5/14/2014
Repeated Measures Designs
each individual participates in each condition of the experiment.
This design is also called a “within-subject” design because the entire experiment is conducted “within” each subject.
Why Researchers Use Repeated Measures Designs
don’t have to worry about balancing individual differences across conditions of the experiment
these designs require fewer participants,
they are convenient and efficient, and
repeated measures designs are more sensitive.
Sensitivity
A “sensitive” experiment is one that can detect the effect of the independent variable, even when that effect is small. Repeated measures designs are more sensitive because “error variation” is reduced. Because the same people participate in each condition, the variability in responses due to different people is reduced (compared to independent groups).
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Repeated Measures Designs Practice Effects
The main disadvantage of repeated measures designs is practice effects. Practice effects arise because people change as they are repeatedly tested. As participants complete the dependent variable measures after each condition, they may get better with practice, or they may become tired or bored.
Practice effects
- become a confounding variable if not controlled. Practice effects must be balanced, or averaged across the conditions of the experiment. Counterbalancing the order of the conditions makes sure that the practice effects are distributed equally across the conditions of the experiment.
Practice Effects, continued
Practice effects must be balanced, or averaged across the conditions of the experiment. Counterbalancing the order of the conditions makes sure that the practice effects are distributed equally across the conditions of the experiment.
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Practice Effects, continued
Counterbalancing the study conditions:
half of the participants do Condition A first, then Condition B,
the remaining participants do Condition B first, then Condition A.
In this way, both Conditions A and B have the same amount of practice effects. Practice effects can’t be eliminated but they can be balanced, or averaged, across the conditions of an experiment.
Balancing Practice Effects
There are two types of Repeated Measures Designs: Complete and Incomplete. The purpose of each type of design is to counterbalance practice effects. Each repeated measures design uses different procedures for balancing practice effects across the conditions of the experiment.
Complete Design
Practice effects are balanced within each participant in the complete design. Each participant experiences each condition of the experiment several times (or hundreds of times), using different orders each time. A complete repeated measures design is most often used when each condition is brief (e.g., simple judgments about stimuli).
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Complete Design, continued
Two methods for generating orders of the conditions in the complete design are block randomization and ABBA counterbalancing.
Block randomization
A “block” represents all conditions of the experiment (e.g., 4 conditions, A B C D).
A random order of the block is generated (e.g., ACBD)
Thus, a participant would first do condition A, then C, then B, then D.
A new random order would be generated for each time the participant completes the conditions of the experiment.
Example: 4 stories
Example: 4 stories
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Complete Design, continued
Block randomization balances practice effects only when the conditions are presented many times.
Many administrations of the conditions are needed to balance, or average, practice effects across the conditions of the experiment.
Block randomization is not useful when the conditions are presented only a few times to each participant.
Complete Design, continued
A different method is needed when conditions are presented only a few times to each participant.
ABBA Counterbalancing
Present the conditions of the experiment in one random sequence (e.g., D A B C), followed by the opposite of that sequence (C B A D).
If the conditions are presented again, generate another random order of the conditions, followed by the opposite sequence.
Complete Design, continued
ABBA counterbalancing balances practice effects only when practice effects are “linear.” Linear practice effects occur when participants change in the same way following each condition. Nonlinear practice effects occur when participants change dramatically following the administration of a condition. This can occur when participants experience an insight (“aha”) regarding how to complete the experimental task during the course of the experiment. They are likely to use this new insight in subsequent conditions.
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Incomplete Design
Each participant experiences each condition of the experiment exactly once (rather than many times, as in the complete design). Practice effects are balanced across participants in the incomplete design rather than within subjects, as in the complete design.
Incomplete Design, continued
The general rule for balancing practice effects in the incomplete design is that each condition of the experiment (e.g., A, B, C) must appear in each ordinal position (1st, 2nd, 3rd) equally often. If this rule is followed, practice effects will be balanced and will not confound the experiment.
Incomplete Design, continued
Techniques for balancing practice effects in the incomplete design include all possible orders and selected orders. All possible orders: The preferred technique for balancing practice effects in an incomplete design that has four or fewer conditions of the independent variable.
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Incomplete Design, continued
With two conditions (A, B) there are two possible orders: AB and BA. Half of the participants would be randomly assigned to receive condition A first, followed by B. The remaining participants would complete condition B first, followed by A. With three conditions (A, B, C), there are six possible orders: ABC, ACB, BAC, BCA, CAB, CBA Participants would be randomly assigned to one of the six orders.
Incomplete Design, continued
With four conditions (ABCD) there are 24 possible orders (ABCD, ABDC, ACBD, ACDB, etc.).
With five conditions there are 120 possible orders, and with six conditions there are 720 possible orders.
At least one participant must receive each order of the conditions.
Because of this, all possible orders is usually used for experiments with four or fewer conditions of the independent variable.
Incomplete Design, continued
Selected orders: Rather than using all possible orders, another method for balancing practice effects in the incomplete design is to select particular orders of the conditions. Two methods for selecting orders are the Latin Square method and Random Starting Order with Rotation.
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Latin Squares
2 conditions
Group 1 gets A B
Group 2 gets B A
A condition appears Learning effect tends to precisely once in each row balance out and column in the square
Latin Squares
3 conditions
Group 1 gets A B C
Group 2 gets C A B
Group 3 gets B C A
A condition appears Learning effect tends to precisely once in each row balance out and column in the square
Latin Squares
4 conditions
Group 1 gets A B C D
Group 2 gets D A B C Group 3 gets C D A B
Group 4 gets B C D A
A condition appears Learning effect tends to precisely once in each row balance out and column in the square
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Latin Squares
4 conditions
Group 1 gets A B C D
Group 2 gets D A B C Group 3 gets C D A B
Group 4 gets B C D A
A condition appears Learning effect tends to precisely once in each row balance out and column in the square
Latin Squares
5 conditions
Group 1 gets A B C D E
Group 2 gets E A B C D
Group 3 gets D E A B C
Group 4 gets C D E A B
Group 5 gets B C D E A
A condition appears Learning effect tends to precisely once in each row balance out and column in the square
Latin Squares
3 conditions
Group 1 gets A B C
Group 2 gets C A B
Group 3 gets B C A
However, does not fully eliminate learning effect. B follows A twice
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Balanced Latin Squares
4 conditions A B C D Group 1 gets B D A C Group 2 gets D C B A Group 3 gets C A D B
Each condition appears once in each row and column, as before Each condition before and after each other condition an equal number of times
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