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CHAPTER 7: Repeated Measures Designs 245FIGURE 7.3In this chapter we introduced repeated measures designs and methods for counterbalancing.Repeated Measures DesignsOnceHow many times was eachlevel given to each participant?More than onceIncompleteCompleteAre there four orfewer conditions?Are there likely to beanticipation problems?Yes No Yes NoAll possibleordersSelectedordersBlockrandomizationABBAcounterbalancingstudied most efficiently by testing fewer participants several times. Repeatedmeasures designs are generally more sensitive experiments. Finally, particularareas of psychological research (e.g., psychophysics) may require the use ofrepeated measures designs.For any repeated measures design experiment to be interpretable, however,practice effects must be balanced. Practice effects are changes that participantsundergo because of repeated testing. In a complete repeated measures design,practice effects are balanced for each participant. Block randomization and ABBAcounterbalancing can be used to balance practice effects in a complete repeatedmeasures design. ABBA counterbalancing should not be used, however, if practiceeffects are expected to be nonlinear or if anticipation effects are likely.In an incomplete repeated measures design, each participant receives eachtreatment only once, and the balancing of practice effects is accomplished acrossparticipants. Techniques for balancing practice effects in an incomplete repeatedmeasures design involve either the use of all possible orders or selectedorders (the Latin Square and rotation of a random starting order).The process of data analysis of the results of repeated measures designsis essentially the same as that for analyzing the results of random groupsdesigns. An added step for the complete repeated measures design is that eachparticipant’s scores first must be summarized within each condition. The dataare examined for errors and then summarized using descriptive statistics suchas the mean, standard deviation, and measures of effect size. Null hypothesistesting and confidence intervals are used to make claims that the independentvariable has produced an effect on behavior.The most serious problem in any repeated measures design is differentialtransfer—when performance in one condition differs depending on whichcondition it follows. Procedures for detecting the presence of differentialtransfer are available, but there is little that can be done to salvage a study inwhich it occurs.