Single-Variable, Correlated-Groups Designs Graziano and Raulin Research Methods: Chapter 11 Graziano & Raulin (2000) Graziano & Raulin (1997)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)