Abstract(s)
Although visual inspection remains common in the analysis of single-case designs, the lack of
agreement between raters is an issue that may seriously compromise its validity. Thus, the
purpose of our study was to develop and examine the properties of a simple structured criterion
to supplement the visual analysis of alternating-treatment designs. To this end, we generated
simulated datasets with varying number of points, number of conditions, effect sizes and
autocorrelations, and then measured Type I error rates and power produced by the visual
structured criterion (VSC) and permutation analyses. We also validated the results for Type I
error rates using nonsimulated data. Overall, our results indicate that using the VSC as a
supplement for the analysis of systematically alternating-treatment designs with at least five
points per condition generally provides adequate control over Type I error rates and sufficient
power to detect most behavior changes.