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Lecture 13Psyc 300A
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Review
• Confounding, extraneous variables • Operational definitions • Random sampling vs random
assignment• Internal validity• Null hypothesis • Type I and type II errors
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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?
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Review: Operational definitions
• For each of the previous studies, operationalize the IV and DV
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Review: Random sampling vs random assignment
• What is the difference between the two?
• Random assignment is a way to prevent confounding
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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)
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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
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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
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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
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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)
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Graphing Relationships: One IV
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1
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10
Romantic Action
Movie type
Ratin
g
0
2
4
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8
10
Romantic Action
Movie TypeRa
ting
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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
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Example: Movie Preferences
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Romantic Action
Movie Type
Ra
ting
men
women
Men Women Mean
Romantic
3 6 4.5
Action 7 4 5.5
Mean 5 5
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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
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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
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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
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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
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Group Activity: Main Effects and Interactions
Make graphs of the following situations:
Var A Var B AxB interaction
p < .05 n.s. p < .05
p < .05 p < .05 p < .05
n.s. p < .05 n.s.
n.s. n.s. p < .05
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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
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A 2 x 3 Interaction
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Relaxation Focus Affirmation
Coping Technique
Rat
ing hi stress
lo stress