History of Single Case Experimental Designs

Single Case Experimental Design (SCEDs) have shaped research across psychology, medicine, and clinical trials by focusing on individual-level data. Long before large-scale statistical methods took over, researchers relied on structured observations to understand behaviour, cognition, and treatment effects. In SCEDs, the individual serves as their own control, with intervention conditions introduced sequentially. This approach allows for precise tracking of changes within an individual over time. SCEDs typically involve repeated measurement of a precisely defined target behaviour, rather than relying on broad or vague constructs, making the data highly specific and actionable. Data collection occurs across multiple phases, which helps account for variability and ensures that the effects of the intervention are clearly observable. This article follows the development of SCEDs, highlighting key figures, methodological shifts, and real-world applications.

Early Foundations in Observation

The historical origins of single case experimental designs (SCEDs) can be traced back to the early scientific endeavours that relied on individual observation to establish empirical knowledge. Many of the earliest experiments in psychology, medicine, and physiology used systematic within-subject comparisons rather than between-group designs. Long before statistical generalisation became the dominant mode of inference, scientists and researchers employed systematic observations on single subjects to understand behaviour, cognition, and physiological processes. 

The 19th Century: Psychology and Medicine

In the 19th century, pioneers in psychology and medicine recognised the value of controlled single-subject investigations. For example, Jean-Martin Charcot’s work on hysteria and aphasia involved meticulous observations of individuals over time. Around the same period, Francis Galton introduced methodological refinements in studying individual differences, laying a foundation for later developments in experimental design.

​​In the 19th century, pioneers in psychology and medicine recognised the value of controlled single-subject investigations, laying the groundwork for modern N of 1 trials and single case experimental designs. For example, Jean-Martin Charcot (1825–1893), a French neurologist, conducted meticulous longitudinal case studies on hysteria, aphasia, and neurological disorders at the Salpêtrière Hospital in Paris, closely observing how individual patients’ symptoms evolved over time. His research demonstrated the importance of tracking symptoms and responses to interventions at the individual level, influencing early neurology, psychiatry, and the later development of psychoanalytic case studies. Around the same period, Francis Galton (1822–1911) pioneered the study of individual differences, developing early methods for measuring traits such as intelligence and reaction times. His contributions to biometrics, correlation, and statistical analysis provided a methodological foundation for experimental research, reinforcing the need for precise measurement and control in studying individuals. Together, Charcot’s case-based clinical research and Galton’s quantitative approach shaped the evolution of evidence-based medicine and personalised research methodologies, directly influencing the development of single case experimental designs and N of 1 trials in psychology, neurology, and medicine.

Behaviourism and Experimental Control in the Early 20th Century

The early 20th century saw behaviourists, particularly John B. Watson and later B. F. Skinner, formalising experimental procedures that controlled variables within single subjects. Skinner’s operant conditioning experiments on animals in the 1930s exemplified this approach, reinforcing the notion that controlled manipulation of variables within individuals could yield generalisable principles of behaviour. His meticulous records of response patterns contributed to shaping systematic single case methodologies. 

Mid-20th Century: Expanding Rigour in Research

By the mid-20th century, applied behaviour analysts, including Sidney Bijou and Donald Baer, advanced the methodological rigour of SCDs, particularly in developmental psychology and education. They established experimental procedures that manipulated environmental variables while maintaining consistent observational records over time. These studies played a crucial role in demonstrating functional relationships between interventions and behavioural outcomes. 

Refining Methodologies (1960s-1970s)

By the mid-20th century, researchers such as Sidney Bijou and Donald Baer introduced multiple baseline and reversal (ABAB) designs, improving the ability to test interventions without large samples. The introduction of multiple baseline, reversal, alternating treatment, and changing criterion designs helped strengthen experimental control, allowing for more rigorous assessments of individual-level interventions. These structured designs bolstered the reliability of SCEDs in applied settings like special education and clinical psychology. 

Expanding into Medicine and Rehabilitation (1980s–1990s)

By the 1980s, the application of SCEDs had expanded significantly into medical and rehabilitation sciences, as they offered a valuable method for investigating treatments in fields where individual variability was a major factor. One of the key advantages of SCEDs in these contexts was their ability to monitor changes over time in a single individual, making them particularly useful for conditions that are highly individualised, such as neurological disorders, chronic pain, and mental health conditions. In neurorehabilitation, SCEDs were used to evaluate rehabilitation for conditions like stroke recovery or brain injury, where the patient's responses to treatment can vary widely. For instance, SCEDs allowed clinicians to observe and measure the effects of therapy over time, such as physical rehabilitation or cognitive therapies, and to adjust the treatment plan based on the patient’s progress. These designs were particularly beneficial in cases where large-scale clinical trials would not be feasible due to the uniqueness of each case.

The 1990s saw further refinement in data collection methods, making SCEDs more viable for clinical decision-making. Meta-analyses of SCEDs gained traction during this period, improving their credibility in systematic reviews and enhancing their acceptance within the broader clinical research community.

Recognition in Medicine and the Role of Standardised Guidelines (2000s–2010s)

Initially, N of 1 trials faced resistance in mainstream medicine due to their perceived lack of generalisability. However, by the 2010s, they were increasingly recognised as Level 1 evidence for treatment decisions, particularly in cases where individual variability in response to treatment was significant. Their acceptance was supported by growing evidence demonstrating their reliability in personalised medicine.

The introduction of the Single Case Reporting guideline In BEhavioral interventions (SCRIBE) helped standardise SCED methodology and reporting. SCRIBE established criteria to enhance transparency, reproducibility, and scientific rigour in single case studies, making them more acceptable within clinical and research communities. These guidelines reinforced SCEDs as a credible and structured approach to evidence-based decision-making.

Further validation of SCEDs as an evidence-based approach came through”, which helped define best practices. This rule requires SCED studies to demonstrate effects across multiple independent studies to establish reliability. Additionally, the Risk of Bias in N of 1 Trials (RoBiNT) scale was developed to assess methodological quality. By providing a structured way to evaluate SCEDs, the RoBiNT scale further strengthened their credibility within research and clinical settings

Throughout its history, SCEDs have continuously evolved through empirical refinement and methodological innovation. From its early foundations in detailed case observations to its current application in clinical trials and precision medicine, the principles underlying SCEDs remain deeply rooted in rigorous experimental control and systematic analysis.

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