Table Of Content
The variation inherent to systematic replication allows the researcher, educator, or clinician to determine the extent to which the findings will generalize across different types of participants, settings, or target behaviors. As noted by Johnston and Pennypacker (2009), conducting direct replications of an effect tells us about the certainty of our knowledge, whereas conducting systematic replications can expand the extent of our knowledge. When such changes are large and immediate, visual inspection is relatively straightforward, as in all three graphs in Figure 1. If only the average performance during each phase is considered, each of these graphs includes a between-phase change in level. On closer inspection, however, each presents a problem that threatens the internal validity of the experiment and the ability of the clinical researcher to make a warranted causal inference about the relation between treatment (the independent variable) and effect (the dependent variable).
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ATDs and AATDs can be useful in comparing the effects of two or more interventions or independent variables. Unlike multiple-treatment designs, these designs can allow multiple comparisons in relatively few sessions. The issues related to multiple-treatment interference are also relevant with the ATD because the dependent variable is exposed to each of the independent variables, thus making it impossible to disentangle their independent effects.
Illustrations and Comparison of the Results
For example, a researcher might establish a baseline of studying behaviour for a disruptive student (A), then introduce a treatment involving positive attention from the teacher (B), and then switch to a treatment involving mild punishment for not studying (C). The participant could then be returned to a baseline phase before reintroducing each treatment—perhaps in the reverse order as a way of controlling for carryover effects. This particular multiple-treatment reversal design could also be referred to as an ABCACB design.
Alternating Treatments and Adapted Alternating Treatments Designs
Percent of intervals with challenging behavior and mands during functional analysis, intervention demonstration, and component analysis. From “A component analysis of functional communication training across three topographies of severe behavior problems,” by Wacker et al., 1990, Journal of Applied Behavior Analysis, 23, p. 424. Please list any fees and grants from, employment by, consultancy for, shared ownership in or any close relationship with, at any time over the preceding 36 months, any organisation whose interests may be affected by the publication of the response.
Participants
As such, the interventionist replicated the target identification phase with new target responses. Sean’s target behaviors listed in Table Table22 represent targets identified during the second target identification assessment. All participants responded correctly following the presentation of a full physical prompt on 100 % of the trials. For all other prompt topographies, correct responding was lower across participants (range, 0 to 50 % correct). There are close relatives of the basic reversal design that allow for the evaluation of more than one treatment. In a multiple-treatment reversal design, a baseline phase is followed by separate phases in which different treatments are introduced.
Although visual analysis supported the inference that treatment effects were functionally related to the independent variable, the results of this study did not meet the design standards set out by the WWCH panel because the design consisted of only two treatments in comparison with each other. To meet the criterion of having at least three attempts to demonstrate an effect, studies using an ATD must include a direct comparison of three interventions, or two interventions compared with a baseline. To be considered as support for an evidence-based practice, this design would need to have incorporated a third intervention condition or to have begun with a baseline condition.
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Then, the prompt topography that was identified as being most efficient (i.e., task acquisition in the fewest training trials) for each participant was included in the assessment of prompt-fading procedures, which compared MTL, LTM, and progressive time delay procedures. The authors used these results to establish an efficiency ranking of the prompt-fading procedures for each participant. Next, the authors conducted a generality test to assess the comparative efficiency of prompt-fading procedures when used to teach functional domestic and vocational skills to the participants. The generality test confirmed that the prompt-fading procedure identified as most efficient in the prompt-fading assessments continued to promote the most efficient response acquisition across new skills.
Visual Data Inspection as a Data Reduction Strategy: Changes in Level, Trend, and Variability
If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behaviour is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is latency, which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible. In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home.
Independence means that changing behavior in one condition will not affect performance in the others. If the conditions are not independent, implementing the intervention in one condition may lead to changes in behavior in another condition while it remains in the baseline phase (McReynolds & Kearns, 1983). This makes it challenging (if not impossible) to demonstrate convincingly that the intervention is responsible for changes in the behavior across all the conditions. When implementing the intervention across individuals, it may be necessary—to avoid diffusion of the treatment—to ensure that the participants do not interact with one another. When the intervention is implemented across behaviors, the behaviors must be carefully selected to ensure that any learning that takes place in one will not transfer to the next.
As such, bidirectional changes are much less likely to be the result of extraneous factors. Nevertheless, the results did not show any evidence of noneffect, and the results would be considered strong evidence in favor of the intervention. In the present study, MTL prompting led to the quickest skill acquisition for all participants. This stands in contrast to previous studies (Glendenning et al. 1983; Libby et al. 2008; Seaver and Bourret 2014; Walls 1981), in which efficiency outcomes were inconsistent across participants. Since the participants in this study were all young learners (i.e., preschoolers), it is possible that the MTL procedure is generally more consistently effective and efficient with this population. As Green (2001) suggested, it is possible that young learners benefit more from more intrusive transfer-of-stimulus-control procedures, which might not necessarily be the case for older or more advanced learners.
In order to further study the results for Ashley, we quantify the degree of consistency for each condition in Fig. This figure represents a modified Brinley plot, constructed as described in Blampied (2017) with the additional graphical aids described Manolov and Tanious (2020). In particular, for ATDs, the coordinates of each data point are defined by a condition A value (X-axis) and the corresponding condition B value (Y-axis) from the same block of the ATD. The left panel focuses on the condition A measurements, represented in the X-axis, and it represents the distance between each condition A value and the condition A mean via the horizontal dashed lines. In complementary fashion, the right panel focuses on the condition B measurements, represented in the Y-axis, and it represents the distance between each condition B value and the condition B mean via the vertical dashed lines.
One of the great scientific strengths of SSEDs is the premium placed on internal validity and the reliance on effect replication within and across participants. One of the great clinical strengths of SSEDs is the ability to use a response-guided intervention approach such that phase or condition changes (i.e., changes in the independent variable) are made based on the behavior of the participant. This notion has a long legacy and reflects Skinner's (1948) early observation that the subject (“organism”) is always right. In contrast with these two strengths, there is a line of thinking that argues for incorporating randomization into SSEDs (Kratochwill & Levin, 2009).
The researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. The authors also mention that each participant reached the criterion of 90% accuracy for three consecutive sessions faster for the touch points program. The comparison excludes the first and last measurements for which only one of the data paths is present. There are multiple choices available for the design, demolition, and construction of your project.
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At the time of the study, the interventionist had 7 years of experience working with individuals with developmental disabilities using behavior analytic procedures and was a board-certified behavior analyst. A second observer collected data for interobserver agreement and treatment integrity purposes. The second observer had been trained to collect data by the interventionist prior to assisting with the current study. One of the tools used to help answer the question of “what works” that forms the basis for the evidence in evidence-based practice is meta-analysis—the quantitative synthesis of studies from which standardized and weighted effect sizes can be derived. Meta-analysis methodology provides an objective estimate of the magnitude of an intervention's effect.
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. At this point, it seems fair to conclude that it remains an open question about whether randomization is superior to replication with regard to producing clinically meaningful effects for any given participant in an SSED.
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