intro & more content is needed, but here is a start…

Source :

• https://kpu.pressbooks.pub/psychmethods4e/chapter/single-subject-research-designs/  (Section: Reversal Designs) NOTE: THIS HAS FIGURES WE MIGHT WANT TO USE
  
• Mauldin, 11.2

Withdrawal, or Reversal, Design

Another option would be to withdraw treatment for a specified time and continue to measure the client, establishing a new baseline. If the client continues to improve after the treatment is withdrawn, then it is likely to have lasting effects. This is called a withdrawal design and is represented as A-B-A or A-B-A-B.

Reversal Designs: It is the most single-subject research design, also known as ABA design. During the first phase, A, a baseline is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. There may be a period of adjustment to the treatment during which the behavior of interest becomes more variable and begins to increase or decrease. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on.

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? Why use an ABA design, for example, rather than a simpler AB design? Notice that an AB design is essentially an interrupted time-series design applied to an individual participant. Recall that one problem with that design is that if the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment (assuming that the treatment does not create a permanent effect), it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

There are close relatives of the basic reversal design that allow for the evaluation of more than one treatment. In, multiple-treatment reversal design, you start with a baseline phase and then try different treatments one by one. For example, one might begin by observing a disruptive student’s behavior (A), then give them positive attention from the teacher (B), and later try mild punishment for not studying (C) we can go back to the baseline before reintroducing each treatment, maybe in the reverse order to account for any effect from previous treatments. This type of design is also called as ABCACB design. a baseline phase is followed by separate phases in which different treatments are introduced.

Alternating-treatment designs: In an alternating treatment design, two or more treatments are altered relatively quickly on a regular schedule. For example, positive attention for studying could be used one day and mild punishment for not stuyding the next, and so on. Or one treatment could be implemented in the morning and another in the afternoon. Alternating treatment designs are often a quick and effective way of comparing treatments, but only when the treatments are fast acting.

Mutiple-baseline design

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a child with an intellectual delay, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place. Perhaps something else happened at about the same time as the treatment—for example, the student’s parents might have started rewarding him for good grades. One solution to these problems is to use a , which is represented in Figure 10.3. There are three different types of multiple-baseline designs which we will now consider.

Multiple-Baseline Design Across Participants

In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is unlikely to be a coincidence.

Multiple-Baseline Design Across Behaviors

In another version of the multiple-baseline design, multiple baselines are established for the same participant but for different dependent variables, and the treatment is introduced at a different time for each dependent variable. Imagine, for example, a study on the effect of setting clear goals on the productivity of an office worker who has two primary tasks: making sales calls and writing reports. Baselines for both tasks could be established. For example, the researcher could measure the number of sales calls made and reports written by the worker each week for several weeks. Then the goal-setting treatment could be introduced for one of these tasks, and at a later time the same treatment could be introduced for the other task. The logic is the same as before. If productivity increases on one task after the treatment is introduced, it is unclear whether the treatment caused the increase. But if productivity increases on both tasks after the treatment is introduced—especially when the treatment is introduced at two different times—then it seems much clearer that the treatment was responsible.

Multiple-Baseline Design Across Settings

In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home. Then a treatment such as positive attention might be introduced first at school and later at home. Again, if the dependent variable changes after the treatment is introduced in each setting, then this gives the researcher confidence that the treatment is, in fact, responsible for the change.

Multiple Treatment Design

In single-subjects design, it is possible to  begin a new course of treatment or add a new dimension to an existing treatment.  This is called a multiple treatment design.  The graphing would continue as before, but with another vertical line representing the second intervention, indicating a new treatment began.