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One commonly used alternative to the ATD is called the adapted alternating treatments design (AATD; Sindelar, Rosenburg, & Wilson, 1985). Whereas the traditional ATD assesses the effects of different interventions or independent variables on a single outcome variable, in the AATD, a different set of responses is assigned to each intervention or independent variable. The resulting design is similar to a multiple-baseline, across-behaviors design with concurrent training for all behaviors. For example, Conaghan, Singh, Moe, Landrum, and Ellis (1992) assigned a different set of 10 phrases to each of three conditions (directed rehearsal, directed rehearsal plus positive reinforcement, and control). This strategy allowed the researchers to determine whether the acquisition of new signed phrases differed across the three conditions.
Analytical Implications for Randomization Tests
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Quantitative Techniques and Graphical Representations for Interpreting Results from Alternating Treatment Design
As a result, this case would be considered strong evidence supporting the effectiveness of the intervention based on the WWCH evidence-based practice criteria. This was because, in addition to meeting the minimum number of sessions per phase, it included a direct comparison between three conditions as well as a direct comparison with a baseline phase. The data from the baseline phase established that the participant did not respond correctly in the absence of the intervention.
A special latin square for the use of each subject “as his own control”
The key to this design is that the treatment is introduced at a different time for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is extremely unlikely to be a coincidence. Although the clinical/educational appeal of such a metric seems obvious (easy to calculate, it is consistent with visual inspection of graphic data), there are potential problems with the approach. For example, there are ceiling effects for PND, making comparisons across or between interventions difficult (Parker & Hagan-Burke, 2007; Parker et al., 2007), and PND is based on a single data point, making it sensitive to outliers (Riley-Tillman & Burns, 2009).
Illustrations and Comparison of the Results
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Each classroom was equipped with a computer, tables, chairs, and shelves to store materials. It was typical for participants to receive this kind of individualized instruction in their classrooms throughout the day. A baseline phase is followed by separate phases in which different treatments are introduced. The level of responding before any treatment is introduced and therefore acts as a kind of control condition. Overall, ABAB designs are one of the most straightforward and strongest SSED “treatment effect demonstration” strategies.
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Once the participant reached the mastery criterion in a condition, maintenance probes were conducted. One maintenance probe was conducted for every five training sessions in the other two conditions. Maintenance probes were conducted the same as baseline, with the exception that correct independent responses were reinforced.
Review of Methods to Equate Target Sets in the Adapted Alternating Treatments Design
The fact that no change in responding was observed in the control condition, however, is evidence that the changes were due to the intervention rather than a result of some factor outside of the study. As further demonstration of the experimental effect of directed rehearsal plus reinforcement, a final condition was implemented in which the treatment package was used to teach the phrases from the other two conditions. This condition further strengthened the evidence for the effectiveness of the intervention, as performance on all three words sets reached 100% by the end of the phase. In sum, the latency to change observed during the alternating treatments phase meant that this study merits a rating of moderate evidence in favor of the intervention.
Autocorrelated data will, in turn, artificially inflate p values and affect Type 1 error rates. The issues are germane because of the WWCH and related efforts to establish standard approaches for evaluating SSED data sets as well as the problem of whether and how to derive standardized effect sizes from SSED data sets for inclusion in quantitative syntheses (i.e., meta-analysis). Although an assumption of independence suggests that researchers should select conditions that are clearly dissimilar from one another, the conditions must be similar enough that the effects of the independent variable can be replicated across each of them. If the multiple baselines are conducted across participants, this means that all the participants must be comparable in their behaviors and other characteristics.
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Finally, the low degree of superiority for retention for Participant 1008 is well-aligned with the results about the consistency of the effect. Fig.9,9, it can be seen that the differences between conditions in each block are relatively far away from the overall mean difference. That is, the vertical distance between the dots and the dashed diagonal line is relatively large, compared to the mean difference. Fig.9,9, the typical distance between the overall mean difference and the difference between conditions within each block is more than three times (342%) of the overall mean difference.
Specific operational definitions for each response targeted in the study are provided in Table Table1.1. A correct independent response was defined as performing the specified response within 2 s of the presentation of the verbal discriminative stimulus (e.g., clapping when the interventionist said “Clap!”), in the absence of prompts. An incorrect response was defined as the failure to perform a response within 2 s of the presentation of the discriminative stimulus or performing an action that was different from the one specified by the interventionist (e.g., shaking the head when the interventionist said “Clap!”). In ATDs, it is important that all potential “nuisance” variables be controlled or counterbalanced.
The effect of an independent variable is easier to detect when the “noise” in the data is minimized. Percentage of unprompted questions asked by three participants in baseline, intervention, and generalization sessions using a multiple-baseline, across-participants design. From “Question-asking and collateral language acquisition in children with autism,” by Koegel, Koegel, Green-Hopkins, and Barnes (2010), Journal of Autism and Developmental Disorders, 40, p. 512. In Panel A of Figure 2, no change is observed until the third session of the intervention phase.
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