Chapter 11 Human Participants Review

1. Chapter 11Human Participants Review Wednesday, July 16

2. Single-variable, 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

3. Correlated-Groups Designs • More sensitive than independent-groups designs • Controlling for individual differences makes it easier to detect small effects • The existence of a correlation between conditions has important implications for design and analysis

4. Within-Subjects Designs • All participants are exposed to all experimental conditions • Each participant serves as “his or her own control” • In this way individual differences are removed from the treatment effect

5. Within-Subjects Designs • 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)

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. Strengths and Weaknesses • Strengths • 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

13. 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)