Evaluating sources of baseline data using dual‐criteria and conservative dual‐criteria methods: A quantitative analysis
Using DC or CDC rules, FA-extracted baselines slightly inflate false positives yet stay clinically acceptable.
01Research in Context
What this study did
Falligant et al. (2020) asked a simple question. Does it matter where you grab baseline data when you use DC or CDC visual rules?
They compared two sources. One group used the first points from the functional-analysis condition. The other group used a separate baseline phase. Then they counted how often each source cried “improvement” when nothing really changed.
What they found
Baselines pulled from the FA itself gave more false alarms. Still, both sources kept the error rate low enough for day-to-day use.
In short, you can trust either source, but know that FA-scraped baselines lean slightly toward yelling “it worked” too soon.
How this fits with other research
Falligant et al. (2020) ran a sister study the same year. That paper showed longer Phase B cuts false positives and baseline type still matters. Together the two papers say: pick your baseline source, then give the next phase time to breathe.
Rader et al. (2021) found even doctoral BCBAs misread FA graphs about one-third of the time. Falligant’s work does not clash; it offers a guardrail. Use DC/CDC rules so the graph, not the reader, makes the call.
Mount et al. (2011) showed novices quit baseline early when data bounce around. Falligant adds a tool: if you must use jumpy FA points as baseline, pair them with the conservative CDC rule to slow the exit.
Why it matters
You can stop worrying about finding the “perfect” baseline. Pull the first FA points if you must, just stay alert for false positives and run a few more sessions in the next phase. The DC/CDC checkers are free, fast, and keep your visual inspection honest.
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02At a glance
03Original abstract
Scheithauer et al. (2020) recently demonstrated that differences in the source of baseline data extracted from a functional analysis (FA) may not affect subsequent clinical decision-making in comparison to a standard baseline. These outcomes warrant additional quantitative examination, as correspondence of visual analysis has sometimes been reported to be unreliable. In the current study, we quantified the occurrence of false positives within a dataset of FA and baseline data using the dual-criteria (DC) and conservative dual-criteria (CDC) methods. Results of this quantitative analysis suggest that false positives were more likely when using FA data (rather than original baseline data) as the initial treatment baseline. However, both sources of baseline data may have acceptably low levels of false positives for practical use. Overall, the findings provide preliminary quantitative support for the conclusion that determinations of effective treatment may be easily obtained using different sources of baseline data.
Journal of Applied Behavior Analysis, 2020 · doi:10.1002/jaba.710