1. Chapter 10: Complex Experimental Designs

2. Increasing the Number of Levels of An Independent Variable
Simplest experimental design
Two levels of one independent variable
Compares only two groups
Provides limited information about the form of the relationship between the IV and DV
- Detecting the linearity of the relationship

3. Increasing the Number of Levels of An Independent Variable (con’t)
Is the relationship between the IV and DV
linear or curvilinear?

4. Increasing the Number of Levels of An Independent Variable (con’t)
Was it possible to detect this relationship if only two levels of the IV were used in this experiment?

5. Increasing the Number of Independent Variables: Factorial Designs
Manipulate more than one IV
Typically, two or three IV’s are operating simultaneously
- Closer approximation of real-world conditions

6. Increasing the Number of Independent Variables: Factorial Designs (con’t)
More than one IV (or factor)
All levels of each IV are combined with all levels of the other IV’s
Simplest factorial is a 2 X 2 factorial design

7. Increasing the Number of Independent Variables: Factorial Designs (con’t)
2 X 2 Factorial Design
Factor B:
Knowledge of the Crime
Level 1 = Naïve questioner
Level 2 = Knowledgeable questioner
Factor A:
Type of Question
Level 1 = Misleading
Level 2 = Unbiased

8. 2 X 2 Design = 4 Experimental Conditions (4 groups)
Increasing the Number of Independent Variables: Factorial Designs (con’t)
2 X 2 Design = 4 Experimental Conditions (4 groups)

9. Increasing the Number of Independent Variables: Factorial Designs (con’t)
3 X 4 Factorial Design
2 X 3 Factorial Design
2 X 2 X 2 Factorial Design
Identify the number of experimental conditions
in each of these designs.

10. Increasing the Number of Independent Variables: Factorial Designs (con’t)
Interpretation of Factorial Designs
Two kinds of information
Main effect of an independent variable
2. Interaction between the independent variables

11. Increasing the Number of Independent Variables: Factorial Designs (con’t)
Factorial designs with manipulated and nonmanipulated variables (sometimes called IV x PV designs
Independent variable (IV) x participant variable (PV)
Allows researchers to examine how different individuals respond to the same manipulated IV

12. Increasing the Number of Independent Variables: Factorial Designs (con’t)
A main effect tells us the effect each variable has by itself.
An interaction tells us that the effect of one independent variable depends on the particular level of the other.
Outcomes of a 2 X 2 factorial design
Interactions and simple main effects

13. Increasing the Number of Independent Variables: Factorial Designs (con’t)
Interactions and moderator variables
A moderator variable influences the relationship between two other variables

14. Increasing the Number of Independent Variables: Factorial Designs (con’t)

15. Increasing the Number of Independent Variables: Factorial Designs (con’t)

16. Increasing the Number of Independent Variables: Factorial Designs (con’t)

17. Increasing the Number of Independent Variables: Factorial Designs (con’t)

18. Increasing the Number of Independent Variables: Factorial Designs (con’t)

19. Increasing the Number of Independent Variables: Factorial Designs (con’t)

20. Increasing the Number of Independent Variables: Factorial Designs (con’t)

21. Increasing the Number of Independent Variables: Factorial Designs (con’t)

22. Increasing the Number of Independent Variables: Factorial Designs (con’t)

23. Increasing the Number of Independent Variables: Factorial Designs (con’t)
Assignment procedures and factorial designs
Two basic ways of assigning participants to conditions
1. Independent groups design
2. Repeated measures design
Combination of the two basic ways is called a mixed factorial design

24. Increasing the Number of Independent Variables: Factorial Designs (con’t)

25. Increasing the Number of Independent Variables: Factorial Designs (con’t)

26. Increasing the Number of Independent Variables: Factorial Designs (con’t)

27. The End