1. Repeated Measures Designs
Different form Independent Groups Design
Independent groups
Compare groups of participants (experimental vs control)
Repeated measures
Do not assign participants to groups – there is only one group
Compare data across conditions (experimental vs control)
Each individual participates in each condition of the experiment.
after participant finishes one condition they then experiences the next condition of the experiment
dependent variable measured for each condition
hence, “repeated measures” also called a “within-subject” design, because the entire experiment is conducted “within” each subject.

2. Examples of Independent and Repeated measures design
Groups
Group 1
Group 2
Participant
score 1
5.14 13
8.96 2
0.62 14
3.99 3
7.35 15
6.07 4
7.67 16
1.96 5
3.86 17
0.17 6
2.90 18
6.31 7
1.88 19
9.86 8
8.94 20
1.49 9
2.03 21
1.08 10
5.94 22
1.14 11
6.48 23
4.74 12
4.62 24
3.31
Repeated
Measures
Cond1
Cond2
Participant
score 1
3.04
9.92 2
2.95
1.44 3
3.08
9.47 4
5.41
2.91 5
1.61
8.97 6
5.30
8.75 7
5.29
5.77 8
5.35
1.11 9
5.87 10
7.89
1.76 11
2.66
1.37 12
2.55
3.01

3. Repeated Measures Designs
Advantages of using a repeated measures design
(1) conduct an experiment when few participants are available
when studying a special population
although commonly used when participants are readily available
(2) conduct the experiment more efficiently
When procedures are short ; show a picture
(3) increase the sensitivity of the experiment
easier to detect difference in independent variable
see next slide
(4) study changes in participants’ behavior over time
learning research with before manipulation, after manipulation
compare across stimuli such as photographs

4. Sensitivity
A “sensitive” experiment is one that can detect the effect of the independent variable, even when that effect is small
because “error variation” is reduced
From individual differences
From variation in procedure
In repeated measures design the same people participate in each condition
variability in responses due to different people is reduced (compared to an independent groups design).

5. Box 7.1 Repeated measurements and the Repeated Measures Design
Repeated measurements is collecting data from the same person more then once
longitudinal survey design when survey researchers administer surveys more than once to the same people
test–retest reliability when researchers investigate consistency to establish the reliability of a measure
Repeated measures design experiment
researchers manipulate an independent variable to compare measures of participants’ behavior in two or more conditions.

6. The Role of Practice Effects in Repeated Measures Designs
FIGURE 7.1 There are both positive and negative effects of practicing a new skill. Repeating the same experience can lead to improvement, but it also can lead to fatigue, a decrease in motivation, and even boredom.

7. The Role of Practice Effects in Repeated Measures Designs
The main disadvantage of repeated measures designs is practice effects.
Practice effects arise because people change as they are repeatedly tested.
they may get better with practice
they may become tired or bored.

8. Practice effects become a confounding variable if not controlled.
Example: Suppose a researcher compares two different study methods, A and B.
Condition A: Participants read a text passage and use a highlighter to indicate the important points. Participants then take a test of this material.
Condition B: Participants read 10 pages of similar text, and make up sample test questions and answers. Participants then take a test of this material.
Participants experience Condition A first then Condition B
Participants have higher test score on B
Can we conclude that condition B is a better study method than condition A?

9. Balancing Practice Effects
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.
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.

10. Balancing Practice Effects
There are two types of Repeated Measures Designs: Complete and Incomplete.
Based on type of counterbalancing
Each repeated measures design uses different procedures for balancing practice effects across the conditions of the experiment.
Complete balances conditions for each participant
Incomplete balances conditions across participants

11. Counter Balancing Repeated measures design
Cond1
Cond2
Participant
score 1
3.04
9.92 2
2.95
1.44 3
3.08
9.47 4
5.41
2.91 5
1.61
8.97 6
5.30
8.75 7
5.29
5.77 8
5.35
1.11 9
5.87 10
7.89
1.76 11
2.66
1.37 12
2.55
3.01
Repeated
Measures
Participant
Cond1
Cond2 1
Con
Exp 2 3 4 5 6 7 8 9 10 11 12

12. V basketball team _______ V cat _______ L chair _______
L beetle ________
V chair ________
V ant ________
L basketball team ________
L cat ________
V spider ________
V monkey ________
L centipede ________
L desk ________
V caterpillar ________
V table ________
L hammock ________
L tripod ________
V butterfly ________
V man ________
L husky ________
L hamster ________
V tarantula ________
V parakeet ________
L sofa ________
L millipede ________
V chimpanzee ________
V milking stool ________
L snail ________ 1A
V beetle _______
L ant _______
L spider _______
V basketball team _______
V cat _______
L chair _______
L table _______
V husky _______
V desk _______
L caterpillar _______
L monkey _______
V hammock _______
V centipede _______
L butterfly _______
L parakeet _______
V sofa _______
V hamster _______
L tarantula _______
L man _______
V tripod _______
V millipede _______
L chimpanzee _______
L milking stool _______
V snail _______

13. 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).

14. 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.

15. First impressions, rate photographs of people on a personality trait such as trustworthiness or likability

16. photos were presented for either 100 ms, 500 ms, and 1000 ms
participants rated 22 faces at each of the three exposure times for a total of 66 face ratings
four walk randomization the 66 trials are separated into 22 blocks of three trials each
within each block the three conditions are in random order

17. 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 practice effects across the conditions of the experiment.
not useful when the conditions are presented only a few times to each participant.
A different method ABBA Counterbalancing is needed when conditions are presented only a few times to each participant.

18. Complete Design (continued)
ABBA Counterbalancing
For each participant present the conditions of the experiment in one random sequence
with two conditions A, B then opposite sequence B, A
with three conditions A, B, C followed by C, B, A
with four conditions A, B, C, D followed by D, C, B, A
If the conditions are presented again, generate another random order of the conditions followed by the opposite sequence.

19. 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 presentation of a 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.

20. Example of linear practice effects:
Suppose participants gain “one unit” of practice with each administration of a condition (there are zero practice effects with the first administration):
Practice effects are balanced because the total practice effects is +5 for each condition:
Mod: Fast: Slow:
Example of nonlinear practice effects:
Suppose a participant figures out a method for completing the task on the 2nd trial and then uses this new method for subsequent administrations:
The total practice effects are not the same for each condition:
Mod: = 6 Fast: = 12 Slow: = 12

21. Complete Design (continued)
nonlinear practice effects
This represents a confounding because differences in scores on the dependent variable may not be caused by the independent variable (conditions A, B, C) but to different amounts of practice effects associated with each condition.
ABBA counterbalancing should not be used when practice effects are likely to vary or change during the course of the experiment (i.e., when practice effects are nonlinear). Use block randomization.

22. Complete Design (continued)
ABBA counterbalancing should not be used when anticipation effects can occur.
Anticipation effects occur when participants develop expectations about which condition will appear next in a sequence.
Participants’ responses may become influenced by their expectations rather than by the conditions of the independent variable.
If anticipation effects are likely, use block randomization.