Tuesday, April 30, 2024

10 1 Overview of Single-Subject Research Research Methods in Psychology

single subject research design

This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. There may be a period of adjustment to the treatment during which the behavior of interest becomes more variable and begins to increase or decrease. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state.

Multiple-Baseline Designs

There are controls that need to be implemented, and a case study does not equate to a single-subject experimental design. We saw, I think, a nice revolution in terms of attention to these types of designs, giving credit to the type of data that could be obtained from these types of designs, and a flourishing of these designs really through the 1980s into the 1990s and into the 2000s. But I think — I’ve talked with other single-subject design investigators, and now we’re seeing maybe a little bit of a lapse of attention, and a lack of training again among our young folks. Also with the disorders that we deal with, it’s very hard to get the number of participants that we would need for the gold standard randomized controlled trial. Using single-subject designs works around the possible limiting factor of not having enough subjects in a particular area of study. The essence of single-subject design is using repeated measurements to really understand an individual’s variability, so that we can use our understanding of that variability to determine what the effects of our treatment are.

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2 Single-Subject Research Designs

(The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at his or her school. But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results. Researcher Vance Hall and his colleagues were faced with the challenge of increasing the extent to which six disruptive elementary school students stayed focused on their schoolwork (Hall, Lund, & Jackson, 1968).Hall, R. For each of several days, the researchers carefully recorded whether or not each student was doing schoolwork every 10 seconds during a 30-minute period. The treatment was that when the student was doing schoolwork, the teacher gave him or her positive attention in the form of a comment like “good work” or a pat on the shoulder.

Alternating Treatments and Adapted Alternating Treatments Designs

First she would need to establish a baseline of how frequently the disruptions occurred. In the example below, the target student was disruptive seven times on the first day, six times on the second day, and seven times on the third day. Note how the sequence of time is depicted on the x-axis (horizontal axis) and the dependent variable (outcome variable) is depicted on the y-axis (vertical axis). Horner and colleagues report that more than forty-five scholarly journals accept and publish single-subject research studies (Horner, et al. 2005). See also Campbell and Stanley 1963, Kazdin and Tuma 1982, and Kratochwill and Levin 1992. In the B phase, the same behavior of the individual is counted over time under experimental conditions while treatment is administered.

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I. Chapter 1: The Science of Psychology

It should be noted, however, that randomization tests in and of themselves do not necessarily address the problem of autocorrelation. Number of correct responses and tongue clicks during discrete trial training sessions in Spanish (Sp.) and English (Eng.) using an alternating treatments design. From “Effects of language instruction on response accuracy and challenging behavior in a child with autism,” by Lang et al., 2011, Journal of Behavioral Education, 20, p. 256. 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.

We use this method rather than counting how many times someone is off task because one could continually be off task and that would only be a count of 1 since the person was continually off task. Someone who might be off task twice for 15 second would be off task twice for a score of 2. However, the second person is certainly not off task twice as much as the first person. Therefore, recording whether the person is off task at 10-second intervals gives a more accurate picture.

single subject research design

Abstract

Group data are described using statistics such as means, standard deviations, Pearson’s r, and so on to detect general patterns. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Single-subject designs compare experimental to control conditions repeatedly over time within the same individual.

The group analysis obscured these individual variations.2 These results could mean that there is no consistent effect, but they may also point to specific subject characteristics that account for the differences. The scientific method is neither singular nor fixed; it is an evolving, plural set of processes. It develops and improves through time as methodology rises to meet new challenges (Lakatos, 1978; Hull, 1988; Kuhn and Hacking, 2012).

Multiple-Baseline Design Across Participants

If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trendOne factor that is considered in the visual inspection of single-subject data. An increase or decrease in the independent variable over several observations., which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect.

Thus, when multiple baselines are conducted across participants, one or more individuals may wait some time before receiving a potentially beneficial intervention. In some cases, the simultaneous and continuous data collection in all legs of multiple-baseline designs is not feasible or necessary. Multiple-probe designs are a common variation on multiple baselines in which continuous baseline assessment is replaced by intermittent probes to document performance in each of the conditions during baseline. Probes reduce the burden of data collection because they remove the need for continuous collection in all phases simultaneously (see Horner & Baer, 1978, for a full description of multiple-probe designs). Pre-intervention probes in Condition 1 are obtained continuously until a stable pattern of performance is established. Meanwhile, single data collection sessions would be conducted in each of the other conditions to assess pre-intervention levels.

The use of single-subject experimental designs (SSEDs) has a rich history in communication sciences and disorders (CSD) research. A number of important studies dating back to the 1960s and 1970s investigated fluency treatments using SSED approaches (e.g., Hanson, 1978; Haroldson, Martin, & Starr, 1968; Martin & Siegel, 1966; Reed & Godden, 1977). Despite their history of use within CSD, SSEDs are sometimes overlooked in contemporary discussions of evidence-based practice.

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.

Group experiments with between-group comparisons often assign these individuals to conditions (experimental or control) randomly. Other group experiments (such as a randomized block design) assign individuals to conditions systematically to explicitly balance the groups according to particular pre-considered individual factors. In both cases, the assumption is that if alternative variables influence the dependent variable (DV), they are unlikely to do so differentially across groups. Conventional approaches to single-subject data analysis rely on visual inspection (as reviewed earlier in this article). From the perspective of clinical significance, supporting a “visual inspection–only” approach is warranted because the practitioner (and, ultimately, the field of practice) is interested in identifying only those variables that lead to large, unambiguous changes in behavior.

This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on. One commonly used alternative to the ATD is called the adapted alternating treatments design (AATD; Sindelar, Rosenburg, & Wilson, 1985). Whereas the traditional ATD assesses the effects of different interventions or independent variables on a single outcome variable, in the AATD, a different set of responses is assigned to each intervention or independent variable.

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