Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 14: Correlated Groups Designs 1 Objectives Correlated groups Repeated-measures designs Carryover effects
Matched-group designs Mixed-model designs 2 Why Correlated-Groups? Sometimes the treatment groups in a study are not independent This is an assumption underlying between-groups designs 3
Correlated-Groups Logic Minimizing within-group variance is a constant goal (increases our power) Homogeneous groups can help Subject variables can also be used Correlated-groups is another good design strategy 4 Correlated-Groups Logic
Figure 14.1 We can partition out variance due to individual differences This reduces MSwithin and increases the F Two options: Repeated measures design Matched-participants design 5 Figure 14.1
6 Repeated-Measures Design Data collected over multiple conditions, using the same set of participants Testing same person over multiple levels of the IV Could be manipulated or could be time Figure 14.2 7
Figure 14.2 8 Repeated-Measures Results Summary table e.g., Table 14.3 Notice that you are able to partition out an additional chunk of the variance in the DV Compare with Table 14.2 This reduces MSwithin
For this type of design the groups are not independent 9 Table 14.2 vs. Table 14.3 Between Within Total Between Participants Within
Total SS 22.4286 36.2500 58.6786 df 6 21 27
MS 3.7381 1.7262 F 2.166 p 0.088 SS 22.43
26.11 10.14 58.68 df 6 3 18 27 MS 3.74
8.70 0.56 F 6.63 p 0.00 10 Repeated-Measures Pros/Cons
Advantages Increased power Each participant is his/her own control Smaller required sample size Disadvantages Several forms of potential carryover effects Table 14.4 11 Reducing Carryover Effects Several design options exist
Use between-subjects design Especially if experiment cause irreversible change Use a special design modification: Solomon four-group design Counterbalancing Latin square design 12 Solomon Four-Group Design Incorporates 3 control groups to account for sequence-related events
Use a 2 x 2 factorial (b-g) ANOVA Table 14.5 and Figure 14.4 illustrate Group 1 Group 2 Pretest O1 Treatment Treatment
O3 Group 3 Treatment Group 4 Note: O x represents an observation or test score Posttest O2 O4 O5 O6
13 Counterbalancing Requires random shuffling of the sequence of testing for each participant Total # of possible arrangements = k! Sample size must allow you to have enough people to adequately test each of the possible arrangements
14 Latin Square Design Alternative to full counterbalancing Ensures that a. Each condition occurs once in each position of the sequence b. Sequence is random Table 14.7 Complex analysis though 15
Matched-Group Design Retains power of repeated-measures, but tests each participant in only 1 condition Used when an important subject variable correlates with the DV Each condition is a separate group of participants Matched across conditions to control for variance due to this subject variable 16
Matched-Groups Design Table 14.8 shows steps Ordered pairs of participants have similar scores on the pretest A.K.A. randomized block design Sometimes matching is done on several subject variables at once Because groups are matched, they are not independent 17 Table 14.8
18 Matched-Groups Pros/Cons Advantage Potential for high power Disadvantage Potential for low power if matching technique fails to match on an important subject variable Important = significantly linked to DV
19 Yoked-Control Group Researcher randomly pairs control participant with active participant Both participants experience exactly the same sequence of study procedures, except the control participant is not exposed to the IV Figure 14.6 20
Figure 14.6 21 Mixed-Model Designs Between- and within-subjects elements combined Between-subjects: Experimental vs. Control Within-subjects: Multiple trials Somewhat more complicated in terms of design, but analysis is based on same principles as we have already discussed
This chapters Research in Action section provides a good illustration 22 What is Next? **instructor to provide details 23
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