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 MS within and increases the F Two options: –Repeated measures design –Matched-participants design 5

Figure

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

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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 MS within For this type of design the groups are not independent 9

Table 14.2 vs. Table

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

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 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

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

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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 chapter’s Research in Action section provides a good illustration 22

What is Next? **instructor to provide details 23