ABAB Design — Reversal / Withdrawal Design

Both are reversal designs, but the ABAB design adds a second intervention phase after the withdrawal. This second B phase provides an independent replication of the functional relationship within the same study, substantially strengthening causal inference. The ABA design ends with the withdrawal phase, leaving the participant in the no-treatment condition and offering only one demonstration of experimental control.

A failure to reverse can mean several things: the behavior may have been permanently acquired (making reversal design inappropriate), a history or carryover effect may be masking the reversal, or the baseline was not stable enough to detect change. Document the non-reversal carefully. If reversal is conceptually expected but does not occur, consider switching to a multiple baseline design, which does not require withdrawal, for future work.

This is a genuine concern. Ethics committees and participants must be informed in advance that the intervention will be temporarily withdrawn. The withdrawal is generally acceptable when (a) the behavior poses no immediate safety risk, (b) the withdrawal phase is kept as brief as scientifically necessary, and (c) the intervention is fully reinstated in the final B phase. Some behaviors — crisis-level self-injury, life-sustaining treatments — make any withdrawal unacceptable, and an alternative design such as a multiple baseline should be used.

There is no fixed rule, but the conventional minimum is three data points per phase. In practice, researchers continue collecting data until the series shows a stable pattern — consistent level, predictable trend, and low variability. Five or more data points per phase is a widely recommended target. Stability criteria should be defined a priori to prevent selective stopping.

Yes. Running the ABAB design across multiple participants independently constitutes systematic direct replication, which is how single-subject research builds generalizable evidence. Each participant's data are analyzed individually; the cross-participant consistency of effects is then evaluated as evidence of external validity. This is different from averaging data across participants, which would obscure the individual-level functional relationships the design is built to reveal.