1. Single-Variable, Correlated-Groups Designs
Graziano and Raulin
Research Methods: Chapter 11
Graziano & Raulin (2000)
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
Groups formed by matched random assignment
More sensitive than independent-groups designs
Graziano & Raulin (2000)

3. Within-Subjects Designs
All participants are exposed to all experimental conditions
Need to control for sequence effects
Sequence effects result from the experience with one condition affecting the performance in subsequent conditions
Controlled by varying the order of presentation (such as with counterbalancing)
Graziano & Raulin (2000)

4. 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 row represents one condition
Note that the columns represent conditions, NOT the order of testing
Graziano & Raulin (2000)

5. Within-Subjects Strengths
More sensitive to small group differences because the variability due to individual differences is statistically eliminated
Fewer participants are needed because each participant appears in each condition
Instructions may take less time because participants were already instructed on the task in previous conditions
Graziano & Raulin (2000)

6. 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
Graziano & Raulin (2000)

7. Matched-Subjects Designs
Introduces correlation by matching the participants in each group with participants from the other groups
Should match on “relevant” variables
Variables that affect the dependent variable
Variables that show considerable natural variation in the population sampled
Graziano & Raulin (2000)

8. Matching Participants
Match participants in sets, where the size of the set 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, you randomly assign the participants in the set to the conditions
Graziano & Raulin (2000)

9. 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
Tell the program that 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
Graziano & Raulin (2000)

10. Strengths and Weaknesses
Increased sensitivity to small differences between groups,but without the sequence effects of within-subjects designs
Weaknesses
Extra work of matching participants
Participants without appropriate matches cannot be used in the study
Graziano & Raulin (2000)

11. Single-Subject Designs
Single-subject designs are extensions of within-subjects designs
One participant is tested under all conditions
A variation on time-series designs, with repeated measurement of the dependent variable
Highly refined single-subject designs exist
These designs covered in Chapter 13
Graziano & Raulin (2000)

12. Summary Can introduce a correlation in two ways
Within-subjects designs
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)
Graziano & Raulin (2000)