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Including a larger number of measurements can increase internal validity when the outcomes are congruent or can suggest new avenues of research when they are not, as was the case in Libby et al. (2008). Fourth, the prompt topography assessment identified stimuli that were potent enough to evoke correct responding. These stimuli were presented in isolation; it is possible that when presented in a hierarchy, such as from most intrusive to least intrusive (e.g., from full physical to gestural), transfer of stimulus control is achieved. Last, such a formal prompt topography assessment might not be needed in clinical settings, if program development is designed by a clinician who is familiar with the learner’s skills and deficits. For example, if the clinician observes that the learner is typically not able to respond correctly to model prompts, she might use other stimulus topographies as prompts and set a separate goal for physical imitation, so that the learner ultimately can respond to imitative prompts as well. A potential limitation of randomization tests is that some applied researchers may not be familiar with the correct interpretation of its p-value, but this could also be applicable to other data analytical techniques suggested in the SCED context.
Abstract
It is worth noting that although they are often used interchangeably in the literature, the terms withdrawal design and reversal design refer to two related but distinctly different research designs. In the withdrawal design, the third phase represents a change back to pre-intervention conditions or the withdrawal of the intervention. In contrast, the reversal design requires the active reversal of the intervention conditions. For example, reinforcement is provided contingent on the occurrence of a response incompatible with the response reinforced during the intervention (B) phases (see Barlow, Nock, & Hersen, 2009, for a complete discussion of the mechanics and relative advantages of reversal designs).
Desirable Qualities of Baseline Data
There is no specific limitation that the use of randomization of condition ordering entails because it is also possible to combine randomization and counterbalancing (e.g., see Edgington & Onghena, 2007, ch. 6). This could occur, for instance, when determining the sequence at random for participant 1 (e.g., ABABBAAB) and counterbalancing for participant 2 (i.e., BABAABBA). In ATD research, the measures of variability within a condition commonly reported are the (1) range and (2) standard deviation (Manolov & Onghena, 2018). Beyond reporting these values, the “visual aid and objective rule” (VAIOR, Manolov & Vannest, 2019) also includes the degree of variability within conditions. VAIOR assesses whether the data from one condition are superior to the data from the other condition, with the latter being summarized by a trend line and a variability band. The trend line is fitted by applying the Theil-Sen method (Vannest et al., 2012) to the data obtained in one condition (usually the baseline condition or another reference condition).
Analyzing Data From Single-Case Alternating Treatments Designs
The data from the final phase of the study depicted in Figure 4 are worth noting because they show the continued performance of the dependent variable in the absence of the treatment. In some ways, this is akin to a return to baseline conditions, as in the second “A” condition of a withdrawal design. In this case, however, the behavior does not return to pre-intervention levels, suggesting that the behavior is nonreversible and that using a reversal design to demonstrate the effects of the intervention would have been inappropriate.
On the other hand, a multielement design can be used for assessing contextual variables and ATD for assessing interventions (Ledford et al., 2019). Previous publications on best practices for applying ATD recommend a minimum of five data points per condition, and limiting consecutive repeated exposure to two sessions of any one condition (What Works Clearinghouse, 2020; Wolery al., 2018). The rapid alternation between conditions distinguishes ATDs from other SCEDs, which are characterized by more consecutive repeated measurements for the same condition (Onghena & Edgington, 2005). An adapted alternating treatment design (Sindelar et al. 1985) was used to evaluate the effects of three conditions (i.e., MTL, LTM, and control) on each participant’s acquisition of one-step directions. The order of presentation of the three levels of the independent variable during the alternating treatment analysis was randomized using a random number generator software.
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Ki Se Tsu Hair Salon / iks design
What changes in each possible alternation sequence, from which the actual alternation sequence was selected at random, are the labels, which denote the treatment conditions. Thus, when constructing the randomization distribution, other possible orderings/labels such ABABAB and ABABBA are assigned to each measurement in its original sequence (6, 8, 9, 7, 5, 7) and the test statistic is computed according to these labels. If an increase in the target behavior is desired, the p-value is the proportion of pseudostatistics as large as or larger than the actual test statistic. As an alternative, if a decrease is the aim of the intervention, the p-value is the proportion of pseudostatistics as small as or smaller than the actual test statistic. During baseline, for most sessions, there were no programmed consequences for responding, whereas in one session, programmed consequences were provided (i.e., James’ first baseline session). There were other deviations from the procedures, such as assigning two gross-motor movements to James’ MTL condition and two fine-motor movements to his LTM condition; however, the reliability of the findings across participants suggests that these integrity issues had little or no effect.
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Therefore, overall, the MTL procedure was more effective than the LTM procedure in this experiment. The LTM procedure was more efficient for the three participants who met a criterion with this procedure when considering trials to criterion; however, the MTL procedure was more efficient for all participants when considering errors to criterion. The authors concluded that the MTL procedure might have delayed learning, but it resulted in fewer errors. Green (2001) stated that minimizing the number of errors is crucial for avoiding the development of faulty stimulus control.
Hair Salons and Barbershops: Examples in Plan and Section
A major drawback, however, is that the logic of SSEDs allows only for the comparison of adjacent conditions. This restriction helps to minimize threats to internal validity, such as maturation, that can lead to gradual changes in behavior over time, independent of study conditions. As a result, it is not appropriate to comment on the relative effects of the interventions (i.e., the “B” and “C” phases) in an ABACAC study because they never occur next to one another. Rather, one can only conclude that one, both, or neither intervention is effective relative to baseline. On the other hand, beginning with a full reversal or withdrawal design (ABAB), with it followed by a demonstration of the effects of the second intervention (CAC, resulting in ABABCAC), allows for the direct comparison of the two interventions. The BC comparison, however, is never repeated in this sequence, limiting the internal validity of the comparison.
In Fletcher et al. (2010), a comparison was performed between TOUCHMATH, a multisensory mathematics program and a number line, for three middle-school students (Ashley, Robert, and Ken) with moderate and multiple disabilities in the context of solving single-digit mathematics problems. The data for the comparison phase in which the two interventions are alternated are presented in Fig. A comparison involving actual and linearly interpolated values (abbreviated as ALIV, Manolov & Onghena, 2018) assesses the magnitude of effect, by focusing on the average distance between the data paths. Complementary to the visual structured criterion, ALIV quantifies the magnitude of the separation between data paths. By comparing the outcomes of each treatment, Charlie’s therapist identifies that the combination of visual schedules and social stories is the most effective approach for him.
In the current climate of evidence-based practice, the tools provided by SSEDs are relevant for researchers and practitioners alike. The potential strength of the internal validity of SSEDs allows researchers, clinicians, and educators to ask questions that might not be feasible or possible to answer with traditional group designs. Because of these strengths, both clinicians and researchers should be familiar with the application, interpretation, and relationship between SSEDs and evidence-based practice. One approach to comparing two or more data paths is to use the visual structured criterion (VSC; Lanovaz et al., 2019).
The WWCH panel recommended that an intervention have a minimum of five supporting SSED studies meeting the evidence standards if the studies are to be combined into a single summary rating of the intervention's effectiveness. In order to provide a more in-depth analysis of the statistically significant result obtained via a randomization test, as reported by the original authors, we compared several different types of quantitative analyses to see if they would yield similar conclusion. For instance, the application of VAIOR (Fig. (Fig.6,6, left panel) indicates that 43% (3 of 7) of the measurements in the condition without ultrasound are outside the variability band constructed around the trend line for this condition. According to the VAIOR criterion for sufficient change, requiring for doubling this percentage (Manolov & Vannest, 2019), at least 86% of the measurements of the condition with ultrasound should be outside the upper limit of the variability band. Other analytical methods, such as visual analysis and clinical significance measures, as well as assessment of social validity should be considered as well. However, the p-value based on the presence of randomization in the design is an objective quantification, which is valid thanks to the use of the randomization of condition ordering as it was actually implemented during the study.
The prompt topography that resulted in the highest level of responding was included in the development of prompt hierarchies for LTM and MTL prompting. Thus far, the designs that we have described are only appropriate to answer questions regarding the effects of a single intervention or variable. In many cases, however, investigators—whether they are researchers, educators, or clinicians—are interested in not only whether an intervention works but also whether it works better than an alternative intervention. One strategy for comparing the effects of two interventions is to simply extend the logic of withdrawal designs to include more phases and more conditions. The most straightforward design of this type is the ABACAC design, which begins with an ABA design and is followed by a CAC design.
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