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A second is that visual inspection can be unreliable, with different researchers reaching different conclusions about the same set of data (Danov & Symons, 2008).Danov, S. A survey evaluation of the reliability of visual inspection and functional analysis graphs. 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). Because they involve multiple individuals, some group designs can provide important information about the generality of an effect across the included population, especially in the case of within-subject group designs. Unfortunately, some often-misused aspects of group designs tend to temper this advantage.
General Features of Single-Subject Designs
From a practice perspective, one of the problems of statistical significance is that it can over- or underestimate clinical significance (Chassan, 1979). During the initial four sessions of the alternating treatments phase, responding remained at zero for all three word sets. Steadily increasing trends were observed in both of the directed rehearsal conditions beginning in the fifth session, whereas responding remained at zero in the control condition. The rate of acquisition in the directed rehearsal plus positive reinforcement condition was higher than in directed rehearsal alone throughout the alternating treatments phase. The latency in correct responding observed during the initial sessions of the alternating treatments was a demonstration of noneffect.
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To name just a few high-impact examples, Hodgkin et al. (1952) classic work on voltage clamping utilizing the giant squid neuron involved multiple parametric IV implementations on single neurons. This fruitful precedent of single-subject-like experiments at the very foundation of historical neuroscience together with the well-established efficacy of single-subject design in other fields imply that the wider adoption of the full methodology can succeed. Finally, related to several different comments in the preceding sections regarding practical significance, there is the issue of interpreting effects directly in relation to practice in terms of eventual empirically based decision making for a given client or participant. At issue here is not determining whether there was an effect and its standardized size but whether there is change in behavior or performance over time—and the rate of that change. Riley-Tillman and Burns (2009) argued that effect size estimates may make valuable contributions for future quantitative syntheses; however, for a given practitioner, data interpretation and subsequent practice decisions are driven more by slope changes, not by average effect sizes.
An Overview of Single-Subject Experimental Design
The independent variable (in this case, the token reinforcement system with the increasing dB criterion) was actively manipulated by the researchers, and the dependent variable was measured systematically over time. Each phase included a minimum of three data points (but not the five points required to meet the standards fully), and the number of phases with different criteria far exceeded the minimum three required. Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally.
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Reversal or A-B-A
In other cases still, the behavior may come to be maintained by other contingencies not under the control of the experimenter. An intervention or treatment cannot be considered evidence based following the results of a single study. The WWCH panel recommended that an intervention have a minimum of five supporting SSED studies meeting the evidence standards if the studies are to be combined into a single summary rating of the intervention's effectiveness. In Panel A of Figure 2, no change is observed until the third session of the intervention phase. This latency brings into question the assumption that the manipulation of the independent variable is responsible for the observed changes in the dependent variable.
Early in the research process, inductive inference based on single-subject manipulations are ideal to generate likely and testable abstractions (Russell, 1962). Using single-subject approaches for this inductive phase requires fewer resources compared to fully powered group approaches and can be more rigorous than small-n group pilots. An effect can be isolated in one individual, then systematically replicated across relevant differences and contexts until it fails to replicate, at which time explanatory variables can be adjusted until replicated results are produced. The altered experiment can then be analyzed in comparison to previous experiments to form a more general understanding that can be tested in a new series of experiments. After sufficient systematic replication, hybrid and group designs can assess the extent to which inductively and contextually informed abstractions generalize across the widest relevant populations.
SSEDs can be helpful in identifying the optimal treatment for a specific client and in describing individual-level effects. As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009)[2]. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at the student’s school. There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. For example, if a treatment seemed to reduce the incidence of self-injury in a child with an intellectual delay, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed.
The effect of an independent variable is easier to detect when the “noise” in the data is minimized. 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.
IV. Chapter 4: Psychological Measurement
For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place. Perhaps something else happened at about the same time as the treatment—for example, the student’s parents might have started rewarding him for good grades.
Further, the analysis approach taken in many group designs that narrowly examines changes in central tendency (such as the mean) of groups can limit the assessment of generality within the sampled population since averaging will wash out heterogeneity of effects. Other aspects of rigor in group designs can also affect external validity (e.g., Kravitz et al., 2004; Grossman and Mackenzie, 2005; Williams, 2010; Button et al., 2013). By contrast, Panel B of Figure 2 shows a data set in which an increasing trend is present during the baseline phase. As a result, any increases observed during the intervention phase may simply be a continuation of that trend rather than the result of the manipulation of the independent variable. This underscores the importance of “good” baseline data, and, in particular, of the need to continue collecting baseline data to eliminate the possibility that any trends observed are likely to continue in the absence of an intervention.
For example, restricted inclusion criteria are often necessary to produce clear results. When desired generality involves only such a restricted population (e.g., only acute stroke patients, or only layer IV glutamatergic cortical neurons), this practice carries no disadvantage. However, if the study aims to identify more widely applicable processes, stringent inclusion criteria can produce cleaner but overly conditional results, limiting external validity (Henrich et al., 2010).
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