1. EXPERIMENTAL PSYCHOLOGY
Alla Chavarga
Tuesdays 9:00-11:20am 2412J
or 6:10-8:30pm 432-IA
LABS:
MW 6:10-9:45pm 3106J Kravitz  MW 6:10-9:45pm 4109J Ergun  TR 11:30-3:05pm 3106J Fein  TR 11:30-3:05pm 4109J Hazan

2. Factorial Designs CHAPTER 5
Lecture Outline
What is a factorial design? How do we identify and describe a factorial design?
Main Effects
Interactions
How to report the findings of a factorial analysis

3. AN EXAMPLE OF AN EXPERIMENT
An education researcher believes that watching YouTube videos can be helpful to students taking college-level mathematics courses. He plans an experiment in which he assigns students randomly to two classes: Math 101 taught in the traditional fashion, and Math 101 with YouTube video assignments. He teaches the courses for the duration of one semester in their respective styles, and gives the same final exam at the end. He then compares the average final exam score between the two classes.
What is the independent variable?
What is the dependent variable?
The independent variable is class and there are 2 levels:
Traditional math class
YouTube-based math class

4. AN EXAMPLE OF AN EXPERIMENT
An education researcher believes that watching YouTube videos and study style affects student performance in a mathematics course. He plans an experiment in which he assigns students randomly to two classes: Math 101 taught in the traditional fashion, and Math 101 with YouTube video assignments. He asks half the “traditional class” students to study on a daily basis while the other half is asked to “cram” for the test. He does the same for the “Youtube class”, then compares the students’ scores on their common final exam.
What is the independent variable?
What is the dependent variable?
There are two independent variables:
Class type (2 levels): traditional, Youtube-based
Study style (2 levels): daily, cram

5. WHAT IS A FACTORIAL DESIGN?
A factorial design is an experiment containing 2 or more factors, and in which experimental groups consist of all possible combinations of factor levels.
For example:
I believe that type of lighting in a room and the color of the test paper affect scores. I will use 2 types of lighting (dim, bright) and three paper colors (red, blue, white)
BRIGHT
DIM
LIGHTING
2 levels of lighting (bright, dim)
3 levels of paper color (red, blue, white)
RED
BLUE
WHITE
PAPER COLOR
2 x 3 = 6 total conditions:
Bright room, red paper
Bright room, blue paper
Bright room, white paper
Dim room, red paper
Dim room, blue paper
Dim room, white paper

6. WHAT IS A FACTORIAL DESIGN?
Notation system: Typically we report our design using several digits with an x (read: “by”) in between:
Digits represent levels: 3 levels of color x 2 levels of lighting
If I had three IVs (factors): lets add sex: 3 x 2 x 2 = 3 levels of color, 2 levels of lighting, 2 levels of sex
BRIGHT
DIM
RED
BLUE
WHITE
LIGHTING
PAPER COLOR M F

7. WHAT IS A FACTORIAL DESIGN?
Examples of factorial designs:
Bystander effect: The number of people who help an injured confederate is affected by size of crowd (small, large) and sex (male, female).
2 x 2 Factorial Design
Small Large
Male
Female
Talk therapy (psychoanalysis, CBT, DBT) and medication (placebo, antidepressant) affect depression self-report score.
2 x 3 Factorial Design
P-A CBT DBT
Placebo
Drug
Type of music (classical, rock, hip-hop, none) and caffeine consumed (0mg, 10mg, 20mg) affect reaction time in a computer task.
3 x 4 Factorial Design
C R H N
0mg
10mg
20mg

8. Loftus and Palmer (1974)

9. 45 75 TESTING A FACTORIAL DESIGN: MAIN EFFECTS
Let’s start with a NON-FACTORIAL EXAMPLE:
Loftus & Palmer (1974) showed that the wording of a question intended to elicit details from eyewitness testimony had a significant impact on the details reported. In other words, the kinds of questions you are asked may affect your memory of an event after it has occurred.
Independent variable: wording of the question (made contact, smashed)
Dependent variable: estimated MPH
“made contact” “smashed” 45 75

10. 45 45 TESTING A FACTORIAL DESIGN: MAIN EFFECTS
Let’s start with a NON-FACTORIAL EXAMPLE:
What if you had obtained this result? What would you conclude?
Independent variable: wording of the question (made contact, smashed)
Dependent variable: estimated MPH
“made contact” “smashed” 45 45

11. 80 80 20 20 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 80 20 50 50
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 80 80 80 20
non-driver
driver 20 20
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There is a main effect of driver status.

12. 60 30 60 30 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 45 60 30
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 60 30 45
non-driver
driver 60 30
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There is a main effect of wording.

13. 30 50 30 50 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 40 30 50
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 30 50 40
non-driver
driver 30 50
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There is a main effect of wording.

14. 70 70 10 10 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 75 15 40 40
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 70 70 75 15
non-driver
driver 10 10
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There is a main effect of driver status.

15. 70 90 20 40 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 80 30 45 65
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 70 90 80 30
non-driver
driver 20 40
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There is a main effect of driver status
AND a main effect of wording.

16. 70 70 70 70 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 70 70 70
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 70 70 70
non-driver
driver 70 70
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There are NO main effects.

17. 80 20 20 80 TESTING A FACTORIAL DESIGN: MAIN EFFECTS 50 50 50
Now, a FACTORIAL DESIGN EXAMPLE:
Independent variable:
wording (made contact, smashed)
Driver status (driver, non-driver)
Dependent variable: estimated MPH
CONTACT SMASHED 80 20 50
non-driver
driver 20 80
Is there a MAIN EFFECT of wording?
Is there a MAIN EFFECT of driver status?
There are NO main effects.
But it seems like SOMETHING is going on.
There is an INTERACTION EFFECT.

18. 80 20 20 80 TESTING A FACTORIAL DESIGN: INTERACTIONS 50 50 50
Interaction effect: When the effect of one independent variable DEPENDS on the level of the other.
CONTACT SMASHED
The effect of wording DEPENDS on driver status. If you are a non-driver, the word “contact” elicits a greater estimate, but if you are a driver, the word “smashed” elicits a greater estimate.
Note: There are no main effects in this example! 80 20 50
non-driver
driver 20 80

19. 80 20 20 80 TESTING A FACTORIAL DESIGN 50 50 50 CONTACT SMASHED
non-driver
driver 20 80
There are no MAIN EFFECTS.
There is an INTERACTION EFFECT.

20. 80 30 10 60 TESTING A FACTORIAL DESIGN 55 35 45 45 CONTACT SMASHED
non-driver
driver 10 60
There is a MAIN EFFECT of driver status.
There is an INTERACTION EFFECT.

21. 80 40 60 100 TESTING A FACTORIAL DESIGN 90 50 60 80 CONTACT SMASHED
non-driver
driver 40 60
There is a MAIN EFFECT of driver status AND wording.
There is no INTERACTION EFFECT.

22. 30 70 30 70 TESTING A FACTORIAL DESIGN 55 30 70 CONTACT SMASHED
non-driver
driver 30 70
There is a MAIN EFFECT of wording.
There is no INTERACTION EFFECT.

23. 50 70 50 20 TESTING A FACTORIAL DESIGN 60 35 50 45 CONTACT SMASHED
non-driver
driver 50 20
There is a MAIN EFFECT of driver status and wording.
There is an INTERACTION EFFECT.

24. 50 50 80 20 TESTING A FACTORIAL DESIGN 50 65 35 CONTACT SMASHED
non-driver
driver 80 20
There is a MAIN EFFECT of wording.
There is an INTERACTION EFFECT.

25. 50 50 50 50 TESTING A FACTORIAL DESIGN 50 50 50 CONTACT SMASHED
non-driver
driver 50 50
There are no MAIN EFFECTS.
There is no INTERACTION EFFECT.

26. 100 100 250 100 TESTING A FACTORIAL DESIGN: THE STROOP EFFECT 100 175
TYPE
COLOR
TYPE
WORD
100
100
100
175
CONGRUENT
INCONGRUENT
250
100

27. TESTING A FACTORIAL DESIGN: THE STROOP EFFECT
TYPE
COLOR
TYPE
WORD
Shortcut to determine if there is an interaction:
100
100
Subtract!
CONGRUENT
INCONGRUENT
250
100 __ __
______
- 150
_____
If these two numbers are the same, there is no interaction. Otherwise, there is!

28. DEFINITIONS: Main effects and interactions
Overall effect of individual factors (IVs)
Interaction Effect
When the effect of one factor (IV) depends on the level of the other factor (IV)
For every TWO-way ANOVA you conduct, you will always report TWO main effects and ONE interaction
Main effect of IV1
Main effect of IV2
Interaction Effect
The more factors (IVs) you have, the more effects you will report.
Once you have your results, you share them both in writing and in visual form (tables, figures)

29. REPORTING YOUR RESULTSA B
You will present your results in Figure form, either as a line graph (A) or as a bar chart (B).
You will also need to report your results IN TEXT (results section). This includes the report of the statistical test (ANOVA) as well as the means and standard deviations (YOU will have to calculate these).
You must indicate which effects were significant and which were not.
Must include statement of statistical test in proper reporting format:
test letter (df)=Value, p decision
Must include a statement (in words) explaining the effect.

30. REPORTING YOUR RESULTS
There was a significant main effect of task type (F(1)= 2.56, p<.05. Overall, RT was greater for the “type the color” task (M=175ms, SD=50ms) than “type the word” task (M=100ms, SD=50ms).
There was a significant main effect of congruency(F(1)= 3.2, p<.05. Overall, RT was greater for the incongruent condition (M=175ms, SD=50ms) than the congruent condition (M=100ms, SD=50ms).
There was a significant interaction effect of congruency(F(16)=4.2, p<.05. The effect of congruency depends on the level of task type: when performing the “type the color” task, incongruent RT (M=250ms, SD=50ms) was greater than congruent RT (M=100ms, SD=50ms). However, in the “type the word” task, there was no difference between congruent RT (M=100ms, SD=50ms) and incongruent RT (M=100ms, SD=50ms).

31. REPORTING YOUR RESULTS
There was no significant main effect of task type (F(1)= 2.56, p<.NS).
There was a significant main effect of congruency(F(1)= 3.2, p<.05. Overall, RT was greater for the incongruent condition (M=175ms, SD=50ms) than the congruent condition (M=100ms, SD=50ms).
There was a significant interaction effect of congruency(F(16)=4.2, p<.05. The effect of congruency depends on the level of task type: when performing the “type the color” task, incongruent RT (M=250ms, SD=50ms) was greater than congruent RT (M=100ms, SD=50ms). However, in the “type the word” task, there was no difference between congruent RT (M=100ms, SD=50ms) and incongruent RT (M=100ms, SD=50ms).