Behavioral Research Chapter 10

1. Behavioral ResearchChapter 10 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 • Hard task – mocking confederate

6. Interpretation of Factorial Designs • Main EffectThe impact of each IV on the DV. -The number of main effects depends on the number of Independent Variables. • InteractionThe effect of any combination of two or more IV. on the D.V.. • The effect that an independent variable has on the dependent variable depends on the level of the other independent variable.

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: Predictability: predictable vs. non predicable DV: Number of attempts at difficult puzzle during different noise levels

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. 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.