Traditional research approaches, such as randomized controlled trials, are effective tools for assessing intervention success. However, when applied to a variety of rehabilitation settings and research issues, these approaches have practical constraints. Alternative methodologies exist that can enhance standard study findings and increase our capacity to assess the efficacy of therapies for specific patients. Evidence-based rehabilitation places a strong emphasis on the personal progress of the individuals (Smith & Little, 2018). Small-N study designs are one such alternative strategy examined in this work. Small-N designs often involve 10 or fewer individuals, whose behavior (also referred to as outcomes) is tracked and compared over time. Small-N design problems and prospects for improving evidence-based rehabilitation are highlighted.
Small-N designs may include a dynamic intervention that is adjusted throughout stages of the design, unlike classic parallel-group RCTs where the independent variable (intervention) is standardized. Different types of small-N designs are distinguished by how the intervention is implemented. While the science of small-N research is still evolving, the small-N literature commonly describes three design frameworks: basic and experimental withdrawal designs, multiple baseline designs, and shifting intensity or alternating treatment designs (Goodwin & Goodwin, 2016). It’s important to remember that the small-N designs described below differ in their ability to draw causal inferences between the treatment (independent variable) and the outcome (dependent variable), just as traditional group comparison designs differ in their ability to control threats to internal validity.
As Chris mentions in his post, the limitations of this study design are not necessarily flaws, but rather additional characteristics that make it a better fit for some projects rather than others. It works best for projects and questions of specific and detailed nature, meaning that it is easier for researchers to pick this design when it is needed. The aforementioned concerns concerning the internal validity and general representativity of the data can be navigated by ensuring the scientific method is followed at every step.
Reference
Goodwin, K. A., & Goodwin, C. J. (2016). Research in Psychology: Methods and Design (8th Edition). Wiley Global Education US. Web.
Smith, P. L., & Little, D. R. (2018). Small is beautiful: In defense of the small-N design. Psychonomic bulletin & review, 25(6), 2083-2101., Web.