT

19
Q

Based on this graph, what can we conclude?

A

MAIN EFFECT OF A, MAIN EFFECT OF B, NO INTERACTION EFFECT

20
Q

Based on this graph, what can we conclude?

A

NO EFFECT OF A, NO EFFECT OF B, INTERACTION EFFECT

21
Q

Based on this graph, what can we conclude?

A

MAIN EFFECT OF A, NO EFFECT OF B, INTERACTION EFFECT

22
Q

The variability accounted for by the model (SSM) is due what?

A

The variability accounted for by the model (SSM) is due to differences between the groups (e.g., the treatment effect)

23
Q

What needs to be considered when choosing contrasts?

A
  1. Use control group as reference point
  2. Only 2 pieces
  3. Independence
24
Q

What are the basic guidelines for contrasts?

A
  1. Choose sensible comparisons
  2. Groups coded with positive weights will be compared against groups coded with negative weights
  3. The sum of weights for a comparison should be zero
  4. If a group is not involved in a comparison, automatically assign it a weight of zero
  5. For a given contrast, the weights assigned to the group(s) in one chunk of variation should be equal to the number of groups in the opposite chunk of variation.
25
Q

True or False:

Orthogonal contrasts are not required?

A

True

But they do facilitate interpretation

26
Q

When are two contrasts considered orthogonal?

A

When the products of their weights sum to zero