Using Single-Case Designs in Practical Settings: Is Within-Subject Replication Always Necessary?
A big, clean first jump in an ABAB graph usually repeats, so you can sometimes drop the second reversal and still trust your data.
01Research in Context
What this study did
Lanovaz and colleagues looked at 501 published ABAB graphs. They asked: does the first AB slice predict the full reversal pattern?
They scored each graph’s effect size and checked whether the return-to-baseline phase later matched the early trend.
What they found
Big, clear effects seen right away repeated 85% of the time. Larger jumps from A to B meant surer replication.
When the effect looked huge early on, the rest of the graph usually followed suit.
How this fits with other research
Bigham et al. (2013) already showed that stable A-B designs give fewer than 2% false positives. Lanovaz et al. (2019) extend that comfort to ABAB: if the first jump is large, you may not need the second reversal.
Heyvaert et al. (2014) pooled autism studies and said “interventions work.” Lanovaz updates that message by giving a rule of thumb—big effect, skip the extra phases.
Suzuki et al. (2023) add another layer: low baseline variability also helps you trust shorter designs. Combine both clues—large effect plus steady baseline—and you can feel even safer.
Why it matters
You can save time in clinics and schools. When you see a sharp, clean change after the first intervention phase, you might stop at B instead of pulling the treatment away. Fewer reversals mean less stress for clients and faster decisions about what works.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →After the first intervention phase, eyeball the effect—if the level change looks large and steady, consider ending the reversal and moving to maintenance.
02At a glance
03Original abstract
Behavior analysts have widely adopted and embraced within-subject replication through the use of reversal and multielement designs. However, the withdrawal of treatment, which is central to these designs, may not be desirable, feasible, or even ethical in practical settings. To examine this issue, we extracted 501 ABAB graphs from theses and dissertations to examine to what extent we would have reached correct or incorrect conclusions if we had based our analysis on the initial AB component only. In our first experiment, we examined the proportion of datasets for which the results of the first AB component matched the results of the subsequent phase reversals. In our second experiment, we calculated three effect size estimates for the same datasets to examine whether these measures could predict the relevance of conducting a within-subject replication. Our analyses indicated that the initial effects were successfully replicated at least once in approximately 85% of the cases and that effect size may predict the probability of within-subject replication. Overall, our results support the rather controversial proposition that it may be possible to set threshold values of effect size above which conducting a replication could be considered unnecessary. That said, more research is needed to confirm and examine the generalizability of these results prior to recommending changes in practice.
Perspectives on Behavior Science, 2019 · doi:10.1007/s40614-018-0138-9