CHAPTER 11 True Experiments, Part 2: Factorial Designs

Question 1

Factorial design:

Answer 1

research design that involves all combinations of at least two values of two or more independent variables

Question 2

advantages of using factorial designs?

Answer 2

• to examine the effect of two or more variables in a single experiment
• As long as you are designing an experiment, building another independent variable into the design may not require much additional effort
• You might have more than one alternative hypothesis to rule out
• the third reason for studying two or more variables in the same experiment is to reveal interactions among variables
• Another advantage over single-factor designs is that a potentially confounding variable can be built into the design by including that confounding variable as a factor in a factorial design.

Question 3

main effect:

Answer 3

in a factorial experiment, the effect of one independent variable, averaged over all levels of another independent variable

Question 4

interaction:

Answer 4

when the effect of one independent variable depends on the level of another independent variable

• If the graphical representation of a factorial experiment shows curves that are not parallel, there is an interaction between the variables

Question 5

the common types of interactions in psychological research

Answer 5

• antagonistic, synergistic, and ceiling effect

Question 6

antagonistic interaction:

Answer 6

interaction in which the two independent variables tend to reverse each other’s effects (also known as a crossover interaction)

Question 7

synergistic interaction:

Answer 7

interaction in which the two independent variables reinforce each other’s effects

• the higher level of B enhances the effect of A, and vice versa

Question 8

ceiling-effect interaction:

Answer 8

interaction in which one variable has a smaller effect when paired with higher levels of a second variable

Question 9

mixed factorial designs:

Answer 9

designs containing at least one variable that is presented in a within subjects fashion and one other variable that is presented in a between-subjects fashion

• E.g.: on study.com measuring effects of age and noise on learning
• It has to be mixed since not all the subjects can experience all the factors of age so they are a within subjects on the noise level but between subjects on age