1
Q
Factorial ANOVA design
A
- Involve the manipulation (experiment) or measurement (quasi-experiment) of 2 or more independent variables
- Investigate the separate effects of each independent variable -> These are called main effects
- Investigate the combined effects of all independent variables -> These are called interactions
- How does the effect of an IV depend on the effect of the other
- There is an F-statistic for every main effect and interaction
2
Q
Factorial design - what are factors
A
Independent variables
One-way ANOVA = 1 IV
1-factor ANOVA = 1 IV
2-factor ANOVA = 2 IV’s
3-factor ANOVA = 3 IV’s
You can include as many factors as you like, but the interpretation becomes more complicated
3
Q
Factorial design - what are levels
A
The number of experimental conditions for each independent variable
IV = age group (children and adolescents) = 2 levels IV = expertise (novice, intermediate, experienced) = 3 levels
4
Q
Factorial design - what are cells
A
The number of individual treatment conditions
Calculated by multiplying the number of levels of each IV
In example above 2 x 3 = 6 cells
5
Q
Factorial design - between subjects
A
- At least 2 independent variables
- All independent variables are manipulated between-subjects
- Each participant provides data in only 1 cell of the analysis
6
Q
Factorial design - within subjects
A
- At least 2 independent variables
- All independent variables are manipulated within-subjects
- Each participant provides data in all cells of the analysis
7
Q
Factorial design - mixed (subjects)
A
- At least 2 independent variables
- At least 1 is manipulated between-subjects and at least 1 is manipulated within-subjects
8
Q
Interacting variables
A
- The effect of one independent variable depends on the other independent variable
- We say that the two variables ‘Interact’
- The more levels of a factor you have means that interactions can become more difficult to interpret
