A-B-A-B
Design
The
A-B-A-B design represents an attempt to measure a
baseline (the first A), a treatment measurement (the
first B), the withdrawal of treatment (the second
A), and the re-introduction of treatment (the second
B). In
other words, the A-B-A-B design involves two parts:
(1) gathering of baseline information, the
application of a treatment and measurement of the
effects this treatment; and (2) measurement of a
return to baseline or what happens when the
treatment is removed and then again applying the
treatment and measuring the change.
In
terms of an actual study, imagine you are attempting
to train your dog to sit on command.
As a simple example how this design might
work, imagine you just adopted two untrained
puppies. Youre first goal as a new dog owner is to teach both
puppies to sit on command.
You want to measure the effects of using a
treat or biscuit versus using verbal and physical
praise, so you apply the treat to Puppy 1 and the
praise to Puppy 2.
The
initial A in this design refers to a baseline for
each subject. To
determine this, you might say the command sit
to each puppy at ten different times and measure how
many times each puppy actually sat.
If puppy 1 sat twice, the ratio would be 2:10
or 20%. If
puppy 2 sat twice, the ratio would be 2:10 or 20% as
well. The
next step would be to apply the treatment or
training to each puppy.
Each puppy would be commanded to sit twice
every ten-minutes for a two-hour period.
Puppy 1 would receive a biscuit if he
responded appropriately and puppy 2 would receive
praise. Neither
would receive any reinforcement or punishment for
noncompliance.
At the end of the two-hour period, the change
due to treatment is measured (the first B). If puppy 1 sat 16 times, the ratio would be 16:24 or 67% and
if puppy 2 sat 12 times, the ratio would be 12:24 or
50%.
The
training would then cease for a period of time, say
24 hours, and the puppies would then be commanded to
sit similar to how we determined the original
baseline. This second baseline (the second A) measures the effects of
extinction, or the withdrawal of the positive
reinforcer, on behavior.
Without continual reinforcement, we are
determining if the second baseline returns to the
original or if the behavioral change we experienced
will continue.
For the sake of this example, assume the
second baseline for puppies 1 and 2 is 20% and 45%
respectively.
Finally,
the treatment is once again applied (the second B)
to measure the effects of spontaneous recovery.
Well assume the re-application of the
treatment or training resulted in a ratio of 67% for
puppy 1 and 80% for puppy 2.
So what does this all
mean? If
you look back at the original A-B, youll notice
that the training with a biscuit increased the ratio
of response from 20% to 67% and the training with
praise increased the behavior from 20% to 50%.
From this initial data, it appears as if the
use of a biscuit produced a greater change in
behavior. However,
looking at the withdrawal of the reinforcer produced
a return to the baseline for puppy 1 while puppy 2
held onto some behavioral change. This would suggest that the use of a biscuit for training
produces a greater change but results in a greater
loss when it is no longer used.
The puppy that received the praise was more
likely to hold onto gains in his behavioral change. See Figure 4.
The
final training also suggests that the puppy that
received the biscuit returned to where he was after
treatment once the reinforcer was applied again but
that puppy 2 jumped way up to 80%.
The final outcome for our single subject
design using two subjects would suggest that the use
of praise results in an overall increase in
behavioral change when compared with the biscuit.
In the final analysis, the praise would be
considered a superior reinforcer or training method
than the biscuit.
Figure
4.1: Determination of Best Training Method
Finally, different
treatments can be applied to different subjects in
order to compare results.
Figure 4.2 shows the application of the ABABA
design on three subjects each being applied a
different treatment.
From the chart it is obvious that treatment 1
increases the desired behavior but that this
behavior returns quickly to baseline when the
treatment is discontinued.
Treatment 2 shows no change in behavior in
any of the trials.
Treatment 3, however, shows an increase in
the desired behavior when the treatment is applied
and that this increase remains consistent even when
the treatment is discontinued.
If the goal is a longstanding increase in
behavior, treatment 3, from the information
available and of the choices offered, is obviously
the best approach.
The A-B-A-B design can
obviously be altered to include any number of
baselines and treatment phases.
To determine the effects of treatment and the
degree of extinction only, a simpler A-B-A design
would be used To
determine if additional training changes the
ultimate results, a more complex A-B-A-B-A-B-A-B
could be employed.
Each study could be completed with only one
subject or the results of different subjects with
different treatment approaches could be compared
(See Figure 4.2).
The complexity of the study depends on both
the original intention and feasibility.
Figure
4.2: Application Three Different Treatments on Three
Single Subjects
When
a more complex schedule is applied, the extinction
phase for one treatment can become the baseline
phase for an additional treatment.
Simply put, this method allows overlapping
treatments to be tested with only a single subject.
Figure 4.3 demonstrates the hypothetical
outcome of using a single subject to determine the
effects of three different treatment methods.
For the first treatment, the initial A is
considered the baseline for Treatment A and B1
represents the application of the first treatment.
A2 represents the removal of Treatment A and
also acts as the baseline for the second treatment. The second treatment is applied at B2 followed by another
extinction phase (A3), which then becomes the
baseline for the third treatment applied at B3.
The final A represents the extinction phase
of the final treatment method.
Looking at the results
in Figure 4.3, you will notice that Treatment A
appears to have had the most effect, however, once
Treatment A was discontinued, the behavior returned
nearly to baseline.
Treatment B had a moderate effect but during
the extinction phase, the behavior remained at a
moderate level suggesting a low or non-existent
extinction of the treatment. Finally, Treatment C appears to have reduced the desired
behavior and is therefore acting as a punisher
rather than a reinforcer.
Luckily the behavior returned to baseline
once Treatment C was discontinued.
The results therefore show that the best
treatment in terms of change is Treatment A but the
best treatment in terms of longer standing impact is
Treatment B. Treatment
C is the worst treatment as it had the opposite
effect than intended.
Figure
4.3: Determination of Best Training Method
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