Because single-subject designs implement conditions across time, they are susceptible to some important limitations including sequence, maturation, and exposure effects. The need to consider within-condition stability, serial dependence in data sets, reversibility, carryover effects, and long experimental time courses can also complicate these designs. Still, manipulations common in neuroscience research is often amenable to these challenges (Soto, 2020). Single-subject designs for phenomena that are not reversable (such as skill acquisition) can also be studied using approaches such as the within-subject multiple baseline. Multiple baselines experiments across behaviors, across cell populations, or across homotopic brain regions may be reasonable if independence can be established (Soto, 2020). Note also that lost in the above discussion concerning effect size metrics is the issue of statistical versus clinical significance.
Reversal or A-B-A
Ideally, several studies using a variety of methodologies will be conducted to establish an intervention as evidence-based practice. When a treatment is established as evidence based using RCTs, it is often interpreted as meaning that the intervention is effective with most or all individuals who participated. Unfortunately, this may not be the case (i.e., there are responders and nonresponders). Thus, systematic evaluation of the effects of a treatment at an individual level may be needed, especially within the context of educational or clinical practice.
2: Single-Subject Research Designs
Lang and colleagues (2011) used an ATD to examine the effects of language of instruction on correct responding and inappropriate behavior (tongue clicks) with a student with autism from a Spanish-speaking family. To ensure that the conditions were equivalent, all aspects of the teaching sessions except for the independent variable (language of instruction) were held constant. Specifically, the same teacher, materials, task demands, reinforcers, and reinforcer schedules were used in both the English and Spanish sessions.
Alternating Treatments and Adapted Alternating Treatments Designs
“It would be wrong to assume that one must stay with a research programme until it has exhausted all its heuristic power, that one must not introduce a rival programme before everybody agrees that the point of degeneration has probably been reached” (Lakatos, 1978). Overall, ABAB designs are one of the most straightforward and strongest SSED “treatment effect demonstration” strategies. Ethical considerations regarding the withdrawal of the intervention and the reversibility of the behavior need to be taken into account before the study begins. Further extensions of the ABAB design logic to comparisons between two or more interventions are discussed later in this article.
Chapter 12: Single-Subject Designs
One argument against the exclusive reliance on visual inspection is that it is prone to Type 1 errors (inferring an effect when there is none), particularly if the effects are small to medium (Franklin, Gorman, Beasley, & Allison, 1996; Todman & Dugard, 2001). Evidence for experimental control is not always as compelling from a visual analysis perspective. In many cases, the clinical significance of behavior change between conditions is less clear and, therefore, is open to interpretation. RCTs do have many specific advantages related to understanding causal relations by addressing methodological issues that may compromise the internal validity of research studies.
They found that the number of aggressive behaviours exhibited by each student dropped shortly after the program was implemented at his or her school. Group research, on the other hand, is good for testing the effectiveness of treatments at the group level. Among the advantages of this approach is that it allows researchers to detect weak effects, which can be of interest for many reasons. For example, finding a weak treatment effect might lead to refinements of the treatment that eventually produce a larger and more meaningful effect. Group research is also good for studying interactions between treatments and participant characteristics. For example, if a treatment is effective for those who are high in motivation to change and ineffective for those who are low in motivation to change, then a group design can detect this much more efficiently than a single-subject design.
The Use of Self-Report Data in Psychology - Verywell Mind
The Use of Self-Report Data in Psychology.
Posted: Fri, 14 Apr 2023 07:00:00 GMT [source]
Single-subject experimental designs (SSEDs) represent an important tool in the development and implementation of evidence-based practice in communication sciences and disorders. The purpose of this article is to review the strategies and tactics of SSEDs and their application in speech-language pathology research. A type of quantitative research that involves studying in detail the behavior of each of a small number of participants. An AB design is a two-part or phase design composed of a baseline ("A" phase) with no changes and a treatment or intervention ("B") phase.[4][5] If there is a change then the treatment may be said to have had an effect. However, it is subject to many possible competing hypotheses, making strong conclusions difficult. Variants on the AB design introduce ways to control for the competing hypotheses to allow for stronger conclusions.
Data 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. If the multiple baselines are being conducted across behaviors, those behaviors must be similar in function, topography, and the effort required to produce them while remaining independent of one another. The withdrawal design is one option for answering research questions regarding the effects of a single intervention or independent variable.
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There may be a period of adjustment to the treatment during which the behaviour of interest becomes more variable and begins to increase or decrease. Similar to withdrawal and multiple-baseline/multiple-probe designs, changing-criterion designs are appropriate for answering questions regarding the effects of a single intervention or independent variable on one or more dependent variables. In the previous designs, however, the assumption is that manipulating the independent variable will result in large, immediate changes to the dependent variable(s). 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.
Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 10.6, however, the changes in level are fairly small. 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.
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. For this study, the maintenance of the behavior after the intervention was withdrawn supports its long-term effectiveness without undermining the experimental control. By replicating an investigation across different participants, or different types of participants, researchers and clinicians can examine the generality of the treatment effects and thus potentially enhance external validity.
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