Analyzing Data From Single-Case Alternating Treatments Designs
Alternating treatments designs (ATDs) have received comparatively less attention than other single-case experimental designs in terms of data analysis, as most analytical proposals and illustrations have been made in the context of designs including phases with several consecutive measurements in the same condition. One of the specific features of ATDs is the rapid (and usually randomly determined) alternation of conditions, which requires adapting the analytical techniques. First, we review the methodologically desirable features of ATDs, as well as the characteristics of the published single-case research using an ATD, which are relevant for data analysis. Second, we review several existing options for ATD data analysis. Third, we propose 2 new procedures, suggested as alternatives improving some of the limitations of extant analytical techniques. Fourth, we illustrate the application of existing techniques and the new proposals in order to discuss their differences and similarities. We advocate for the use of the new proposals in ATDs, because they entail meaningful comparisons between the conditions without assumptions about the design or the data pattern. We provide R code for all computations and for the graphical representation of the comparisons involved.