Chapter 11

Question 1

Define interaction of two independent variables

Answer 1

Whether the effect of the original independent variable depends on another independent variable

Interaction = a difference in differences

Question 2

Define crossover interaction

Answer 2

An interaction where the lines on the graph cross each other

Question 3

Define spreading interaction

Answer 3

Lines are not parallel and do not cross over

Question 4

Define a factorial design

Answer 4

Experiment design

One in which there are two or more independent variables.

Tests all possible combinations of conditions

If a study has two IV and each IV has two levels there will be four conditions

Question 5

Define nested factorial designs

Answer 5

One independent variable is primary, and the other independent variable is nested under it

(Ex: researchers might study two school districts and then study three schools within each district)

Question 6

Define participant variable

Answer 6

A variable whose levels are selected, not manipulated

Ex: age, sex, race

Question 7

Define moderator

Answer 7

(Interactions show moderators)

An independent variable that changes the relationship between another independent and dependent variable

(Results in an interaction)

Question 8

Define a main effect

Answer 8

The overall effect of one independent variable on the dependent variable, averaging over the levels of the other independent variable

Question 9

Define marginal means

Answer 9

The means for each level of an independent variable, averaging over levels of the other independent variable

Question 10

If lines are not parallel in a line graph then there is probably…

Answer 10

An interaction

Can’t confirm without significance test

Question 11

In a bar graph if the bars are the same height then there probably is

Answer 11

No interaction

Can’t confirm without test

Question 12

What’s the best way to describe an interaction

Answer 12

Start with one level of the first independent variable and explain what is happening with the second independent variable at that level, and then moved to the next level of the first independent variable and do the same thing

Ex: when people poured from the small bottle, they poured more detergent when it was on sale than when it was regular priced. When people poured from the large bottle, they poured the same amount of detergent when it was on sale as one it was regular price

Question 13

What’s more important the main effect or an interaction

Answer 13

Interaction

Question 14

If lines are not parallel in a line graph then there is probably…

Answer 14

An interaction

Can’t confirm without significance test

Question 15

In a bar graph if the bars are the same height then there probably is

Answer 15

No interaction

Can’t confirm without test

Question 16

What’s the best way to describe an interaction

Answer 16

Start with one level of the first independent variable and explain what is happening with the second independent variable at that level, and then moved to the next level of the first independent variable and do the same thing

Ex: when people poured from the small bottle, they poured more detergent when it was on sale than when it was regular priced. When people poured from the large bottle, they poured the same amount of detergent when it was on sale as one it was regular price

Question 17

What’s more important the main effect or an interaction

Answer 17

Interaction

Question 18

Define within groups

Answer 18

The same people participate at each level

One group participates in all 4 combinations

Question 19

Define between groups (independent-groups)

Answer 19

Different people participate at each level

Question 20

Define mixed factorial design

Answer 20

One independent variable is manipulated as independent groups (between) and the other is manipulated as within groups

Question 21

Why do we use factorial design’s

Answer 21

It has better external validity

Typically, our behavior depends on more than one variable by testing multiple factors we better approximate the “normal” conditions

(Ex: plant growing w both water and sunlight)

But we are limited with how many factors we can include (# of participants, complexity of data analysis)

Question 22

Why do we use factorial design (dependencies)

Answer 22

Many Times the answers to our research questions is it depends

(Ex: best therapy depends on the patient)

Factorial designs allows us to assess these dependencies

Question 23

Why use factorial designs? (Interaction)

Answer 23

If the effect of one variable depends on another the variables are said to “interact”

Factorial designs allows us to test for interactions

(Ex: does the effect of light depend on how much water the plant has)

Question 24

How do you describe the findings and there’s not an interaction

Answer 24

Ex: there’s not an interaction between them out of coffee drunk and I’m out of beer drunk. The fact of how much beer was drunk was the same no matter how much coffee was drunk

Question 25

How do you calculate an interaction

Answer 25

Calculate the size of the effect of one variable (by subtracting one value from the other) for each level of the other variable

If they’re the same then there is no interaction

(Find differences)

Question 26

Describing a main effect

Answer 26

Ex: There was a main effect of coffee.

On average, participants who drank two cups of coffee slept 2 hours less than participants who drank no cups of coffee

Question 27

How do you calculate main effect

Answer 27

Add and divide sections it asks about and compare sum (find avg)

If sum is the same there is no main effect