As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009).Ross, S. They were interested in how a school-wide bullying prevention program affected the bullying behavior of particular problem students. 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 behaviors they exhibited toward their peers.
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Educator Preparation.
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Abstract
When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results. Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations. There has been a small but steady body of work addressing effect size calculation and interpretation for SSEDs. Space precludes an exhaustive review of all the metrics (for comprehensive reviews, see Parker & Hagan-Burke, 2007, and related papers from this group).
Multiple-Baseline Design Across Settings
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).
Experimental questions
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The best AI tools to power your academic research.
Posted: Sat, 20 Jan 2024 08:00:00 GMT [source]
There are, however, a number of points that can be made regarding the use (derivation, interpretation) of effect size indices that are common to all. The simplest and most common effect size metric is the percentage of nonoverlapping data (PND; Scruggs, Mastropieri, & Casto, 1987). It is easy to calculate by hand and, therefore, is easily accessible to practitioners. Then, the number of data points that fall above (or below) the line is tallied and divided by the total number of intervention data points. If, for example, in a study of a treatment designed to improve (i.e., increase) communication fluency, eight of 10 data points in the intervention phase are greater in value than the largest baseline data point value, the resulting PND would equal 80%.
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I have also created instructions for creating single-subject research design graphs with Excel. I also have additional information about how to record single-subject research data.
Direct replication refers to the application of an intervention to new participants under exactly, or nearly exactly, the same conditions as those included in the original study. This type of replication allows the researcher or clinician to determine whether the findings of the initial study were specific to the participant(s) who were involved. Systematic replication involves the repetition of the investigation while systematically varying one or more aspects of the original study.
Kazdin (2010), however, compellingly argued that certain characteristics of SSEDs make them an important addition and alternative to large-group designs. He argued that RCTs may not be feasible with many types of interventions, as resources for such large-scale studies may not be available to test the thousands of treatments likely in use in any given field. In addition, the carefully controlled conditions in which RCTs must be conducted to ensure that the results are interpretable may not be comparable and/or possible to implement in real-life (i.e., uncontrolled) conditions. SSEDs are an ideal tool for establishing the viability of treatments in real-life settings before attempts are made to implement them at the large scale needed for RCTs (i.e., scaling up).
The percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect. A baseline is established for several participants and the treatment is then introduced to each participant at a different time.
Multipleprobe
Autocorrelated data will, in turn, artificially inflate p values and affect Type 1 error rates. In ATDs, it is important that all potential “nuisance” variables be controlled or counterbalanced. 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. Although many behaviors would be expected to return to pre-intervention levels when the conditions change, others would not.
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. Unlike the Lang et al. (2011) study, the design used in this study met the WWCH standards. 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.
One solution to these problems is to use a multiple-baseline design, which is represented in Figure 10.3. There are three different types of multiple-baseline designs which we will now consider. 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.
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. Similarly, contexts or stimuli must be sufficiently dissimilar so as to minimize the likelihood of effect generalization.
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