Table Of Content
Apart from discussing the potential advantages provided by each of these data analytic techniques, barriers to applying them are reduced by disseminating open access software to quantify or graph data from ATDs. When target responses that were not mastered in control and LTM prompting were reassigned to be taught using MTL prompting, the targets previously assigned to the control condition were mastered for two of three participants. No participants mastered the targets that had been previously assigned to the LTM condition. These outcomes are surprising because correct responding was under extinction for the control condition and correct responses were reinforced during the LTM condition. One would expect that responses exposed to extinction would take longer to recondition as compared to previously reinforced responses (Bouton & Swartzentruber 1989). The literature on the effects of instructional history on response acquisition might help clarify these outcomes.
Descriptive Data Analytic Techniques
At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviours they exhibited toward their peers. (The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. They found that the number of aggressive behaviours exhibited by each student dropped shortly after the program was implemented at his or her school. But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results.
The Role of SSEDs in Evidence-Based Practice
MAE, standing for mean absolute error (also called “mean absolute deviation”) is the average of these horizontal (left panel) or vertical (right panel) distances. Therefore, the longer these horizontal or vertical lines, the larger the value of MAE (mean absolute error) and, thus, the lower the consistency within each condition. Children with autism spectrum disorders (ASDs) often require prompts to learn new behaviors and prompt-fading strategies to transfer stimulus control from the prompt to the naturally occurring discriminative stimuli. Two of the most commonly used prompt-fading procedures are most-to-least (MTL) and least-to-most (LTM) prompting (Libby et al., 2008). These procedures employ the same prompt topographies, including verbal, gestural, and physical prompts; however, they differ in the order in which the prompts are presented.
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Figure 8 shows one participant's correct responses during sessions across baseline phases, alternating treatments phases, and extended treatment phases. Thus, it is important to label the type of ATD correctly so applied researchers can analyze the data properly and readers can easily understand (and be able to replicate) the analyses performed. When block randomization of conditions is used, the comparisons to be performed between adjacent conditions are more straightforward because the presence of blocks makes it easier to apply ADISO and it enables using only actually obtained measurements without the need to interpolate as in ALIV. Moreover, the alternation sequences that can possibly be generated using block randomization are not the same as the ones that can arise when using an ATD with restricted randomization.
Coon and Miguel (2012) found that the procedure that had been used before is likely to result in more efficient acquisition when compared to a never-before-experienced teaching procedure. In contrast, Finkel and Williams (2001) found that textual prompts were effective at teaching intraverbals to one child with ASD, but echoic prompts were not. The authors speculated that the participants may have attended more to the textual prompts because of a history of failure with echoic prompts. Thus, in one study, instructional history facilitated acquisition of new responses, while in another study, it appeared that instructional history may have interfered with acquisition of new responses.
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Review of Methods to Equate Target Sets in the Adapted Alternating Treatments Design
This article provides a comprehensive overview of SSEDs specific to evidence-based practice issues in CSD that, in turn, could be used to inform disciplinary research as well as clinical practice. 1“Single-case designs” (e.g., What Works Clearinghouse, 2020), “single-case experimental designs” (e.g., Smith, 2012), “single-case research designs” (e.g., Maggin et al., 2018), or “single-subject research designs” (e.g., Hammond & Gast, 2010) are terms often used interchangeably. Another possible term is “within-subject designs” (Greenwald, 1976), referring to the fact that in most cases the comparison is performed within the same individual, although in a multiple-baseline design across participants there is also a comparison across participants (Ferron et al., 2014). In the top panel of Figure 10.5, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline.
There were few overlapping data points between the different criterion phases, and changes to the criterion usually resulted in immediate increases in the target behavior. These results would have been further strengthened by the inclusion of bidirectional changes, or mini-reversals, to the criterion (Kazdin, 2010). Such temporary changes in the level of the dependent measure(s) in the direction opposite from that of the treatment effect enhance experimental control because they demonstrate that the dependent variable covaries with the independent variable.
Given that such sequences do not allow for a rapid alternation of conditions, other randomization techniques are more commonly used to select the ordering of conditions. A randomly determined sequence arising from an ATD with block randomization is equivalent to the N-of-1 trials used in the health sciences (Guyatt et al., 1990; Krone et al., 2020; Nikles & Mitchell, 2015), in which several random-order blocks are referred to as multiple crossovers. Another option is to use “random alternation with no more than two consecutive sessions in a single condition” (Wolery et al., 2018, p. 304). Such an ATD with restricted randomization could lead to a sequence such as ABBABAABAB or AABABBABBA, with the latter being impossible when using block randomization. An alternative procedure for determining the sequence is through counterbalancing (Barlow & Hayes, 1979; Kennedy, 2005), which is especially relevant if there are multiple conditions and participants. Counterbalancing enables different ordering of the conditions to be present for different participants.
Hair Dresser's Salon Talstrasse Zürich / Wülser Bechtel Architekten
The purpose of this article is to review the strategies and tactics of SSEDs and their application in speech-language pathology research. The closer that the dots are to the red horizontal line, the more similar the differences between conditions in each block. Thus, the differences are most similar (i.e., most consistent) for Ken and more variable (i.e., least consistent) for Ashley. All the procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment (assuming that the treatment does not create a permanent effect), it is much clearer that the treatment (and removal of the treatment) is the cause. For a clear example, interested readers are referred to Silberglitt and Gibbons’ (2005) documentation of a slope-standard approach to identifying, intervening, and monitoring reading fluency and at-risk students. Of course, the approach (relying on slope values from serially collected single-subject data) is not without its problems. Depending on the frequency and duration of data collection, the standard error of the estimate for slope values can vary widely (Christ, 2006), leading to interpretive problems for practice.
One of the main problems of SSEDs is that the evidence generated is not always included in meta-analyses. Alternatively, if studies based on SSEDs are used in meta-analysis, there is no agreement on the correct metric to estimate and quantify the effect size. In relation to randomization, Item 8 of the CENT guidelines require reporting “[w]hether the order of treatment periods was randomised, with rationale, and method used to generate allocation sequence. When applicable, type of randomisation; details of any restrictions (such as pairs, blocking)” (Vohra et al., 2015, p. 4). In the SCRIBE guidelines, Item 8 requires the authors to “[s]tate whether randomization was used, and if so, describe the randomization method and the elements of the study that were randomized” (Tate et al., 2016, p. 140). Quantifying the difference between the data paths entails using observed behavior via direct measurement and linearly interpolated values.
Experimental control is demonstrated when the effects of the intervention are repeatedly and reliably demonstrated within a single participant or across a small number of participants. The way in which the effects are replicated depends on the specific experimental design implemented. For many designs, each time the intervention is implemented (or withdrawn following an initial intervention phase), an opportunity to provide an instance of effect replication is created. Transparent reporting is necessary with regards to the design used to isolate the effects of the independent variable on the dependent variable that match SCRIBE guidelines for SCEDs (Tate et al., 2016) and CENT guidelines for N-of-1 trials from the health sciences (Vohra et al., 2015). To begin with, the name of the design should be correctly and consistently specified across studies, in order to be able to locate them and include them in systematic reviews and meta-analyses. Difficulties might arise because the same design is sometimes referred to using different names (e.g., as an ATD or a multielement design; Hammond & Gast, 2010; Wolery et al., 2018).
The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied (Fisch, 2001)[3]. (Note that averaging across participants is less common.) Another approach is to compute the percentage of nonoverlapping data (PND) for each participant (Scruggs & Mastropieri, 2001)[4]. This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%.
For example, having different experimenters conduct sessions in different conditions, or running different session conditions at different times of day, may influence the results beyond the effect of the independent variables specified. Therefore, all experimental procedures must be analyzed to ensure that all conditions are identical except for the variable(s) of interest. Presenting conditions in random order can help eliminate issues regarding temporal cycles of behavior as well as ensure that there are equal numbers of sessions for each condition. Numerous criteria have been developed to identify best educational and clinical practices that are supported by research in psychology, education, speech-language science, and related rehabilitation disciplines. Some of the guidelines include SSEDs as one experimental design that can help identify the effectiveness of specific treatments (e.g., Chambless et al., 1998; Horner et al., 2005; Yorkston et al., 2001). It is important not only to state how the alternation sequence was determined, but also to provide additional details.
The withdrawal design is one option for answering research questions regarding the effects of a single intervention or independent variable. Like the AB design, the ABA design begins with a baseline phase (A), followed by an intervention phase (B). However, the ABA design provides an additional opportunity to demonstrate the effects of the manipulation of the independent variable by withdrawing the intervention during a second “A” phase. A further extension of this design is the ABAB design, in which the intervention is re-implemented in a second “B” phase. ABAB designs have the benefit of an additional demonstration of experimental control with the reimplementation of the intervention. Additionally, many clinicians/educators prefer the ABAB design because the investigation ends with a treatment phase rather than the absence of an intervention.
In contrast, a major assumption of the changing-criterion is that the dependent variable can be increased or decreased incrementally with stepwise changes to the dependent variable. Typically, this is achieved by arranging a consequence (e.g., reinforcement) contingent on the participant meeting the predefined criterion. The changing-criterion design can be considered a special variation of multiple-baseline designs in that each phase serves as a baseline for the subsequent one (Hartmann & Hall, 1976).
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