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2,11,24–26For example, for an AB design, researchers could specify the number of time points at which outcome data will be collected, (e.g., 20), define the minimum number of data points desired in each phase (e.g., 4 for A, 3 for B), and then randomize the Lobo et al.Page 5J Neurol Phys Ther. Author manuscript; available in PMC 2018 July 01.Author ManuscriptAuthor ManuscriptAuthor ManuscriptAuthor Manuscript
initiation of the intervention so that it occurs anywhere between the remaining time points (points 5 and 17 in the current example).27,28For multiple-baseline designs, a dual-randomization, or “regulated randomization” procedure has been recommended.29If multiple-baseline randomization depends solely on chance, it could be the case that all units are assigned to begin intervention at points not really separated in time.30Such randomly selected initiation of the intervention would result in the drastic reduction of the discriminant and internal validity of the study.29To eliminate this issue, investigators should first specify appropriate intervals between the start points for different units, then randomly select from those intervals, and finally randomly assign each unit to a start point.29Single Case Analysis Techniques for Intervention ResearchThe What Works Clearinghouse(WWC) single-case design technical documentation provides an excellent overview of appropriate SC study analysis techniques to evaluate the effectiveness of intervention effects.1,18First, visual analyses are recommended to determine whether there is a functional relation between the intervention and the outcome. Second, if evidence for a functional effect is present, the visual analysis is supplemented with quantitative analysis methods evaluating the magnitude of the intervention effect. Third, effect sizes are combined across cases to estimate overall average intervention effects which contributes to evidence-based practice, theory, and future applications.2,18Visual AnalysisTraditionally, SC study data are presented graphically. When more than one participant engages in a study, a spaghetti plot showing all of their data in the same figure can be helpful for visualization. Visual analysis of graphed data has been the traditional method for evaluating treatment effects in SC research.1,12,31,32The visual analysis involves evaluating level, trend, and stability of the data within each phase (i.e., within-phase data examination) followed by examination of the immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases (i.e., between-phase comparisons). When the changes (and/or variability) in level are in the desired direction, are immediate, readily discernible, and maintained over time, it is concluded that the changes in behavior across phases result from the implemented treatment and are indicative of improvement.