1. Research Methods in Psychology
Repeated Measures Designs

2. Repeated Measures Designs
Each individual participates in each condition of the experiment
completes the DV with each condition
hence “repeated measures”
Also called “within-subject” design
entire experiment is conducted “within” each subject

3. Repeated Measures Designs, continued
Why Use a Repeated Measures Design?
no need to balance individual differences across conditions of experiment
all participants are in each condition
fewer participants needed
convenient and efficient
more sensitive

4. A sensitive experiment
Sensitivity
A sensitive experiment
can detect the effect of an independent variable
even if the effect is small
Repeated measures designs are more sensitive than independent groups designs
“error variation” is reduced
same people participate in each condition
variability due to individual differences eliminated

5. Main disadvantage of repeated measures designs is practice effects
People change as they are tested repeatedly
performance may improve over time
people may become bored or tired over time
Practice effects become a potential confounding variable if not controlled

6. Practice Effects, continued
Example:
Suppose a researcher compares two different study methods, A and B
Condition A: participants use a highlighter to mark key points while reading a text, then take a test on the material
Condition B: participants read a text, then make up sample test questions and answers, then take a test on the material

7. Practice Effects, continued
Suppose
all participants first experience Condition A and then Condition B
results indicate test scores are higher in Condition A compared to Condition B
Is marking text with highlighter (A) better than writing sample questions/answers (B)?
impossible to know
confounding of IV with order of presentation
practice effects (boredom, fatigue) may account for poorer performance in Condition B

8. Practice Effects, continued
Practice effects must be balanced, or averaged, across conditions
Counterbalancing the order of conditions distributes practice effects equally across conditions
half of the participants do Condition A, then B
the remaining participants to Condition B, then A
Conditions A and B then have equivalent practice effects
practice effects aren’t eliminated, but they are averaged across the conditions of the experiment

9. Counterbalancing Practice Effects
Two types of repeated measures designs
Complete and Incomplete
purpose of each type of design is to counterbalance practice effects
each design uses different procedures for counterbalancing practice effects

10. Complete Design
Practice effects are balanced within each participant in the complete repeated measures design
each participant experiences each condition several times, using different orders each time
a complete repeated measures design is used when
each condition is brief (e.g., simple judgments about stimuli)‏

11. Complete Design, continued
Two methods for generating orders of conditions
block randomization
ABBA counterbalancing

12. Complete Design, continued
Block randomization
a block consists of all conditions (e.g., 4 conditions: A, B, C, D)‏
generate a random order of the block (ACBD)‏
participant completes condition A, then C, then B, then D
generate a new random order for each time the participant completes the conditions of the experiment (e.g., DACB, CDBA, ADBD)‏

13. Complete Design, continued
Block randomization
balances practice effects only when conditions are presented many times
practice effects are averaged across the many presentations of the conditions
practice effects are not balanced if conditions are presented only a few times to each participant

14. Complete Design, continued
ABBA counterbalancing
used when conditions are presented only a few times to each participant
procedure: present one random sequence of conditions (e.g., DABC), then present the opposite of the sequence (CBAD)‏
each condition has the same amount of practice effects

15. Complete Design, continued
ABBA counterbalancing
balance practice effects that are “linear”
linear practice effects
participants change in the same way following each presentation of a condition
nonlinear practice effects
participants change dramatically following the administration of a condition
example: participant experiences insight about how to complete an experimental task (“aha … now I get it”)‏
likely to use this insight in subsequent conditions

16. Complete Design, continued
Example of linear practice effects
suppose participants gain “one unit” of practice with each administration (“trial”) of a condition
there are zero practice effects with the first administration
Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 6
Condition A B C C B A
Practice Effects
Practice effects are balanced because total practice effects is +5 for each condition:
A: B: C: 2 + 3

17. Complete Design, continued
Example of nonlinear practice effects:
Suppose a participant figures out a method for completing the task on the third trial, and then uses the new method for subsequent trials
Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 6
Condition A B C C B A
Practice Effects
Practice effects are not balanced across the conditions:
A: = 5 B: = 6 C: = 10

18. Complete Design, continued
Nonlinear practice effects create a confounding
differences in scores on the DV may not be caused by the IV (conditions A, B, C)‏
differences on DV may be due 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 over time (i.e., nonlinear practice effects)‏
use block randomization instead

19. Complete Design, continued
ABBA counterbalancing should not be used when anticipation effects can occur
participants develop expectations about which condition will appear next in a sequence
responses may be influenced by expectations rather than actual experience of each condition
if anticipation effects are likely, use block randomization

20. Incomplete Design
Each participant experiences each condition of the experiment exactly once
complete design: more than once
Practice effects are balanced across participants in the incomplete design
complete design: practice effects balanced within each subject

21. Incomplete Design, continued
General rule for balancing practice effects
each condition (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 across conditions
will not confound the experiment

22. Incomplete Design, continued
Two techniques for balancing practice effects in an incomplete repeated measures design
all possible orders
selected orders

23. Incomplete Design, continued
All possible orders
use when there are four or fewer conditions
two conditions (A, B) → two possible orders: AB, BA
half of the participants would be randomly assigned to do condition A first, followed by B
other half of participants would complete condition B first, followed by A
three conditions (A, B, C) → six possible orders:
ABC, ACB, BAC, BCA, CAB, CBA
participants would be randomly assigned to one of the six orders

24. Incomplete Design, continued
four conditions (ABCD) → 24 possible orders (ABCD, ABDC, ACBD, ACDB, ADBC, etc.)‏
five conditions → 120 possible orders
six conditions → 720 possible orders
at least one participant must receive each order of the conditions
therefore, all possible orders is used for experiments with four or fewer conditions of the IV

25. Incomplete Design, continued
Selected orders
select particular orders of conditions to balance practice effects
two methods
Latin Square
random starting order with rotation
each condition appears in each ordinal position exactly once
each participant is randomly assigned to one of the orders of conditions

26. Incomplete Design, continued
Procedure for Latin Square
randomly order the conditions of the experiment (e.g., ABCD)‏
number the conditions (A = 1, B = 2, C = 3, D = 4)‏
use this rule for generating the 1st order:
1, 2, N, 3, N – 1, 4, N – 2, 5, N – 3, 6, etc.
where N = last number of conditions
the first order of four conditions would be

27. Incomplete Design, continued
to generate 2nd order of conditions add 1 to each number in the first order ( )‏
“N” represents the number of conditions (e.g., 4); we can’t use N + 1 because this would create a 5th condition
additional rule: N + 1 always is “1” -- the first condition
the second order of conditions is
to generate 3rd order of conditions add 1 to each number in the second order (again, N + 1 = 1)‏
the third order of conditions is
follow the same procedure for each subsequent order
The number of orders is the same as the number of conditions (e.g., 4 conditions → 4 orders)‏

28. Incomplete Design, continued
Match letters of conditions to their numbers to create the Latin Square
1st 2nd 3rd 4th 1st 2nd 3rd 4th
A B D C
B C A D
C D B A
D A C B

29. Incomplete Design, continued
Each condition appears in each ordinal position equally often, which balances practice effects
For example, condition “A” appears in each ordinal position:
1st 2nd 3rd 4th
A B D C
B C A D
C D B A
D A C B

30. Incomplete Design, continued
Another advantage of Latin Square
each condition precedes and follows every other condition once (e.g., AB and BA, BC and CB)‏
1st 2nd 3rd 4th
A B D C
B C A D
C D B A
D A C B
this helps to control for potential order effects

31. Incomplete Design, continued
Random starting order with rotation
generate a random order of conditions (e.g., ABCD)‏
rotate the sequence by moving each condition one position to the left each time
1st 2nd 3rd 4th
A B C D
B C D A
C D A B
D A B C
each condition appears in each ordinal position to balance practice effects
unlike Latin Square, order of conditions is not balanced

32. Data Analysis of Repeated Measures Designs
Complete repeated measures designs require an additional step
Because participants complete each condition many times, a summary score (e.g., mean) is computed for each participant for each condition
this represents each participant’s average performance in each condition

33. Data Analysis, continued
Suppose
two participants complete two conditions (A, B) of an experiment four times each
the DV is their rating on a 1–5 scale
assume IV (conditions A, B) represents two types of stimuli participants are asked to judge (e.g., size of the stimuli)‏

34. Data Analysis, continued
Suppose the following data are observed in an ABBA design:
Condition Participant 1 Participant 2
A 2 1
B 4 4
B 5 4
A 1 1
B 3 5
A 1 2
A 2 3
B 5 5

35. Data Analysis, continued
To analyze these data we first need to compute the average rating for each condition (A, B) for each participant:
Condition Participant 1 Participant 2
A 2 1
B 4 4
B 5 4
A 1 1
B 3 5
A 1 2
A 2 3
B 5 5
A: ( )/4 = 1.50 A: ( )/4 = 1.75
B: ( )/4 = 4.25 B: ( )/4 = 4.50

36. Data Analysis, continued
Next calculate the mean for each condition across all participants
In this example with two participants, the means for conditions A and B are
Condition A Condition B
participant
participant
mean
Null hypothesis testing or confidence intervals would be used to determine whether this difference between means is reliable

37. The Problem of Differential Transfer
Repeated measures designs should not be used when differential transfer is possible
occurs when the effects of one condition persist and affect participants’ experience of subsequent conditions
use independent groups design instead
assess whether differential transfer is a problem by comparing results for repeated measures design and random groups design

38. Comparison of Two Designs
Differences between repeated measures design and independent groups design
Independent variable
repeated measures: each participant experiences every condition of the IV
independent groups: each participant experiences only one condition of the IV
What is balanced (averaged) across conditions to rule out alternative explanations for findings?
repeated measures: practice effects
independent groups: individual differences variables