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FACTORIAL DESIGN
Has at least two factors (IV's), each with at least two levels
Then the two factors can be examined simultaneously
*Factor = IV in the context of ANOVA.
MAIN EFFECTS
Main effect of Factor A
Is there an effect of factor A on the DV?
On average, is there a difference between the levels of factor A on the DV?
Main effect of Factor B
Is there an effect of factor B on the DV?
On average, is there a difference between the levels of factor B on the DV?
Factor A x Factor B Interaction
Does the effect of factor A on the DV change at each level of factor B?
SIMPLE EFFECT
The effect of one factor at one level of the other factor
MARGINAL MEAN
The mean of factor A or B's levels
CELL MEAN
The mean of factor A at each level of factor B
Vice versa
ADVANTAGES OF FACTORIAL ANOVA
Allows us to examine both the individual effect of each factor and interactions between factors
Interactions indicate whether the effect of one factor on a DV is 'moderated/qualified' by another factor
Interactions tell us whether the effect of one factor changes at different levels of another factor
NOTATION
TYPE OF DESIGN
between subjects
within subjects
mixed model
NUMBER OF FACTORS
two-way / three-way, etc.
NUMBER OF FACTOR LEVELS
2×2 / 3×3 / 4×4
COMBINED
A 2×2 between-subjects factorial design.
PLOTTING GRAPHS
Y-axis for DV
X-axis for the factor with the most levels or the factor which is most theoretically important
Other factor is represented by separate lines on the graph
All cell means in the design must be represented on the graph
LINES AND INTERACTIONS
Parallel lines indicate that there is no interaction (rough guide)
Non-parallel lines indicate an interaction
Disordinal interaction (lines cross) = effect reverses
Ordinal interaction (lines do not cross) = effect changes in size, not direction
READING MAIN EFFECTS FROM GRAPH
Visible as the differences in the average height of points for each factor.