How Can Single-Case Data Be Analyzed? Software Resources, Tutorial, and Reflections on Analysis.
Free R code now turns your single-case graphs into statistics you can cite.
01Research in Context
What this study did
Manolov et al. (2017) built free R tools that mix graphs with statistics for single-case work.
The paper shows how to run level-shift tests, trend lines, and effect sizes in one click.
You still get the familiar line graph, but now it carries p-values and confidence bands.
What they found
No new client data were collected.
The team proved the code works on old data sets and posted everything on GitHub.
Users can copy two lines of R and reproduce any figure or number in the tutorial.
How this fits with other research
Ruiz et al. (2025) now supersedes this work. Their RDARBS package makes the same plots in under a minute without writing code.
Gilroy et al. (2025) extends the idea to literature reviews. SCARF-UI is a point-and-click website that applies the target’s logic to whole groups of studies.
Wolfe et al. (2019) looks like a rival at first glance—they push visual-only rules while Rumen pushes statistics. In truth the two tools answer different questions: Wolfe helps you decide fast if an effect is visible; Rumen gives numbers for the journal reviewer who wants p-values.
Why it matters
You no longer have to choose between eyeballing graphs or paying for expensive software. The free tools from this paper—and the even easier 2025 updates—let you show parents, teachers, and reviewers both a clear picture and a solid number. Download the package, run your next A-B-A-B data, and paste the output straight into your report.
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02At a glance
03Original abstract
The present article aims to present a series of software developments in the quantitative analysis of data obtained via single-case experimental designs (SCEDs), as well as the tutorial describing these developments. The tutorial focuses on software implementations based on freely available platforms such as R and aims to bring statistical advances closer to applied researchers and help them become autonomous agents in the data analysis stage of a study. The range of analyses dealt with in the tutorial is illustrated on a typical single-case dataset, relying heavily on graphical data representations. We illustrate how visual and quantitative analyses can be used jointly, giving complementary information and helping the researcher decide whether there is an intervention effect, how large it is, and whether it is practically significant. To help applied researchers in the use of the analyses, we have organized the data in the different ways required by the different analytical procedures and made these data available online. We also provide Internet links to all free software available, as well as all the main references to the analytical techniques. Finally, we suggest that appropriate and informative data analysis is likely to be a step forward in documenting and communicating results and also for increasing the scientific credibility of SCEDs.
Behavior modification, 2017 · doi:10.1177/0145445516664307