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Threats to Internal Validity
Example of One-Shot Case Study
Participants Brush with Crest Ask preference
Can’t tell if there was any effect of toothpaste type
X O
Concepts Basic Experiment
An IV with at least 2 levels Experimental Control Random Assignment Strengthens the Internal Validity - We can
tell if the IV caused a change in the DV!
Concepts Confound
When an uncontrolled variable is present in your experiment
You cannot identify whether the IV or the uncontrolled variable is causing the change in the DV
Weakens internal validity
Improving Internal Validity What can we do??? Ensure all aspects of the experiment
are equal except for the IV manipulation Add a good equivalent control group
(before the manipulation!) Any differences between groups can be
attributed to your manipulation
Improving Internal Validity Basic Control group design
Why does the control group have to be equivalent?
X OO
Threats to Internal Validity Nonequivalent Control group design
Selection differences - When participants who form the groups come from existing natural groups; a confound!
X OO
Overweight Volunteers
Traditional Dieters
Well Designed Experiments Posttest Only Design
X OO
Participantsrandom
random
Benefits: Ensures control and experimental groups are equal Limitation: Can’t demonstrate equality for sure; differences in mortality rate
Well Designed Experiments Pretest-Posttest Design
O X OO O
Participantsrandom
random
Benefits: You can see if mortality rate was due to any preexisting condition
Limitations: You might sensitize participants to your hypothesis
Design Variations
1. Independent Groups design aka Between Groups design 2 (or more) different groups determined
by Simple random assignment Matched Pairs random assignment
Used when you need to ENSURE equality on some measure
Matched Pairs Assignment Measure groups of
control variable of interest (e.g., IQ)
Arrange highest to lowest
Randomly assign 1st pair to each group; repeat for each pair
110109107107104103103101
9898
G1 G2
110107104103
98
109107103101
98
WholeSample
Means 104.4 103.6
Design Variations
2. Repeated Measures design aka Within Groups design Each person acts as their own control, so
fewer subjects needed Very sensitive to small differences since
both groups are identical on everything Problems???
Repeated Measures Design Order Effects
When the order in which the levels of the IV are presented affect the DV (threatens internal validity)
Practice Fatigue Contrast
Repeated Measures Design Overcome by
Increasing time interval between conditions counterbalancing
Randomly divide the sample into groups and administer the levels of the IV in reverse order
analyze all groups together
Repeated Measures Design Counterbalancing
1st 2nd
Sample
Group A
Group B
Alcohol Sober
Sober Alcohol
Repeated Measures Design Counterbalancing Problems:
The number of possible conditions dramatically increases the number of orders
2 conditions = 2 orders (2 x 1) 3 conditions = 6 orders (3 x 2 x 1) 5 conditions = 120 orders! At 30 Ss per condition, you need a LOT of
subjects
Repeated Measures Design Overcoming Counterbalancing
Problems: Latin Square Design
Special procedure for ensuring that each condition occurs at every position (1st, 2nd, etc.) and that each condition occurs before and after every other condition at least once.
Between Groups vs Repeated Measures Repeated measures advantages
Requires fewer participants Reduces differences between groups -
better able to detect small differences Between Groups advantage
No order effects
Between Groups vs Repeated Measures Also consider
Generalization - sometimes we experience in the real world variables alone, but sometimes together - choose the design that mirrors the outside world
Conditions with permanent changes don’t lend themselves to repeated measures - the sample is “spoiled” in the first condition