Table Of Content
We considered that a phase change showed a clear change when a sufficient number of points fell above or below both lines (see Table 1 from Fisher et al. (2003) for specific number of points); if not, we rated it as showing no clear change. For each dataset, we then compared the results of the first AB component to the subsequent BA and AB components. Table 2 presents a summary of methodological and assessment standards to permit conclusions about treatment effects [29, 30]. These standards were derived from Horner et al. [29] and from the recently released What Works Clearinghouse (WWC) pilot standards for evaluating single-case research to inform policy and practice (hereafter referred to as the SCD standards) [31]. The table also presents some procedural information and advantages and disadvantages for each design.
Multiple-Baseline and Multiple-Probe Designs
The principal difference between SCEDs and between-subjects experimental designs concerns the definition of the experimental units. Comparing the internal validity of ABA design and ABAB design, both designs offer reliable means of assessing the effectiveness of interventions. While ABA design focuses on comparing the intervention phase to the baseline phase, ABAB design takes it a step further by demonstrating reversibility and the direct impact of the intervention. During the initial baseline phase (A), the behavior of interest is observed and measured without any intervention. Once the baseline data is collected, the intervention phase (B) begins, where a specific treatment or intervention is implemented. Internal validity is a crucial concept in research methodology, especially when evaluating the effectiveness of interventions.
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The repeated measures, and resulting time-series data, that are inherent to all SCDs (e.g., reversal and multiple-baseline designs) make them useful designs to conduct parametric analyses. For example, two doses of a medication, low versus high, labeled B and C, respectively, could be assessed using a reversal design [67]. There may be several possible sequences to conduct the assessment such as ABCBCA or ABCABCA. If C is found to be more effective of the two, it might behoove the researcher to replicate this condition using an ABCBCAC design. A multiple baseline across participants could also be conducted to assess the two doses, one dose for each participant, but this approach may be complicated by individual variability in medication effects.
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In many cases, the clinical significance of behavior change between conditions is less clear and, therefore, is open to interpretation. Wacker and colleagues (1990) conducted dropout-type component analyses of functional communication training (FCT) procedures for three individuals with challenging behavior. The data presented in Figure 6 show the percentage of intervals with hand biting, prompts, and mands (signing) across functional analysis, treatment package, and component analysis phases. The functional analysis results indicated that the target behavior (hand biting) was maintained by access to tangibles as well as by escape from demands.
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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. A multiple baseline design can be used when there is more than one individual or behavior in need of treatment. The alternating treatments design can be used when you want to determine the effectiveness of more than one treatment. The changing conditions design can be used to study the effect of two or more treatments on the behavior of an individual.
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Experimental designs like ABA and ABAB are invaluable in the realm of research, enabling investigators to evaluate the impact of interventions and treatments. While ABA is simpler and effective for initial assessments, ABAB designs offer a more comprehensive view, providing insights into replicability and sustainability. The ABA design is used when the analyst wants to provide stronger evidence that the treatment they have chosen was an effective one.
Pros and Cons of ABA Design
Within these designs, typically an initial baseline condition (A1) is first implemented in which the IV is not in place. Then the IV or intervention (B1) is put into place, with data again being recorded until a stable trend is displayed. The next condition withdraws the IV and returns to the baseline condition (A2), which if successful in establishing a functional relationship, should show a data pattern similar to the first baseline (A1). The last condition is reinstating the IV (B2), which should be similar to the data in (B1) if a functional relationship is to be demonstrated. When it comes to experimental research designs, both ABA (also known as withdrawal design) and ABAB (also known as reversal design) play significant roles in understanding and evaluating the effects of interventions. While they have similarities, there are key differences that distinguish them from each other.
Multiple-Treatment Designs
This helps to establish a causal relationship between the treatment and the behavior change. It allows researchers and practitioners to have confidence in the effectiveness of the interventions being studied. External validity, which refers to the generalizability of the findings to other populations and settings, is also important. Having a clear understanding of internal validity is essential when considering the design and implementation of interventions in Applied Behavior Analysis (ABA) and ABAB design. Internal validity refers to the degree to which a study accurately measures the relationship between the independent variable and the dependent variable, without being influenced by confounding factors or biases.
An even bigger concern is the use of a withdrawal design when the goal of the intervention is to decrease an unsafe behavior (e.g., physical aggression or self-injurious behavior). Withdrawing the treatment even for a brief period of time presents the opportunity for an increase in the unsafe behavior. Withdrawal of the interventions in this situation should be limited to cases in which determining that the IV is the cause of behavior is necessary in order for treatment to be successful. In many cases, if the behavior goes down after the IV is implemented, then that is likely enough evidence to continue the intervention. The scientific analysis of behavior seeks to demonstrate a functional relationship between an independent variable (IV, i.e., the intervention) and the behavior or dependent variable (DV; Baer, Wolf, & Risely, 1968; Skinner, 1953). Experimentation is designed to demonstrate how the IV impacts or changes the DV; in other words, how does an intervention change behavior.
In ABAB design, internal validity refers to the extent to which the observed changes in behavior are a direct result of the intervention and not due to other factors. Following the intervention phase, the treatment is removed, and a second baseline phase (A) is reinstated to determine if the behavior returns to its original level. Finally, the intervention phase (B) is reintroduced to observe whether the behavior changes again.
By returning to baseline conditions, we could assess and possibly rule out the influence of the price increase on smoking. Not only is this desirable from the participant’s perspective but it also provides a replication of the main variable of interest—the treatment [33]. However, doing so comes at the cost of practitioner flexibility in making phase/condition changes based on patterns in the data (i.e., how the participant is responding). This cost, it is argued, is worth the expense because randomization is superior to replication for reducing plausible threats to internal validity. The within-series intervention conditions are compared in an unbiased (i.e., randomized) manner rather than in a manner that is researcher determined and, hence, prone to bias. The net effect is to further enhance the scientific credibility of the findings from SSEDs.
In light of traditional methods to establish preliminary efficacy and optimize treatments, Riley and colleagues advocated for “rapid learning research systems.” SCDs are one such system. Guyatt and colleagues also investigated the minimum duration of treatment necessary to detect an effect [5]. Visual analysis of the time-series data revealed that medication effects were apparent within about 1–2 weeks of exposure, making a 4-week trial unnecessary. This discovery was replicated in a number of subjects and led them to optimize future, larger studies by only conducting a 2-week intervention. The duration of the baseline and the pattern of the data should be sufficient to predict future behavior.
By utilizing these designs, professionals in the field can gain insights into the effectiveness of interventions and tailor therapy approaches to meet the specific needs of individuals with autism and other behavioral challenges. The first aspect is the overall difference in level between phases, which we quantified using the absolute mean difference between all A phase observations and all B phase observations. Another important indicator for treatment effectiveness in randomized AB phase designs is the immediacy of the treatment effect (Kratochwill et al., 2010). On the basis of the recommendation by Kratochwill et al., we defined the ITEI in a randomized AB phase design as the average difference between the last three A observations and the first three B observations. In accordance with the WWC standards’ recommendation that a “phase” should consist of five or more measurement occasions (Kratochwill et al., 2010), we took a minimum limit of five measurement occasions per phase into account for the start point randomization in the RT.
In contrast, during the intervention phase, performance is stable, with a range of only 6%. All three of these types of changes may be used as evidence for the effects of an independent variable in an appropriate experimental design. The use of single-subject experimental designs (SSEDs) has a rich history in communication sciences and disorders (CSD) research.
First, the manipulation of the treatment effect in this simulation study was very large and accounted for most of the variability in the power. Consequently, the expected size of the treatment effect is an important factor in selecting the number of measurement occasions for the randomized AB phase design. Of course, the size of the treatment effect cannot be known beforehand, but it is plausible that effect size magnitudes vary depending on the specific domain of application.
This can make them more practical with behaviors for which a return to baseline levels cannot occur. Depending on the speed of the changes in the previous conditions, however, one or more conditions may remain in the baseline phase for a relatively long time. Thus, when multiple baselines are conducted across participants, one or more individuals may wait some time before receiving a potentially beneficial intervention. A more recent simulation study by Levin, Ferron, and Gafurov (2018) investigating several different randomization test procedures for multiple-baseline designs showed similar results. Another option to obtain phase designs with more statistical power would be to extend the basic AB phase design to an ABA or ABAB design. Onghena (1992) has developed an appropriate randomization test for such extended phase designs.
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