Alternating Treatment Design in ABA

Table Of Content

• Alternating treatments design: one strategy for comparing the effects of two treatments in a single subject.
• Alternating Treatments and Adapted Alternating Treatments Designs
• Analysis of Effects in SSEDs
• Illustrations and Comparison of the Results
• The influence of behavior preceding a reinforced response on behavior change in the classroom.
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The data paths are represented by lines connecting sessions within each condition of the ATD. Thus, visual analysis assesses the magnitude and consistency of the separation between conditions (Horner & Odom, 2014), also referred to as differentiation (Riley-Tillman et al., 2020) between the data paths (e.g., whether they cross or not and what is the vertical distance between them). This comparison usually incorporates consistency and level or magnitude of the difference in the dependent variable across the treatment conditions (Ledford et al., 2019). While Seaver and Bourret examined the use of extensive assessments on the same topography of a behavior chain (i.e., making Lego© patterns), the current study employed a briefer analysis of antecedent variables.

Alternating treatments design: one strategy for comparing the effects of two treatments in a single subject.

The dependent variable ranges between 10 and 15 units during the baseline, then has a sharp decrease to 7 units when treatment is introduced. However, the dependent variable increases to 12 units soon after the drop and ranges between 8 and 10 units until the end of the study. The dependent variable ranges between 12 and 16 units during the baseline, but drops down to 10 units with treatment and mostly decreases until the end of the study, ranging between 4 and 10 units. Speech volume during a token reinforcement intervention and follow-up using a changing-criterion design. From “A controlled single-case treatment of severe long-term selective mutism in a child with mental retardation,” by Facon, Sahiri, and Riviere, (2008), Behavior Therapy, 39, p. 313.

Alternating Treatments and Adapted Alternating Treatments Designs

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Analysis of Effects in SSEDs

This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see. One solution to these problems is to use a multiple-baseline design, which is represented in Figure 10.4. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one.

Number of phrases signed correctly during directed rehearsal, directed rehearsal with positive reinforcement, and control sessions using an adapted alternating treatments design. From “Acquisition and generalization of manual signs by hearing-impaired adults with mental retardation,” by Conaghan, Singh, Moe, Landrum, and Ellis, 1992, Journal of Behavioral Education, 2, p. 192. The results of this study (see Figure 7) demonstrated that the student produced a higher number of correct responses and engaged in fewer challenging behaviors when instruction was delivered in Spanish than in English. The superiority of the Spanish instruction was evident in this case because there was no overlap in correct responding or inappropriate behaviors between the English and Spanish conditions.

Randomization tests for restricted alternating treatments designs.

Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it. 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).

The influence of behavior preceding a reinforced response on behavior change in the classroom.

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Alternating treatment design (ATD) is a single-case experimental design (SCED1), characterized by a rapid and frequent alternation of conditions (Barlow & Hayes, 1979; Kratochwill & Levin, 1980) that can be used to compare two (or more) different treatments, or a control and a treatment condition. An ATD can be understood as a type of “multielement design” (see Hammond et al., 2013; Kennedy, 2005; Riley-Tillman et al., 2020; see Barlow & Hayes, 1979, for a discussion), but it important to mention two potential distinctions. On the one hand, the term “multilelement design” is employed when an ATD is used for test-control pairwise functional analysis methodology (Hagopian et al., 1997; Hall et al., 2020; Hammond et al., 2013; Iwata et al., 1994).

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In particular, we illustrate the use of several quantitative techniques as complements to (rather than substitutes for) visual analysis. The descriptive quantifications of differences in treatment effects and the inferential techniques (i.e., a randomization test) are applicable to both ATDs with block randomization and restricted randomization. However, the quantifications for assessing the consistency of effects across blocks are only applicable to ATDs with block randomization assignment for the conditions. The analytical options the current manuscript focuses on are scattered across several texts published since 2018.

The interventionists identified the six one-step directions that each participant responded to with the lowest accuracy, and within each category (i.e., fine-motor and gross-motor movements), randomly assigned them to experimental conditions (MTL, LTM, and control, see Table ​Table2).2). There was one exception to this rule; in error, two gross-motor responses were assigned to the MTL condition and two fine-motor responses were assigned to the LTM condition for James. The fact that the response effort for the MTL condition was higher did not affect its effectiveness and efficiency, as further described in the “Results” section.

The linearly interpolated values are the specific locations within a data path for one condition; they lie between session data points from that condition. The interpolated data points represent the value that hypothetically would have been obtained for a given condition if it had taken place on a given measurement occasion; however, in the ATD, the alternative treatment condition is imposed instead. Alternating Treatment Design is implemented by using multiple treatments in a systematic and alternating manner, collecting data on the child’s progress and responses to each treatment to inform decision-making. 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. In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed.

Moreover, the researcher is encouraged to use other data analytic outcomes besides the p-value because other sources of data analysis are not discarded or disregarded when interpreting a p-value. In terms of inferential quantifications, confidence intervals are important for informing about the precision of estimates (Wilkinson & The Task Force on Statistical Inference, 1999) and they can be constructed based on randomization test inversion (Michiels et al., 2017). The visual representation of the data should always be inspected, and the individual values can be analyzed. The researchers can, and must, still seek the possible causes of specific outlier measurements according to their knowledge about the client, the context, and the target behavior.

Second, Joseph demonstrated a decrease in accuracy of responding during maintenance probes. It is important to identify not only optimal teaching procedure but also procedures that lead to better maintenance of responding as well. Third, the measurement of efficiency employed in this study was sessions to criterion, while previous studies reported a broader set of measures, such as trials to criterion, sessions to criterion, and number of errors (e.g., Libby et al. 2008).

For instance, the sequence could be ABBABAAB for participant 1 and BAABABBA for participant 2. In relation to the previously mentioned distinguishing features of ATDs, it is important to adequately identify under what conditions this design is most useful and should be recommended to applied researchers. ATDs are applicable to reversible behaviors (Wolery et al., 2018) that are sensitive to interventions that can be introduced and removed fast, prior to maintenance and generalization phases of treatment analyses.