1. Introduction to Complex Designs
Complex Design has two or more independent variables in the same experiment
The simplest complex design; 2X2
two independent variables (IVs)
And each independent variable has two levels (conditions)
So there are four conditions in a 2X2 design
Factorial Combination
combine independent variables in an experiment to describe their effects on the dependent variable(s).
How many independent variables and conditions in a 2X2X2 design?

2. Kassin (2003) Research Example
Kassin, Goldstein, and Savitsky (2003)
pp in SZ&Z textbook
Research questions
Do interrogators’ expectations about a suspect’s guilt or innocence influence the interrogation tactics they use?
Do interrogators have a confirmation bias in which their initial beliefs about a suspect’s guilt cause them to interrogate more aggressively?

3. Kassin (2003) Research Example
Research design
2 x 2 complex design
IV-1: Interrogator Expectation (independent groups)
Guilty expectation
Innocent expectation
IV-2: Suspect Status (independent groups)
Actual guilt
Actual innocence
Students participated as interrogators or suspects in a laboratory “mock crime.”

4. Kassin (2003) Research Example
Dependent Variables (there were many)
Number of guilt-presumptive questions the interrogator selects for the interview with suspect
Number of persuasive interrogation techniques used during the interview with the suspect
Ratings of the amount of effort the interrogator used to obtain a confession

5. Complex Designs Main effect is the effect of an independent variable
effect of the independent variable on the dependent variable
as if only that variable was manipulated in the experiment.
Interaction effect is the combined effect of independent variables
effect of one of the independent variables differs depending on the level of the second independent variable.

6. Complex Designs Kassin (2003) Example
This factorial design is called a 2 x 2 design
It has four conditions
Actual Guilt/ Guilty Expectation
Actual Guilt/ Innocent Expectation
Actual Innocence/ Guilty Expectation
Actual Innocence/ Innocent Expectation
In one experiment can study
Main effect of Interrogator Expectation
Main effect of Suspect Status
Interaction of Interrogator Expectation and Suspect Status

7. Kassin (2003) Research Example
Factorial combination of 2 x 2 design
Interrogator Expectation
Guilty
Innocent
Suspect
Status
Actual Guilt
Interrogators believed suspect was guilty and suspect actually committed the crime
Interrogators believed suspect was innocent and suspect actually committed the crime
Actual Innocence
Interrogators believed suspect was guilty and suspect did not commit the crime
Interrogators believed suspect was innocent and suspect did not commit the crime

8. Kassin (2003) Research Example
Kassin et al.’s (2003) findings “results”
Main effects
A main effect is the effect of one IV, ignoring (or collapsing across) the effect of the other IV
Two main effects are possible
Interrogator Expectation
Suspect Status

9. Kassin (2003) Findings MAIN EFFECTS
main effect of the Interrogator Expectation
compare the two levels of Interrogator Expectation:
Guilty
Innocent
main effect of the Suspect Status
compare the two levels of Suspect Status:
Actual guilt
Actual Innocence

10. Main Effects (see table 8.1)
Number of Guilt-Presumptive Questions
Interrogator Expectation
Guilty (n = 51)
Innocent (n = 47)
Suspect Status
Actual Guilt
(n = 50)
3.54
(n =26)
2.54
(n =24)
Actual Innocence
(n =48)
3.70
(n =25)
2.66
(n =23)
Means for Suspect Status
3.04
3.18
Means for Interrogator Expectation:

11. Main Effects for Number of Guilt-Presumptive Questions
Interrogator Expectation
Guilt (3.62) compared to Innocent (2.60)
This main effect of Interrogator Expectation maybe statistically significant
Suspect Status
Actual Guilt (3.04) compared to Actual Innocence (3.18)
This main effect of Suspect Status probably not statistically significant
Even though Suspect Status did not produce a main effect it may have an influence
need to look at interaction effects before drawing conclusions
Did Suspect Status influence ratings by interacting with Interrogator Expectation ?

12. Main Effects (see table 8.2)
Number of Persuasive Techniques
Interrogator Expectation
Guilty
(n = 51)
Innocent
(n = 47)
Means for Suspect Status
Suspect Status
Actual Guilt
(n = 50)
7.71
(n =26)
6.59
(n =24)
7.15
Actual Innocence
(n =48)
11.96
(n =25)
10.88
(n =23)
11.42
Means for Interrogator Expectation:

13. Main Effects for Number of Persuasive Techniques
Interrogator Expectation
Guilt (9.83) compared to Innocent (8.73)
This main effect of Interrogator Expectation is probably not statistically significant
Suspect Status
Actual Guilt (7.15) compared to Actual Innocence (11.42)
This main effect of Suspect Status maybe statistically significant
Even though Interrogator Expectation did not produce a main effect it may have an influence
need to look at interaction effects before drawing conclusions
Did Interrogator Expectation influence ratings by interacting with Suspect Status?

14. Interaction Effects
Independent variables work together to influence behavior.
When the effect of one independent variable differs depending on the level of the second independent variable.
To look for an interaction effect look at the effect of Suspect Status at each level of the Interrogator Expectation independent variable.
When we look for interaction effects between independent variables, we often use the subtraction method.

15. Interrogator Expectation Difference Between Means
Interaction Effects (see table 8.3) Caution the marginal numbers in this table are mean difference because we are looking for interaction effects.
Effort to Obtain a Confession
Interrogator Expectation
Guilty (n = 51)
Innocent (n = 47)
Difference Between Means
Suspect Status
Actual Guilt
(n = 50)
5.64
(n =26)
5.56
(n =24)
0.08
Actual Innocence
(n =48)
7.17
(n =25)
5.85
(n =23)
1.32
Difference Between Means

16. Interaction Effects
The subtraction method can be used to examine the effect of the Interrogator Expectation independent variable at each level of the Suspect Status independent variable:
For Actual Guilt, the difference between means for the two scenario conditions was 0.08
For Actual Innocence, the difference between means for the two scenario conditions was 1.32
Because the outcome of the subtraction method yielded very different values (0.08 and 1.32), an interaction effect between the independent variables is likely:
but a test of statistical significance would be needed to confirm this.

17. Interaction Effects
Another way to say this is that the effect of one independent variable, Suspect Status, differed depending on the level of the second independent variable, Interrogator Expectation.
Recall that this is our definition of an interaction effect.

18. Interaction Effects (continued)
Graphs (“Figures”) can be used to detect interaction effects easily.
An interaction effect is likely present in a complex design experiment when the lines in a graph that display the means:
are not parallel;
that is, the lines either intersect, converge, or diverge.

20. Stretching Exercise II

21. Stretching Exercise II

22. Complex Design with Three Independent Variables
Pingitore (1994) study on discrimination in job interviews because of bodyweight
IV-1: Weight of the applicant as seen in a video tape (note: applicants are actors)
Normal
Overweight
IV-2: Sex of the applicant as seen in a video tape (note: applicants are actors)
Female
Male
IV-3: Participant’s self report of concern for their own body weight
Low Body-Schema
High Body-Schema

23. Complex Design with Three Independent Variables
Three Main Effects
Main effect of Weight of the applicant
Main effect of Sex of the applicant
Main effect of Body-Schema
Interactions
2Way: Weight of the applicant with Sex of the applicant
2Way: Weight of the applicant with Body-Schema
2Way: Sex of the applicant with Body-Schema
3Way: Weight of the applicant with Sex of the applicant with Body-Schema