1. Complex Experimental Designs
Behavioral Research
Complex Experimental Designs

2. Simple designs
Composed of one indep var that is manipulated with two levels and one dep var which is measured.
Example: IV: Stress vs. no stress (control)
Both measured by a test of cognitive function
Hypothesis: The affects of stress impair cognitive function stress as well as cognitive function would have to be operationally defined as to what was used as a stressor (IV) and what measurement did one use to measure cognitive function (DV)

3. Factorial design Designs with more than one indep var or factor .
all levels of each indep var are combined with all levels of the other indep var
The simplest type of factorial design is a 2 X 2—has two indep var, each having two levels.

4. Example: 2 X 2 Indep var 1: difficulty of the task—easy or hard
Indep var 2: attitude of the confederate—helpful or mocking
Dep var: performance on a cognitive task

5. Four Experimental Conditions for 2 X 2 Factorial Design
Easy task – helpful confederate
Easy task – mocking confederate
Hard task – helpful confederate

6. Interpretation of Factorial Designs
Main Effect The impact of each indep var on the dep variable:
InteractionThe effect of any combination of two or more indep vars on the dep var.
The effect that an indep var has on the dep var depends on the level of the other indep var.

7. Example: Factorial design
Examining the after-effects of exposure to an irritating noise on several behavioral measures as a measure of frustration: Two levels of each independent variable
Hypothesis: IV one: Irritating noise: loud vs soft
IV two: predictable vs. non predicable
DV: Number of attempts at difficult puzzle during different noise levels

8. Noise intensity vs. Predictability
Loud
Soft
Predictable
Group 1
Group 2
Unpredictable
Group 3
Group 4

9. Noise intensity vs. Predictability
Loud
Soft
Predictable 7 8
Unpredictable 3 5

10. Calculating Main Effects: Comparing Row and Column Means
Column Means: Loud= 5
Soft = 6.5
Row Means: Predictable = 7.5
Unpredictable = 4

11. Interpretation of Main Effects
A reliable difference in the column means would indicate an effect of noise intensity, independent of noise predictability
A reliable difference in the row means would indicate an effect of noise predictability, independent of noise intensity

12. Interactions
Number of attempts to solve the difficult puzzle was greater when the noise was soft than when it was loud.
However, this relationship was dependent on whether the noise was unpredictable

14. Factorial Designs With Manipulated and Nonmanipulated Variables: IV X PV Designs
allow researchers to investigate how different types of individuals respond to the same manipulated variable
E.g., of Participant variables – gender, age, ethnic group, personality characteristics
The simplest IV X PV design includes one manipulated independent variable with at least two levels and one Participant variable with at least two levels
E.g., Participant variable – two different age groups; or males vs. females

15. IV X PV design, Furnham, Gunter, Peterson (1994)
Showed that the ability to study with a distracting task in the room is affected by whether you are more extraverted or introverted
Manipulated var—distraction
Subject var—extroversion or introversion
Measured var—reading comprehension
A repeated measures design was used  college students read material in silence and within hearing range of a TV program

16. Results
Overall, students had higher comprehension scores when they studied in silence
Interaction between extraversion and distraction
Without a distraction, the performance of extraverts and introverts was the same
However, extraverts performed better than introverts when the TV was on.

17. Further Considerations in Factorial Designs
If you were to have a 2 x 2 x 2 factorial design, you could look at it as two 2 x 2 designs.
E.g., 2 (instruction method: lecture or discussion) x 2 (class size: 10 or 40) x 2 (gender)
Divide 2 x 2s by gender—2x2 for males and 2x2 for females
Could then look at the main effects and interactions within each of these 2 x 2s )(three main effects)
gender
lecture vs. discussion
class size (small=10; large= 40)

18. Interactions in a 2 X 2 X 2
Could also look at the interaction in the 2 x 2 x 2 design—have the possibility of 3 simple interactions
Instruction method and class size
Instruction method and gender
Class size and gender
Could also have a three-way interaction, where the effect of the interaction b/t two of the variables differs depending on the particular level of the third variable
Three-way interactions are complicated and hard to interpret

19. F-Statistic Used in Factorial Designs Is an extension of the t-test.
It is an analysis of variance that is a more general procedure than the t-test.
When a study has only one independent variable and only two groups using an F or a t makes no difference.
However analysis of variance (ANOVA) is conducted when there are more than two levels of an independent variable and when a factorial design with two or more independent variables is used.

20. F test is the ratio of two types of variance:
Therefore, the f-test is appropriate for the simplest experimental design as well as more complex.
T-test demonstrates the relationship between two groups and the within group variability
F test is the ratio of two types of variance:
Sytematic variance: deviation of the group means from the grand man which is the mean score of all individuals in all groups. (Grand mean-5.75: Loud=5, Soft = 6.5)
Is small when the differences between group means is small and increases as the group mean differences increase
Error variance: the deviation of the individual scores in each group from their respective group mean.

21. F-Significance Ratio of Systematic variance over Error Variance.
Therefore you want systematic variance (difference between groups as shown by comparing grand mean to group means) to be high.
Error variance to be low (comparison of individual scores against the group mean)
Low error variance indicates homogeneity within your groups which will increase your F statistic and be more likely to show significant results.