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Single Case Study Research Design Example – Before looking at individual research designs, it is useful to consider some features that are common to most of them. Many of these features are illustrated in Figure 10.3, “Results of a Single-Subject Collaborative Study Illustrating Several Principles of a Single-Subject Study,” which shows the results of a single-subject collaborative study. First, the dependent variable (promoted
Shaft) at regular intervals. Second, the study is divided into separate phases, and the participant is tested with one condition per phase. Terms are often capitalized: A, B, C, etc. Figure 10.3 “Results of a joint single-subject study illustrating several principles of single-subject research” represents a design in which a participant was first tested in one condition (A), then tested in another condition (B), and finally tested again in the original condition. condition (A). (This is called reverse engineering and will be discussed in more detail shortly.)
Single Case Study Research Design Example
Another important aspect of a study is that the change from one condition to another usually does not occur after a certain time or number of observations. Rather, it depends on the behavior of the participant. More specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes called a steady-state strategy in single-subject research, which allows behavior to become fairly consistent from observation to observation before conditions change. This makes it easier to detect treatment effects. (Sidman, 1960). Sidman, M. (1960). Tactics of scientific inquiry: Evaluating experimental knowledge in psychology. Boston, MA: Authors Cooperative. The idea is that once the dependent variable reaches a steady state, any change in conditions is relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of the independent variable is easier to detect when the “noise” in the data is minimized.
Research Design Qualitative, Quantitative, And Mixed Methods Approaches (john W. Creswell J. David Creswell)
The final single-subject study design is a reverse study design, where the single-subject study design starts with a no-treatment baseline, then starts with treatment, and then returns to baseline. May include additional treatment conditions and return to baseline. , also called the ABA model. The simplest inverse model with baseline (A) followed by treatment (B) followed by return to baseline (A). . During the first phase A, baseline A in a single-subject study design in which the dependent variable is continuously measured without treatment. Most models start in basic mode, and many return to basic mode at least once. is assigned to the dependent variable. This is the level of response before any treatment is started, and therefore the baseline phase is a kind of control condition. Once a steady-state response is achieved, phase B begins when the investigator introduces the treatment. There may be an adjustment period to treatment, during which the behavior of interest becomes variable and begins to increase or decrease. Again, the researcher waits until this dependent variable reaches a steady state to see if it has changed and by how much. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This baseline plan can also be continued by reintroducing treatment (ABAB), returning to another baseline (ABABA) and so on.
The study by Hall and colleagues was an ABAB reversal design. Figure 10.4 “Evaluation of Results for Robbie, Hall’s Participant, and Colleagues in Their ABAB Reversal Design” approximates Robbie’s data. The percentage of time he spent studying (the dependent variable) was low during the first baseline, increased during the first treatment until leveling off, decreased during the second baseline, and increased again during the second treatment.
Figure 10.4.
Why is a change – removal of treatment – considered necessary in this type of design? Why use, for example, the ABA model instead of a simple AB model? Note that the AB model is essentially an interrupted time series model applied to a single participant. Remember, one problem with that design is that if the dependent variable changes after the treatment is introduced, it’s not always clear that the treatment is responsible for the change. It is possible that something else has changed at the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes
Using Single‐case Research Designs To Examine The Effects Of Interventions In Special Education
By removing the treatment, it is much clearer that the treatment (and the removal of the treatment) is the cause. In other words, translation greatly increases the internal validity of the study.
Next relatives of the basic return plan allow more than one treatment to be evaluated. In the reverse multiple-treatment design, a single-subject research design in which phases introducing different treatments are alternated. , the basic phase is followed by special phases in which different treatments are introduced. For example, a researcher might determine a baseline of a student’s disruptive behavior (A), then introduce a treatment that includes positive attention from the teacher (B), then transition to a treatment that includes mild punishment for neglecting learning (C) . The participant can then be returned to baseline before starting each treatment again—perhaps in reverse order as a way to control for carryover effects. This particular multiple treatment recovery plan can also be called the ABCACB model.
In Alternate Treatment Plan A single-subject study plan in which several treatments are rapidly alternated on a regular schedule. , two or more treatments alternate relatively quickly on a regular schedule. For example, positive learning attention could be used one day and mild punishment for not learning the next day, and so on. Or one treatment can be carried out in the morning and another in the afternoon. Alternative treatment can be a quick and effective way to compare treatments, but only when the treatments are fast-acting.
There are two potential problems with the reverse design—both related to treatment removal. One is that if the treatment works, removing it may be unethical. For example, if a treatment appears to reduce the incidence of self-injury in a child with an intellectual disability, it would be unethical to withdraw that treatment only to show that the incidence of self-injury increases. Another problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive learning attention is removed, a student may continue to learn at a faster rate. It could mean that the positive attention had a lasting impact on the student’s studies, which would certainly be good. But it could also mean that the positive attention isn’t actually the reason for the increased research. Perhaps something else happened at the same time as the treatment—for example, the student’s parents began rewarding him for good grades.
Case Study Research Design Examples
One solution to these problems is to use a multiple baseline design. A single-subject study design in which multiple baselines are specified for different participants, different dependent variables, or different contexts, and the treatment is introduced at a different time for each baseline. . , which is shown in Figure 10.5 “Results of a general multi-based study”. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced to each. Basically, every participant is tested on the AB plan. The key to this design is that the treatment is delivered differently
For each participant. The idea is that if the dependent variable changes when a treatment is introduced to a participant, it can be due to chance. But if the dependent variable changes when the treatment is introduced to more participants—especially when the treatment is started at different times for different participants—then it is highly unlikely that this is due to chance.
Multiple baselines can be applied for different participants, dependent variables, or settings. Treatment starts at a different time at each entry level.
As an example, take the research of Scott Ross and Robert Horner (Ross and Horner, 2009). Ross, S.W., & Horner, R.H. (2009). Bullying prevention through positive behavior support. Journal of Applied Behavior Analysis, 42, 747-759. They were interested in how a school-wide bullying prevention program affected the bullying of troubled students. In each of three different schools, the researchers studied two students who were regularly bullied. During the baseline phase, they observed the students for 10 minutes each day during their lunch break and counted the amount of aggressive behavior they displayed toward their peers. (The researchers used handheld computers to record the data.) After two weeks, they deployed
Single System Research Designs (ssrd) Notes
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