1. Correlated-Groups and Single-Subject Designs
Graziano and Raulin
Research Methods: Chapter 11
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Graziano & Raulin (1997)

2. Correlated-Groups Designs
Introduces a correlation between groups in the way groups are formed
Within-subjects design:
Same participants in each group
Matched-groups design
Uses matched random assignment
More sensitive than independent-groups designs
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3. Within-Subjects Designs
All participants are exposed to all experimental conditions
Need to control for sequence effects
The experience with one condition affecting performance in subsequent conditions
Controlled by varying the order of presentation (counterbalancing)
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4. Target Search Study Hypothetical Study Within-subjects design
Six participants; order counterbalanced
Three conditions
10 distracters
15 distracters
20 distracters
Design on next slide
Data on the following slide
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5. Within-Subjects Design Example
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6. Target Search Study Conditions A (10) B (15) C (20) Participant Order
18.33
22.39
24.97 2
ACB
15.96
20.72
21.79 3
BAC
19.02
22.78
25.46 4
BCA
25.36
27.48
27.91 5
CAB
19.52
24.64
26.75 6
CBA
23.27
24.96
25.49
Mean Scores
20.24
23.83
25.40
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7. Graph of Results
The graph shows how the mean search time increases as the number of distracter items increases
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8. Statistical Analysis Appropriate Statistical Analyses
Correlated t-test (for 2 groups only)
Repeated measures ANOVA
Order data so that each line represents one participant and each column represents one condition
Note that the columns represent conditions, NOT the order of testing
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9. ANOVA Summary Table Source df SS MS F p Between 2 83.69 41.85 32.25
<0.01
Subjects 5
95.85
19.17
Error 10
12.97
1.30
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10. Within-Subjects Strengths
More sensitive to small group differences
The variability due to individual differences is statistically eliminated
Fewer participants are needed
Each participant appears in each condition
Instructions may take less time
Participants were already instructed on the task in previous conditions
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11. Within-Subjects Weaknesses
Because participants experience all conditions, they may figure out the hypothesis (potential subject effects)
Major issue is sequence effects
Practice and carry-over effects
Controlled by varying the order of presentation
Counterbalancing
Random order of presentation
Latin square design
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12. Matched-Subjects Designs
Introduces correlation through matched random assignment
Should match on “relevant” variables
Variables that affect the dependent variable
Variables that show considerable natural variation in the population sampled
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13. Matching Participants
Match participants in sets
Set size is equal to the number of conditions
Matching gets more difficult as:
The number of matching variables increases
Matching is done on continuous variables
The number of conditions increase
Once sets are matched, randomly assign participants in the set to the conditions
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14. Statistical Analysis Analyze as if it were a within-subjects study
Data from matched participants are organized as if the data came from a single participant
Act as if the number of participants was equal to the actual number of participants divided by the number of conditions (e.g., for 40 participants and 4 conditions, tell the program that you had 10 participants and 4 conditions in a within-subjects design)
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15. Strengths and Weaknesses
Increased sensitivity to group differences
No sequence effects
Weaknesses
Extra work of matching participants
Participants without appropriate matches cannot be used in the study
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16. Single-Subject Designs
Extensions of within-subjects designs
Single participant tested under all conditions, with the researcher actively manipulating the independent variable
Variation on time-series designs, with repeated measurement of the dependent variable
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17. History of these Designs
Intensive studies of individuals was common before R. A. Fisher
Fisher’s development of ANOVA changed the focus of psychology to comparing groups of individuals
Skinner was one of the few psychologists who advocated the intensive study of individuals
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18. Logic of these Designs Includes independent variable manipulation
Expect dependent variable response
Note that the response must occur shortly after the manipulation unless there is a theoretical reason to expect a delay
Multiple measures before and after the manipulation to identify normal variation and rule out regression to the mean
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19. Single-Subject Designs
Basic Single-Subject design includes
Baseline period
A treatment phase
A post-treatment evaluation period
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20. Types of Designs ABA Reversal Design Multiple Baseline Design
Single-Subject, Randomized, Time-Series Design
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21. Reversal Design LOGIC PROCEDURES
Apply, then remove, independent variable manipulation
If change occurs at both application and removal, it suggests a causal connection
PROCEDURES
Baseline measures (Condition A)
Treatment application (Condition B)
Return to baseline (Condition A again)
Additional reversals can be included
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22. Reversal Design Example
Reversal conditions
Condition A: attending to self-stimulation
Condition B: withdrawing attention when self-stimulation occurs
Hypothetical results (next slide) suggests that attention does influence self-stimulation
Ethics requires return to Condition B
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23. Hypothetical Results
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24. Published Example DeLeon et al. (1997)
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25. Multiple Baseline Design
LOGIC
Show the effect of the independent variable on several dependent variables
Use when
Reversals are undesirable
Behavioral changes would not reverse
PROCEDURES
Baseline
Manipulation focused on first dependent variable
Manipulation focused on second dependent variable
…and so on
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26. Multiple Baseline Design
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27. Variations on Multiple Baseline Design
Across Behaviors
Testing effects on different behaviors
Across Individuals
Testing effects on different people
Across Settings and Time
Testing effects in different settings or at different times
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28. Single-Subject, Randomized Time-Series Design
LOGIC
Repeated measures of the dependent variable interrupted by a randomly placed intervention
If change occurs at the point of intervention, it suggests a causal connection
PROCEDURES
Select minimum baseline and follow-up periods
Randomly select the point of intervention
Compare pattern of scores before and after the intervention
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29. Single-Subject, Randomized Time-Series Design
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30. Replicating Single-Subject Experiments
Direct replication
Repeating study on the same target behavior
Systematic replication
Evaluate procedures across subjects, settings, and/or target behaviors
Clinical replication
Combining procedures into a treatment “package”
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31. Summary Can introduce a correlation in two ways
Within-subjects and matched-subjects designs
These designs are more sensitive to small differences between groups
The costs for the greater sensitivity are:
Sequence effects (within-subjects design)
Matching difficulties (matched-subjects design)
Single-subject designs allow the experimental manipulation of variables
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