1. Single-subject designs relate to clinical practice. While the unit of analysis in single-subject designs is usually an individual, it can also be a family, a group, a community, or an organization. Single subject designs are within subject designs (each participant is his/her own control), whereas group experimental designs are between subject designs (the participant is either in the treatment or control group).
2. Using single-subject designs to evaluate social work practice has been around since the late 1960’s, when they were borrowed from behavioral therapy analysis. Although these designs can be used with a range of different treatment approaches, including psychodynamic, they are closely associated with behavioral and cognitive approaches, where goals of intervention are often more discrete and unambiguous.
3. At the time these designs were first introduced in social work, there had been a number of group evaluations that had failed to demonstrate that social work services were effective. Single-subject designs were seen as one way to demonstrate the effectiveness of various forms of casework, types of treatment and modalities of intervention. They were also seen as a way to track the effects of treatment in individual cases. They were therefore seen as being of value for both the purposes of the practitioner who wanted to track the effects of their interventions and the researcher who wanted to document the effectiveness of different forms of treatment. Those promoting the adoption of these designs in social work hoped that single subject designs could be used to merge the roles of practitioner and researcher-evaluator and that promising interventions could documented by first being explored with an N of 1, on a case-by-case basis. Those interventions that worked could form the basis for larger group evaluative designs. These goals have only been partially met in routine practice, since the requirements of research and service may sometimes be in conflict with one another. For example, gathering baseline data is needed for research purposes, but is really extraneous for clinical purposes and may in fact be counter-indicated if the client is in crisis.
4. The general paradigm for single-subject designs involves first collecting data for a baseline activity of the target problem, e.g., drug addiction, depression or whatever, and then collecting more data after the introduction of a treatment intervention. The baseline activity phase is referred to as A and the intervention phase as B. The baseline phase is basically a control phase (serving the same function as control groups do in group experiments). To infer that an intervention is effective requires a comparison of shifts in the pattern of the data which coincides with shifts between the baseline and intervention phases. Extraneous events are much better controlled when there are several shifts between baseline and intervention phases for example, ABAB designs (withdrawal or reversal designs). However, the use of these more rigorous designs, where internal validity is maximized, still can have problems of carry-over effects where results from the previous phase carry-over into the next phase, order effects where the ordering of the intervention or treatment affects what results you will get, and the irreversibility of effects where once a change is effected it can not be undone. Also, multiple stage designs that withdraw treatment can at times present ethical problems and feasibility problems because they often cannot be implemented in the complex world of practice, including the constraints of managed care.
5. They are several basic steps in performing a single-subject design that require forethought because of the relationship of the client to the clinician can complicate measurement issues: (1) through negotiation with the client, define and operationalize a target behavior ( the dependent variable) that needs changing; (2) decide how this target behavior will be measured - typically frequency, duration, magnitude or some combination are used - sources of data can vary (self-report scales, direct behavioral observation, interviews, etc.) and who will measure it (client, worker, family member or some combination of the above)--Note that triangulation of measures is important--Note also that potential observable indicators of the problem can be positive or negative -- Measures should be reliable and valid--Watch out for reactivity (process of measuring itself brings about change aka "the Hawthorne Effect"), social desirability where the client tries to please the clinician through improving their measured performance--Note also that qualitative methods can supplement quantitative methods; (3) clinician chooses an intervention (the independent variable) and operationalizes it; (4) baseline phase is introduced -to enhance internal validity of design need to have enough measurement points of baseline frequency of target behavior to show a stable trend in the behavior and to form the basis of comparison when the intervention is applied; and (5) data analysis, often involving examining the visual pattern in graphs to look for visual significanceas in a sharp shift in the data pattern as graphed, using certain statistical procedures to determine statistical significance or the probability that the difference in the data before and after the intervention did not occur by chance, and assessing clinical significance (i.e., whether the change in behavior make a substantive change in the client’s life).
6. While the external validity of single-subject designs is even lower than for traditional experiments, external validity can be extended by replication of important findings can be done. These designs can also be used to examine outlier cases and as a way of testing promising interventions before using group experimental designs.
7. Remember the threats to internal and external validity for experimental design and think about how they apply to single subject designs. The main advantage of experimental designs is that they systematically account, if not always perfectly, for threats to internal validity, i.e., pitfalls that may impede the valid documentation of a relationship between a causal variable and its effect on some other factor. The major threats to validity include:
history—extraneous events occur of the course of time that elapses while an experiment is ongoing and they, not the independent causal factor, affects the dependent or caused factor;
maturation—the subjects being studied change due to aging or the passage of time (without being affected by either the causal factor or extraneous events); testing—pre-tests or other testing prime the subjects so that they answer later during a post-test or other tests differently than they would otherwise;
testing-the mere fact of being tested creates the effect that you are looking for; subjects can improve their performance on a post-test by taking the pre-test;
instrumentation—the testing is done in a way that affects how people are scored, especially where the test scoring method changes as in asking different questions or using different standards of judgment such that it looks like people changed but they really did not;
statistical regression—often called regression to the mean where the people being studied are first tested when they are likely to have extreme scores that will lapse back toward their more normal or average scores later on when they are given a posttest;
selection bias--where people or subjects are not randomly assigned to groups or are not assigned in a way that ensures the groups are comparable;
experimental mortality--where selected subjects drop out of the study;
ambiguity about the direction of causal influence--where there remains confusion about the causal order such that we can not be sure that the cause precedes the effect, as when those who completed a drug treatment program may have already chosen abstinence, such that giving up drugs preceded and in fact promoted successful completion of the treatment program rather than successful completion of treatment engendering abstinence;
diffusion or imitation of treatment--where subjects in the control group are not really treated that differently because the treatment reserved for the experimental group is spread to the control group or is imitated.
8. Various designs, the classic two-group pre/post-test design , the two-group post-test only design, the Solomon four-group design, provide different ways to counter these threats to internal validity and increase the chances of successfully engaging in controlled observation and isolating causality. They all are based on using random assignment or matching with random assignment in order to establish equivalent groups.
9. Yet, even if the threats to internal validity are effectively countered; experiments tend to have a harder time of ensuring external validity or generalizability to a broader population. One major problem experiments have is that there tends to be a tradeoff between internal validity and external validity. The more the experiment is controlled, the more artificial the situation is and the less generalizable it is. Experiments tend to have the problem of research reactivity where subjects behave differently than they will normally outside of the experiment because they are reacting to being in an experiment. This is sometimes referred to as the "Hawthorne Effect."
10. Useful comparisons can be made between single-subject and traditional group designs. In terms of similarities, both are longitudinal and are concerned with: (1) issues of control; (2) with specifying targets of intervention in operational terms; (3) with developing measurement and recording plans for assessing these targets; and (4) can use a combination of process and outcome measures, though single subject designs rarely employ process measures, which try to assess the "black box" of treatment or what kinds of interactions go on between clients and therapists during the course of an intervention. In terms of dissimilarities , single subject designs typically use more repeated measures; duration of the research is more variable; participants are more actively involved in setting the goals and targets of interventions; the choice of design is typically established by the worker rather than the researcher; designs are more flexible, responding to the needs of the particular case rather than fixed; findings have more direct and immediate impact on interventions at the individual case level; and they are much less costly than group designs.