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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). During the baseline phase, performance in the dependent measure is highly variable, with a minimum of 0% and a maximum of 100%.
Basic Features and Components of Single-Subject Experimental Designs
Walking after incomplete spinal cord injury with an implanted neuromuscular electrical stimulation system and a hinged ... - Nature.com
Walking after incomplete spinal cord injury with an implanted neuromuscular electrical stimulation system and a hinged ....
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Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes. We demonstrate the capacity of our models by generating 4.8x the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence.
An Overview of Single-Subject Experimental Design
Most empirical research relies on using the scientific method to conduct large studies that use many participants in a control group and an experimental group. The purpose is to determine the effect of some experimental factor by introducing it to the experimental group, but not to the control group, and seeing what, if any, effect the experimental factor has. However, in some cases researchers will use an alternative method called single-subject research design. The AATD eliminates some of the concerns regarding multiple-treatment interference because different behaviors are exposed to different conditions. As in the multiple-baseline/multiple-probe designs, the possibility of generalization across behaviors must be considered, and steps should be taken to ensure the independence of the behaviors selected.
General Features of Single-Subject Designs
This might include applying the intervention to participants with more heterogeneous characteristics, conducting the intervention in a different setting with different dependent variables, and so forth. 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. Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? Notice that an AB design is essentially an interrupted time-series design applied to an individual participant.
Clinical Research Education
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. Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel. A subfield of psychology (behaviorism) that focuses exclusively on the effects of rewards, punishments, and other external factors on behavior. Figure 10.4 Results of a Generic Single-Subject Study Illustrating Level, Trend, and Latency. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom.
Multiple baseline
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). This notion has a relatively long history (Edgington, 1975) and continues to be mentioned in contemporary texts (Todman & Dugard, 2001). The advantages and disadvantages of the practice are worth addressing (albeit briefly).
Analysis of Effects in SSEDs
This was one of the first studies to show that attending to positive behavior—and ignoring negative behavior—could be a quick and effective way to deal with problem behavior in an applied setting. 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. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. 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.
Visual Data Inspection as a Data Reduction Strategy: Changes in Level, Trend, and Variability
If it is decided that, under some circumstances, it is scientifically sensible to use statistical analyses (e.g., t tests, analyses of variance [ANOVAs], etc.) as judgment aids for effect detection within single case data sets, the next question is a very practical one—can we? In other words, can parametric inferential statistical techniques be applied safely? In this context, the term safely refers to whether the outcome variables are sufficiently robust that they withstand violating the assumptions underlying the statistical test. The short answer seems to be “no,” with the qualifier “under almost all circumstances.” The key limitation and common criticism of generating statistics based on single-subject data is auto-correlation (any given data point is dependent or interacts with the data point preceding it). Because each data point is generated by the same person, the data points are not independent of one another (violating a core assumption of statistical analysis—technically, that the error terms are not independent of one another). Thus, performance represented in each data point may likely be influencing the next (Todman & Dugard, 2001).
Single-subject research, however, would likely reveal these individual differences. A second reason to focus intensively on individuals is that sometimes it is the behavior of a particular individual that is primarily of interest. A school psychologist, for example, might be interested in changing the behavior of a particular disruptive student.
The greater the percentage of non-overlapping data, the stronger the treatment effect. In a multiple-treatment reversal design, a baseline phase is followed by separate phases in which different treatments are introduced. 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 non-overlapping data (PND) for each participant (Scruggs & Mastropieri, 2001)[4]. In a multiple-treatment reversal design, a baseline phase is followed by separate phases in which different treatments are introduced.
The authors discuss the requirements of each design, followed by advantages and disadvantages. The logic and methods for evaluating effects in SSED are reviewed as well as contemporary issues regarding data analysis with SSED data sets. Specific exemplars of how SSEDs have been used in speech-language pathology research are provided throughout.
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). In addition, there is no known sampling distribution, making it impossible to derive a confidence interval (CI). CIs are important because they help create an interpretive context for the dependability of the effect by providing upper and lower bounds for the estimate. The goal of this tutorial is to familiarize readers with the logic of SSEDs and how they can be used to establish evidence-based practice.
Applied researchers, in particular, are interested in treatments that have substantial effects on important behaviors and that can be implemented reliably in the real-world contexts in which they occur. The study by Hall and his colleagues, for example, had good social validity because it showed strong and consistent effects of positive teacher attention on a behavior that is of obvious importance to teachers, parents, and students. Furthermore, the teachers found the treatment easy to implement, even in their often-chaotic elementary school classrooms. Furthermore, the teachers found the treatment easy to implement, even in their often chaotic elementary school classrooms.
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.
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