rd&a ch 7

Factorial designs

used to manipulate two or more independent variables

Complex factorial design

concerned with more than one factor

Curvilinear relations

U-shape relationship

Main effect

the direct effect of an independent variable on a dependent variable

Simple main effect

in a factorial design, the effect of one independent variable at a particular level of another independent variable

Factorial design format

number of levels of first IV × number of levels of second IV

2×2 design

1 interaction and 2 main effects

2×2×2 design

4 interactions (AxBxC, AxB, BxC, AxC) and 3 main effects

Interaction

situation in which the effect of one independent variable on the dependent variable changes depending on the level of another independent variable

Determining number of interactions

a design with two independent variables has 1 possible interaction

Increasing the number of levels of an IV

allows detection of curvilinear or more complex relationships, provides richer information, and allows comparison of more than two groups

Factorial design (definition)

a design in which all levels of each independent variable are combined with all levels of the others

Simplest factorial design

2×2, with two IVs each having two levels

Interpretation of factorial designs

consider main effects of each independent variable and the interaction between them

IV × PV design

factorial design including both an experimental independent variable (IV) and a participant variable (PV)

2×2 factorial outcomes

may or may not have main effect A, main effect B, or an interaction

Analysis of variance (ANOVA)

used to assess the statistical significance of main effects and interactions in factorial designs

Simple main effect analysis

examines mean differences at each level of an independent variable

Independent groups (between-subjects) design

different participants are assigned to each group

Repeated measures (within-subjects) design

same participants experience all conditions

Mixed factorial design

includes both independent groups and repeated measures variables

Increasing levels or variables

increasing the number of levels or independent variables (e.g., 2×3 or 2×2×2) adds complexity and conditions but can make designs harder to manage

2×2×2 design

8 conditions

2×2×3 design

12 conditions

2×2×2×2 design

16 conditions

Too many variables

can make the design overly complex and require large participant numbers