1. Lecture 13 Psyc 300A

2. Review Confounding, extraneous variables Operational definitions Random sampling vs random assignment Internal validity Null hypothesis Type I and type II errors

3. Review: Confounding and extraneous variables Extraneous variables can be confounds, but can also add variability (noise). For each, provide extraneous variable and confound: Study 1: Effect of distraction on pain perception using cold immersion. Study 2: Do girls benefit from sixth grade middle school?

4. Review: Operational definitions For each of the previous studies, operationalize the IV and DV

5. Review: Random sampling vs random assignment What is the difference between the two? Random assignment is a way to prevent confounding

6. Review: Internal validity What is internal validity? Internal validity: Ability to make valid inferences concerning the relationship between the IV and DV in an experiment. (effect on the DV is caused only by the IV)

7. Type I and Type II Errors Accept the Null Hypothesis Reject the Null Hypothesis Null is really True (chance is responsible) Correct Decision Type I Error Null is really False (chance is not responsible) Type II Error Correct Decision

8. Power Power is the probability of avoiding a Type II error. Power is related to: –Alpha level –Effect size (mean and sd) –Number of participants

9. Using More Than Two Levels of an IV What is a level of an IV? In an experiment with an experimental and control group, how many levels? Can we have more than two levels? Example: –Golf club study –Anxiety management techniques for speech-giving Graphing the relationship

10. Advantages of Multi-level Designs Efficiency (fewer participants needed and less time) Ability to see relationships better –Ex: Caffeine and Performance (0, 2, 4 cups of coffee)

11. Graphing Relationships: One IV

12. Multifactor Designs Factorial design: A design in which all levels of each IV are combined with all levels of the other IVs. Advantages of factorial designs: –More efficient (fewer participants and less experimenter time) –Allows us to see how variables interact

13. Example: Movie Preferences MenWomenMean Romantic364.5 Action745.5 Mean55

14. What a Factorial Design Tells You Main effect: The effect of an IV on the DV, ignoring all other factors in the study Interaction effect: When the effect of one IV on a DV differs depending on the level of a second IV. Graphing a factorial design Interpreting the interaction first

15. Examples of Main Effects and Interactions A1= morning A2= late afternoon B1= high fat diet B2= low fat diet DV: 0-50 rating of energy level

16. More Main Effects and Interactions A1= morning A2= late afternoon B1= high fat diet B2= low fat diet DV: 0-50 rating of energy level

17. More Main Effects and Interactions A1= morning A2= late afternoon B1= high fat diet B2= low fat diet DV: 0-50 rating of energy level

18. Group Activity: Main Effects and Interactions Make graphs of the following situations: Var AVar BAxB interaction p <.05 n.s. p <.05 n.s.p <.05 n.s. p <.05

19. Factorial Designs: Naming Conventions The first number is the number of levels in first IV, second number is number of levels in second IV, etc. 2 x 2 2 x 3 2 x 2 x 3 Between-subjects, repeated measures (within), mixed

20. A 2 x 3 Interaction