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Lecture 13 Psyc 300A

Lecture 13 Psyc 300A. Review. Confounding, extraneous variables Operational definitions Random sampling vs random assignment Internal validity Null hypothesis Type I and type II errors . Review: Confounding and extraneous variables.

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Lecture 13 Psyc 300A

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  1. Lecture 13Psyc 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

  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

  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:

  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

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