As we have seen throughout the book, group research involves combining data across participants. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population.
Single-subject research, by contrast, relies heavily on a very different approach called visual inspection The primary approach to data analysis in single-subject research, which involves graphing the data and making a judgment as to whether and to what extent the independent variable affected the dependent variable. Inferential statistics are typically not used. In visually inspecting their data, single-subject researchers take several factors into account.
One of them is changes in the level One factor that is considered in the visual inspection of single-subject data. The overall level of the dependent variable within a condition. 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 trend One factor that is considered in the visual inspection of single-subject data.
An increase or decrease in the independent variable over several observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behavior is increasing during baseline but then begins to decrease with the introduction of the treatment.
A third factor is latency One factor that is considered in the visual inspection of single-subject data. The time between the change in conditions and the change in the dependent variable. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.
In the top panel of Figure 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 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. 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.
Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel. The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. Fisch, G. Evaluating data from behavioral analysis: Visual inspection or statistical models. Behavioural Processes , 54 , — Note that averaging across participants is less common.
Another approach is to compute the percentage of nonoverlapping data A statistic sometimes used in single-subject research. The percentage of observations in a treatment condition that are more extreme than the most extreme observation in a relevant baseline condition.
Scruggs, T. How to summarize single-participant research: Ideas and applications. Exceptionality , 9 , — This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. The greater the percentage of nonoverlapping data, the stronger the treatment effect.
Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it. Practice: Design a simple single-subject study using either a reversal or multiple-baseline design to answer the following questions. Be sure to specify the treatment, operationally define the dependent variable, decide when and where the observations will be made, and so on.
Single-subject research is similar to group research—especially experimental group research—in many ways.
They are both quantitative approaches that try to establish causal relationships by manipulating an independent variable, measuring a dependent variable, and controlling extraneous variables. But there are important differences between these approaches too, and these differences sometimes lead to disagreements. It is worth addressing the most common points of disagreement between single-subject researchers and group researchers and how these disagreements can be resolved.
As we will see, single-subject research and group research are probably best conceptualized as complementary approaches. One set of disagreements revolves around the issue of data analysis.
McCleary mcclearydf sfasu. The key to this design is that the treatment is introduced at a different time for each participant. The school psychologist shall possess the knowledge and professional expertise to collaborate with families and school and community-based professionals in designing, implementing, and evaluating interventions that effectively respond to the educational and mental health needs of students. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population. Peterson, D. The independent school psychologist does not require supervision as described for the entry- level school psychologist.
Some advocates of group research worry that visual inspection is inadequate for deciding whether and to what extent a treatment has affected a dependent variable. One specific concern is that visual inspection is not sensitive enough to detect weak effects.
Danov, S. A survey evaluation of the reliability of visual inspection and functional analysis graphs. Behavior Modification , 32 , — A third is that the results of visual inspection—an overall judgment of whether or not a treatment was effective—cannot be clearly and efficiently summarized or compared across studies unlike the measures of relationship strength typically used in group research. In general, single-subject researchers share these concerns.
However, they also argue that their use of the steady state strategy, combined with their focus on strong and consistent effects, minimizes most of them. If the effect of a treatment is difficult to detect by visual inspection because the effect is weak or the data are noisy, then single-subject researchers look for ways to increase the strength of the effect or reduce the noise in the data by controlling extraneous variables e. If the effect is still difficult to detect, then they are likely to consider it neither strong enough nor consistent enough to be of further interest.
Turning the tables, some advocates of single-subject research worry about the way that group researchers analyze their data.
Specifically, they point out that focusing on group means can be highly misleading. Again, imagine that a treatment has a strong positive effect on half the people exposed to it and an equally strong negative effect on the other half. In a traditional between-subjects experiment, the positive effect on half the participants in the treatment condition would be statistically cancelled out by the negative effect on the other half. The mean for the treatment group would then be the same as the mean for the control group, making it seem as though the treatment had no effect when in fact it had a strong effect on every single participant!
But again, group researchers share this concern. Although they do focus on group statistics, they also emphasize the importance of examining distributions of individual scores. For example, if some participants were positively affected by a treatment and others negatively affected by it, this would produce a bimodal distribution of scores and could be detected by looking at a histogram of the data.
The use of within-subjects designs is another strategy that allows group researchers to observe effects at the individual level and even to specify what percentage of individuals exhibit strong, medium, weak, and even negative effects. The second issue about which single-subject and group researchers sometimes disagree has to do with external validity—the ability to generalize the results of a study beyond the people and situation actually studied. In particular, advocates of group research point out the difficulty in knowing whether results for just a few participants are likely to generalize to others in the population.
Evaluate the effects of your interventions! Single-Subject Designs for School Psychologists shows how practitioners―educators, counselors, and support. Single-Subject Designs for School Psychologists discusses: intervention evaluation and validation procedures the Timely Transitions Game¿combining explicit.
Imagine, for example, that in a single-subject study, a treatment has been shown to reduce self-injury for each of two developmentally disabled children. Even if the effect is strong for these two children, how can one know whether this treatment is likely to work for other developmentally disabled children? Again, single-subject researchers share this concern.
In response, they note that the strong and consistent effects they are typically interested in—even when observed in small samples—are likely to generalize to others in the population. Single-subject researchers also note that they place a strong emphasis on replicating their research results. When they observe an effect with a small sample of participants, they typically try to replicate it with another small sample—perhaps with a slightly different type of participant or under slightly different conditions. Each time they observe similar results, they rightfully become more confident in the generality of those results.
Single-subject researchers can also point to the fact that the principles of classical and operant conditioning—most of which were discovered using the single-subject approach—have been successfully generalized across an incredibly wide range of species and situations. And again turning the tables, single-subject researchers have concerns of their own about the external validity of group research.
One extremely important point they make is that studying large groups of participants does not entirely solve the problem of generalizing to other individuals. Imagine, for example, a treatment that has been shown to have a small positive effect on average in a large group study. It is likely that although many participants exhibited a small positive effect, others exhibited a large positive effect, and still others exhibited a small negative effect. When it comes to applying this treatment to another large group , we can be fairly sure that it will have a small effect on average.
But when it comes to applying this treatment to another individual , we cannot be sure whether it will have a small, a large, or even a negative effect. Another point that single-subject researchers make is that group researchers also face a similar problem when they study a single situation and then generalize their results to other situations. For example, researchers who conduct a study on the effect of cell phone use on drivers on a closed oval track probably want to apply their results to drivers in many other real-world driving situations.
But notice that this requires generalizing from a single situation to a population of situations. Thus the ability to generalize is based on much more than just the sheer number of participants one has studied. Shadish, W. Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin. As with quantitative and qualitative research, it is probably best to conceptualize single-subject research and group research as complementary methods that have different strengths and weaknesses and that are appropriate for answering different kinds of research questions Kazdin, Single-subject research is particularly good for testing the effectiveness of treatments on individuals when the focus is on strong, consistent, and biologically or socially important effects.
floreal.su/profiles/24.php It is especially useful when the behavior of particular individuals is of interest. Clinicians who work with only one individual at a time may find that it is their only option for doing systematic quantitative research. 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.