1. Complex Experimental Designs
Chapter 10

2. Increasing the # of levels of an IV
Simple design = 2 levels
Doesn’t provide a lot of information
Level 1: reward
Level 2: no reward
Use a ratio scale
Linear
Monotonic
Curvilinear
Increases in one variable are accompanied by decreases and increases in the other variable
Inverted-U

3. Increasing the # of IVs: factorial designs
Many IVs are closer to real world conditions
Factorial designs – more than 1 IV
2 x 2 factorial
2 independent variables w/ 2 levels each
2 x 2 = 4 experimental conditions
Main effects
Effect the variable has by itself
Averages across the levels of the other IV
Interactions
The effects of one IV is different at different levels of the other IV

4. Outcomes of a 2 x 2 factorial design
There may or may not be a significant main effect for variable A
There may or may not be a significant main effect for variable B
There may or may not be an interaction between both variables
When there is an interaction, next look at simple main effects (differences at each level of the IV)
This allows analysis of results as though there were separate experiments at each level of the other IV

5. Mixed factorial design
Combining independent groups AND repeated measures
Uses combined assignment of participants
Investigates the combined effects of situational factors and subject variables
Common manipulation is looking at gender differences
Below is a 2x3 mixed factorial
6 total conditions
Good feedback
Bad feedback
No feedback
Overall Means (main effect)
Male 80 75 79 78
Female
100 95 90
Overall Means (main effect 87

6. Increasing the # of IVs in a factorial design
Conducting a 2x2x2 design
Can be seen as two 2x2 designs,
i.e. one for males and one for females
Yields main effects for 3 IVs
Also allows us to look at interactions