Analytical Options for Single-Case Experimental Designs: Review and Application to Brain Impairment

Abstract

Single-case experimental designs meeting evidence standards are useful for identifying empirically-supported practices. Part of the research process entails data analysis, which can be performed both visually and numerically. In the current text, we discuss several statistical techniques focusing on the descriptive quantifications that they provide on aspects such as overlap, difference in level and in slope. In both cases, the numerical results are interpreted in light of the characteristics of the data as identified via visual inspection. Two previously published data sets from patients with traumatic brain injury are re-analysed, illustrating several analytical options and the data patterns for which each of these analytical techniques is especially useful, considering their assumptions and limitations. In order to make the current review maximally informative for applied researchers, we point to free user-friendly web applications of the analytical techniques. Moreover, we offer up-to-date references to the potentially useful analytical techniques not illustrated in the article. Finally, we point to some analytical challenges and offer tentative recommendations about how to deal with them.

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