Chapter 9 - Factorial designs

Question 1

Factorial designs

Answer 1

Experiments that include more than one independent variable in which each level of one independent variable is combined with each level of the others to produce all possible combinations.

Question 2

Factorial design table

Answer 2

Shows how each level of one independent variable is combined with each level of the others to produce all possible combinations in a factorial design.

Question 3

Between-subjects factorial design

Answer 3

All of the independent variables are manipulated between subjects.

Question 4

Mixed factorial design

Answer 4

A design which manipulates one independent variable between subjects and another within subjects.

Question 5

Non-manipulated independent variable

Answer 5

An independent variable that is measured but is non-manipulated.

Question 6

Main effect

Answer 6

The effect of one independent variable on the dependent variable—averaging across the levels of any other independent variable(s).

Question 7

Interaction effect

Answer 7

When the effect of one independent variable depends on the level of another.

Question 8

Spreading interaction

Answer 8

Means there is an effect of one independent variable at one level of the other independent variable and there is either a weak effect or no effect of that independent variable at the other level of the other independent variable.

Question 9

Cross-over interaction

Answer 9

Means the independent variable has an effect at both levels but the effects are in opposite directions.

Question 10

Simple effects

Answer 10

Are a way of breaking down the interaction to figure out precisely what is going on.

Specifically, a simple effects analysis allows researchers to determine the effects of each independent variable at each level of the other independent variable.

Question 11

Summary simple effects

Answer 11

To summarize, rather than averaging across the levels of the other independent variable, as is done in a main effects analysis, simple effects analyses are used to examine the effects of each independent variable at each level of the other independent variable(s). So a researcher using a 2×2 design with four conditions would need to look at 2 main effects and 4 simple effects. A researcher using a 2×3 design with six conditions would need to look at 2 main effects and 5 simple effects, while a researcher using a 3×3 design with nine conditions would need to look at 2 main effects and 6 simple effects. As you can see, while the number of main effects depends simply on the number of independent variables included (one main effect can be explored for each independent variable), the number of simple effects analyses depends on the number of levels of the independent variables (because a separate analysis of each independent variable is conducted at each level of the other independent variable).