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1

Factor

An explanatory variable (synonym for IV/PV)

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2

Factorial design

Research design that includes 2 or more factors, e.g., 2×3×2—3 factors with 7 conditions

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3

Factorial design disadvantages

-more complex

-harder to control all variables (lower internal validity)

-need more participants to have enough power

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4

Factorial design advantages

-more efficient than doing separate experiments to test the effect of each IV

-more external validity

-allows for testing of main effects & interactions

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5

Main effect

Mean differences among the levels/conditions of one factor

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6

There is the same number of main effects as __

Factors

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7

Interaction

One factor has a direct influence on the effect of another factor

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8

T/F: Independent variables have interactions

False

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9

One drug exaggerating the effect of another drug is an example of what?

Interaction

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10

How can you tell if an interaction is present?

The lines denoting the difference between the main effects aren’t parallel

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11

The two-factor study allows researchers to evaluate three separate sets of mean differences, which are…

Mean differences from the main effects of factor 1, 2, and interaction between factors

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12

What are the different types of factorial designs?

-between

-within

-mixed

-combined

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13

Between factorial design

All factors manipulated between participants

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14

Within factorial design

All factors manipulated within participants

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15

Mixed factorial design

At least one factor manipulated between & within participants

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16

Combined factorial design

One factor is experimental and another isn’t; may or may not be mixed

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17

__ depends on the level of measurement for the DV—continuous or discrete?

Statistical analysis

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18

Statistical analysis also depends on what?

-level of measurement of factors (continuous/discrete)

-what the factors are (between, within, etc.)

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19

What statistical analysis do we use for a between subjects design with a continuous DV & discrete factors?

N-way ANOVA

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20

What statistical analysis do we use for a between subjects design with a continuous DV & continuous factors?

ANCOVA

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21

What statistical analysis do we use for a within subjects design with a continuous DV & discrete factors?

N-way repeated measures ANOVA

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22

What statistical analysis do we use for a within subjects design with a continuous DV & continuous factors?

Repeated measures ANCOVA

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23

What statistical analysis do we use for a mixed design with a continuous DV?

Repeated measures N-way ANOVA

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24

What statistical analysis do we use for a combined/non-manipulated design with a continuous DV?

-between: N-way ANOVA/ANCOVA

-within/matched: repeated measures N-way ANOVA/ANCOVA

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25

When do we use ANOVA?

With discrete factors

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26

When do we use ANCOVA?

With continuous factors

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27

What do you do if the main effects are significant?

Post-hoc analyses

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28

Post-hoc analyses

-Bonferroni corrections (conservative; reduced power; higher threshold for rejection—smaller alpha)

-Tukey test (honest significant test)—groups have equal variance

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