Unit 9 Flashcards
What are three reasons for using a factorial research design
In factorial research design you want to examine the effect of two or more variables in a single experiment.
One reason is efficiency: as long as you were 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
To reveal interactions among variables
How many cells/combinations are there in a 2 x 3 x 4 factorial design?
24 combinations
In a factorial experiment, the effect of one independent variable, averaged over all levels of another independent variable.
Main effect
A factorial design with two independent variables is able to have two possible main effects
When effective one independent variable depends on the level of another independent variable
Interaction
How can one tell graphically if there is an interaction between two variables?
If the graphical representation of a factorial experiment shows curves that are not parallel, there is an interaction between the variables. If the lines are parallel, there is no interaction
Is it possible to have an interaction if one or the other independent variable has no main effect, or even if neither independent variable has a main effect?
Yes
Interaction in which the two independent variables tend to reverse each other’s effects
Antagonistic interaction
Interaction in which the two independent variables reinforce each other’s effects
Synergistic interaction
Interaction in which one variable has a smaller effect when paired with higher levels of the second variable
Ceiling-effect interaction
What are the three types of interactions in psychological research in factorial design?
Antagonist interaction, synergistic interaction, ceiling-effect interaction
Factorial design that has at least one within-subjects variable and at least one between-subjects variable
A mixed factorial design
Research design that involves all combinations of at least two values of two or more independent variables
Factorial design