Single Case Experimental Designs Strategies For Studying Behavior Change

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Single Case Experimental Designs Strategies For Studying Behavior Change

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Information For Authors (jcp)

Single-Condition Experimental Designs provides a clear and comprehensive introduction to the use of single-condition experimental designs. The aim of this book is to provide a comprehensive sourcebook on unique experimental designs with practical guidance on their use in a range of research and clinical settings. It is suitable for use as a textbook for a research methodology or clinical assessment and treatment course, or as a desktop reference for experienced researchers and practicing clinicians.

Negative, positive, neutral – we always leave feedback online. First we check that it meets the terms of the review and that it is not fake. We’ll also check if it was written by someone who bought the item, and then include that. Checks are done automatically, although sometimes people are watching. does not pay for reviews. If the reviewer is covered by another party, this will be indicated in the review itself. Many of these features are illustrated in Figure 10.2, which shows the results of a common single-subject study. First, the dependent variable (shown in

Axis) at regular intervals. Second, the study is divided into separate phases and the participant is tested under one condition in each phase. Conditions are often denoted by capital letters: A, B, C, etc. Thus, Figure 10.2 shows a design in which the participant is first tested in one condition (A), then in another condition (B), and finally tested again in the original condition (A). (This is called reverse design and will be discussed in more detail shortly.)

Another important aspect of single-subject research is that transitions from one condition to another usually do not occur after a given time or number of observations. Instead, it depends on the behavior of the participant. Specifically, the researcher waits until the participant’s behavior in one condition is sufficiently consistent from observation to observation before changing conditions. This is sometimes called

Goals Of The Presentation

(Sidman, 1960) [1]. The idea is that if the dependent variable is stable, it will be relatively easy to detect any change in terms. We recall that we encountered the same principle in a more general discussion of experimental research. It is easier to determine the effect of an independent variable when the “noise” in the data is minimized.

Is set for the dependent variable. This is the response rate before any treatment and therefore the baseline is a specific control condition. When a steady-state response is achieved, phase B begins when the investigator starts treatment. There may be a period of adaptation to treatment, during which the behavior of interest becomes variable and begins to increase or decrease. Again, the researcher waits until this dependent variable is stable so that it becomes clear how much has changed and changed. Finally, the researcher withdraws the treatment and waits again until the dependent variable stabilizes. This basic reversal design can also be expanded with treatment reentry (ABAB), return to base again (ABABA), and more.

Hall and colleagues’ study was an ABAB reversal design. Figure 10.3 shows Robbie’s rough data. The proportion of time spent studying (the dependent variable) was low at the first baseline, increased to level off in the first treatment phase, decreased at the second baseline, and returned to increase in the second phase of treatment.

Figure 10.3 Summary of Hall and colleagues’ results in Robbie’s ABAB reversal design [Long description]

Experimental Subjects Are Not Different

Why is reversal—removal of treatment—considered necessary in this type of design? Why use an ABA design, for example, instead of a simpler AB design? Note that the AB design is essentially an interrupted time series design applied to the individual participant. Recall that a problem with this design is that if the dependent variable changes after the treatment is introduced, it is not always clear that the treatment is responsible for the change. Something else may have changed at about the same time, and that 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

With the removal of the treatment (assuming the treatment does not produce a permanent effect), it becomes much clearer that the treatment (and the removal of the treatment) is the cause. In other words, reverse learning significantly increases the internal validity of research.

There are close relatives of the basic reversal design that allow the evaluation of multiple treatments. in AD

, the main stage is followed by separate stages that include different treatment methods. For example, a researcher might frame a disruptive student’s learning behavior (A), then introduce a treatment involving positive teacher attention (B), then switch to a treatment involving mild punishment for not studying can (C). The participant can then be returned to baseline before each treatment is reintroduced, perhaps in reverse order as a way to control for carryover effects. This particular inverse multiprocessing design can also be called an ABCACB design.

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, two or more treatments alternate relatively quickly on a regular schedule. For example, one day positive attention can be used for studying and the next mild punishment for not studying, etc. Or one treatment can be done in the morning and the other in the afternoon. Alternative treatment designs can be a quick and efficient way to compare treatments, but only when the treatments have rapid effects.

There are two potential problems with the recovery design, both of which involve treatment withdrawal. One is that if the treatment works, it is unethical to remove it. For example, if a treatment reduces the self-injury of a child with a developmental disability, it is unethical to remove that treatment to show that self-injury is increasing. A second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, if the positive focus on studying is lost, the student may continue to study at a higher rate. This could mean that positive attention had a lasting effect on student learning,

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