# Making Inferences about Causality - PowerPoint PPT Presentation

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1. Watching violent TV Acting violent Making Inferences about Causality • In general, children who watch violent television programs tend to behave more aggressively toward their peers and siblings. • Question: Can we assume a causal relationship between these two variables? +

2. Making Inferences about Causality • Answer: not necessarily • Although causality generally implies correlation, correlation does not necessarily imply causality. • There are at least three other ways to explain the correlation between TV viewing and aggressive behavior.

3. Watching violent TV Acting violent Making Inferences about Causality (a) Acting aggressively makes you want to watch more violent TV. +

4. Watching violent TV Acting violent Making Inferences about Causality (b) Acting violent makes you want to watch more TV and watching TV makes you act more violently. + +

5. Watching violent TV Living in a violent family Acting violent Making Inferences about Causality (c) A “third variable” influences both variables, causing them to be correlated. + +

6. How can we tease apart these various possibilities? • One way to do so is to conduct an experiment • In an experiment, at least one variable is manipulated (i.e., systematically varied) by the researcher in order to study its effects on another variable.

7. Watching violent TV Acting violent Experimental Research • Features of an experiment (a) At least one variable is manipulated or varied by the experimenter: independent variable (IV) (b) The variable presumably affected by the manipulation is called the dependent variable (DV) (c) random assignment to conditions IV DV +

8. Dependent Variable: Aggressive behavior Number of times the child punches his or her peers on the playground IV DV • Independent Variable: Watching violent TV • Levels: • view an episode of the Sopranos • view an episode of the Sopranos in which the violent scenes have been edited

9. Random Assignment • Why is random assignment important? • Consider what would happen if we assigned men to the “violent” level of the IV and women to the “non-violent” level of the IV. • Sex would be correlated with the IV.

10. Random Assignment • Confounding variable: a variable that influences the dependent variable and is associated with the independent variable • When confounding variables are present, we cannot make a strong inference that the independent variable causes the dependent variable.

11. Random Assignment • Random assignment to conditions helps to remove the problem of confounding variables. • When people are randomly assigned to conditions, we should (in the long run) have equal numbers of men and women in our two conditions. • As a result, the possible confound (e.g., sex) is uncorrelated with the independent variable.

12. Random Assignment • Previously, we had discussed the possibility that the violence of the family context is a “third variable” that might be causing both violent TV viewing and aggressive behavior. • We could control for this possible confound by randomly assigning people to violent TV viewing conditions. • Theoretically, there should be an equal number of people from violent families in each condition.

13. Confounding Variables and Non-Confounding Variables • A variable can exist that has a genuine effect on the dependent variable but that is uncorrelated with the independent variable.

14. Watching violent TV Acting violent Confounding Variables and Non-confounding Variables Acting violent + + + + Living in a violent family Living in a violent family Watching violent TV

15. Experimental Research • Between- and within-subjects designs between-subjects: different people are exposed to each level of the IV within-subjects: the same people exposed to each level of the IV

16. Experimental Research • Pros and cons of between and within designs between: different people in each condition. They may differ in certain respects within: same DV assessed many times. Problems with learning effects and awareness of the manipulation counter-balancing: balancing the order of DVs

17. Counter-balancing t1 t2 t3 Person 1 T1 T2 T3 Person 2 T1 T3 T2 Person 3 T2 T1 T3 Person 4 T2 T3 T2 Person 5 T3 T1 T2 Person 6 T3 T2 T1

18. Experimental Research • Multiple independent variables • called factorial designs [# of levels]  [# of levels] IV#1 IV#2 Example: a 2  3 factorial design 2 levels of the first IV, “violent TV viewing” (e.g., watching violent TV and nonviolent TV) and 3 levels of second IV, “provocation” (e.g., 0 threats, 1 threat, 2 threats)

19. Experimental Research • Mixed design: one factor is a between-subjects factor and the other is a within-subjects factor