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The steps in the changing criterion design must be large enough to clearly show the effects of the independent variable, but not so large that the subject cannot meet the changed criterion. 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.
V. Chapter 5: Experimental Research
If there is a trend in the direction of the anticipated treatment effect during baseline, or if there is too much variability, the ability to detect a treatment effect will be compromised. Thus, stability, or in some cases a trend in the direction opposite the predicted treatment effect, is desirable during baseline conditions. During development and testing of a new intervention, our methods should be efficient, flexible, and rigorous. We would like efficient methods to help us establish preliminary efficacy, or “clinically significant patient improvement over the course of treatment” [12] (p. 137). We also need flexible methods to test different parameters or components of an intervention. Just as different doses of a drug treatment may need to be titrated to optimize effects, different parameters or components of a behavioral treatment may need to be titrated to optimize effects.
Data Analysis in Single-Subject Research
Let's explore the similarities, differences, and considerations when choosing the right design for your study. This approach delivers treatments that usually last for several hours each day. A therapist or behavior technician works with the individual for at least several hours each week and often in different contexts, such as in both home and school settings.
VISUAL, STATISTICAL, AND SOCIAL VALIDITY ANALYSIS
Excessive variability is a relative term, which is typically determined by a comparison of performance within and between conditions (e.g., between baseline and intervention conditions) in a single-case experiment. The mere presence of variability does not mean that a single-case approach should be abandoned, however. Indeed, identifying the sources of variability and/or assessing new measurement strategies can be evaluated using SCDs. Under these conditions, the outcome of interest is not an increase or a decrease in some behavior or symptom but a reduction in variability. Once accomplished, the researcher has not only learned something useful but is also better prepared to evaluate the effects of an intervention to increase or decrease some health behavior.
OPTIMIZATION METHODS AND SINGLE-CASE DESIGNS
Visuals also help individuals on the spectrum understand what’s coming next on their schedule. Many clients will rely on visual support to help manage their anxiety and stress levels. This can result in more appropriate behavior over time and a reduction in tantrums.
These designs are also referred to as interrupted time-series designs [1] and stepped wedge designs [7]. SCDs include an array of methods in which each participant, or case, serves as his or her own control. Although these methods are conceptually rooted in the study of cognition and behavior [14], they are theory-neutral and can be applied to any health intervention. In a typical study, some behavior or symptom is measured repeatedly during all conditions for all participants.
Multiple-Baseline Design Across Settings
Figure 3 presents simplified examples of these two possibilities using a reversal design and short data streams (adapted from [69]). The panel on the left shows additive effects, and the panel on the right shows multiplicative effects. The data also can be analyzed to determine whether each component is necessary and sufficient to produce behavior change.
Comprehensive ABA Therapy
Lastly, consistency can be practicing specific skills like waiting or cleaning up after play. The most important thing is that these skills start to become second nature for the child. It might also be helpful to check in with your therapist during the final five minutes of the session each day. This might be a fantastic time to ask how the session went and if there was anything they would like to share.
The dependent variable ranges between 10 and 15 units during the baseline, then has a sharp decrease to 7 units when treatment is introduced. However, the dependent variable increases to 12 units soon after the drop and ranges between 8 and 10 units until the end of the study. Specifically, the researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions.
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. Instead, the multiple-baseline approach could be used on a within-subject basis, where the durations of not just the baselines but of the different dose conditions are varied across participants [68].
In some cases, the source(s) of variability can be identified and potentially mitigated (e.g., variability could be reduced by automating data collection, standardizing the setting and time for data collection). However, there may be instances when there is too much variability during baseline conditions, and thus, detecting a treatment effect will not be feasible. There are no absolute standards to define what “too much” variability means [27].
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