The demands of traditional experimental methods are often seen as barriers to clinical inquiry for several reasons. Because of their rigorous structure, experiments require control groups and large numbers of homogenous subjects, often unavailable in clinical settings. In addition, group studies typically take measurements at only two or three points in time, potentially missing variations in response that occur over time. Finally, the experimental model deals with group averages and generalizations across individuals, which may not allow the researcher to differentiate characteristics of those patients who responded favorably to treatment from those who did not improve. Therefore, although generalizations are important for explaining behavioral phenomena, clinicians understand that group performance is relevant only if it can be used to understand and predict individual performance.
To illustrate this dilemma, consider a study that was done to determine if the occurrence of stuttering would be different if adults read aloud at "usual" or "fast as possible" rates.1 A group of 20 adults was tested, and no significant difference was seen between the two conditions based on a comparison of group means. However, a closer look at individual results showed that 8 subjects actually decreased their frequency of stuttering, one didn't change, and 11 demonstrated an increase during the faster speaking condition. 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.
Single-subject designs∗ provide an alternative approach that allows us to draw conclusions about the effects of treatment based on the responses of a single patient under controlled conditions. Through a variety of strategies and structures, these designs provide a clinically viable, controlled experimental approach to the study of a single case or several subjects, and the flexibility to observe change under ongoing treatment conditions. Given the focus of evidence-based practice on clinical decision making for individual patients, these designs are especially useful.
Single-subject designs require the same attention to logical design and control as other experimental designs, based on a research hypothesis that indicates the expected relationship between an independent and dependent variable and specific operational definitions that address reliability and validity. The independent variable is the intervention. The dependent variable is the patient response, defined as a target behavior that is observable, quantifiable, and a valid indicator of treatment effectiveness.
Single-subject designs can be used to study comparisons between several treatments, between components of treatments, or between treatment and no-treatment conditions. The purpose of this chapter is to describe a variety of single-subject designs and to explore issues associated with their structure, application and interpretation.
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