What Is a Single-Case Experimental Design? Everything Students and Researchers Need to Know 

What Is a Single-Case Experimental Design? Everything Students and Researchers Need to Know

Single-Case Experimental Designs (SCEDs) have been increasingly accepted as an essential tool for the study of behaviour, clinical and educational research from the outset of their introduction since they allow to assess interventions' effects on the individual level.
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Introduction

Single-Case Experimental Designs (SCEDs) have been increasingly accepted as an essential tool for the study of behaviour, clinical and educational research from the outset of their introduction since they allow to assess interventions’ effects on the individual level. In situations where information on a whole population is lacking or when individual data is required SCEDs offer a strong, evidence-based technique for indirectly unveiling causal relations (Kazdin, 2021). With the improvement of randomization, visual analysis, and statistical modeling techniques over time, SCED methodology have become one of the most advanced methodologies in applied research (Tanious et al., 2024; Jamshidi et al., 2023).

1. Understanding Single-Case Experimental Designs

    • SCEDs comprise a series of repeated measurements taken from a subject during both the baseline and the treatment phases so as to be certain that the treatment produces a change that is really significant. 

What are the Key Characteristics of Single Case Experimental Design? (Kazdin, 2021):

  • Measurement done often: The behavior is measured either on daily basis or session-by-session.
  • Phase distinctions are clear: Baseline (A) and intervention (B) phases are put side by side.
  • Manipulation is systematic: The researchers either introduce or withdraw the interventions thereby, testing their effects.

    • Example Application of  Single Case Experimental Designs in Psychology

    A psychologist measuring the effect of a relaxation app on daily anxiety first makes the participant record the anxiety level daily for 10 days (baseline), then she gives the app to the participant for 14 days, and finally compares the profiles. If the anxiety level goes down only in the phase of the intervention, a causal effect is confirmed.
    According to Epstein and Dallery (2022), SCED methodology have been systematically classified as a design family that consists of several experimental methods including reversal (A–B–A–B), multiple baseline, alternating treatments, and changing criterion designs, each of which can be applied to a particular research scenario.

    2. Why SCEDs Are Important in Modern Research

    SCEDs have been increasingly used in different fields because of their importance in personalized research and dealing with small sample size.

    Reasons for Growth:

  • They are very suitable for cases of uncommon diseases or specific guidelines where it is impractical to use group designs (Aydin, 2024).
  • Research ethics flexibility enabling researchers to keep providing treatment when needed (Krasny-Pacini, 2023).
  • Limited but precise tracking of individual change thus generating rich behavioral data which is often masked by group means.

    • Example:
    In the case of child neurorehabilitation, for instance, the loss of functional movements might differ for each individual child with the disorder. Different therapists may monitor the child’s progressive improvements on a daily basis and adjust the corresponding therapy (Krasny-Pacini, 2023).

    Historical Insight:

    The inception of SCEDs was the non-acceptance of group experiments’ shortcomings particularly in behaviour therapy where one client at a time treatment application is the norm (Aydin, 2024).

    3. Major SCED Types and When to Use Them

    Different SCED methodology meet different research purposes.

    A. Reversal Designs (A–B–A–B)
    • Add and remove treatment and then add it again to confirm results.
    • Used when the behaviour goes back to its original state after the treatment is stopped.

    Example:
    Assessing the use of a reward system to reduce noise in the classroom. If noise decreases during the treatment phase, increases during the no-treatment phase, and decreases again when treatment is given again, then the cause-and-effect relationship is very clear.

    B. Multiple Baseline Designs
    • Intervention is implemented one after the other in a staggered way across participants, behaviours, or settings.
    • Use when: Withdrawal is not allowed (e.g., self-harm interventions).

    Example:
    A communication training program is done one by one in the case of three children with autism.

    C. Alternating Treatment Designs
    • Two or more interventions can be compared in a very short time.
    • Use when: You want to know the best treatment very fast.

    Example:
    Flashcards vs. a mobile app for vocabulary learning are compared by changing sessions every day.

    D. Changing Criterion Designs

    • Gradually adjust expected behaviour to evaluate intervention impact.

    Example:
    Reducing caffeine intake by setting weekly decreasing limits.

    4. What are the Methodological Standards in single-case study methods : Ensuring Scientific Rigor

    Current research explains valid standards which improve the SCED validity.

    Randomization

    The bias can be reduced and casual inference can be strengthened by randomizing phase changes.

    Tanious et al. (2024) reported that randomization prevents the researchers-driven phase timing and strengthen internal validity.

    Example:

    Choosing the day randomly to shift from baseline to treatments avoids unrequired manipulation.

    Replication

    The repeated demonstration of effects required by reliable SCED evidence (Kazdin, 2021).

    • Replicate across participants

    • Replicate across settings

    • Replicate across behaviours

    Example:

    Confidence in the treatment increases if an intervention strengthens fluency for three different students at various times.

    High-Frequency Measurement

    SCEDs Depends on current, various observation.

    Jamshidi et al. (2023) reported that when the data is collected regularly or daily per session, meta-analysis show better effects.

    Example:

    Observing test assessment behaviour in classroom during every zoology class rather than twice a month.

    Table:1 Key Scholarly Contributions to Single-Case Experimentation Research

    Author(s) & Year

    Core Contribution

    Relevance to SCED Research

    Kazdin (2021)

    Defines SCED characteristics, strengths, and methodological standards.

    Establishes foundational SCED principles—repeated measurement, replication, causal inference.

    Aydin (2024)

    Provides a historical overview and explains why SCEDs are essential in modern research.

    Shows SCED evolution and value in individualized, small-sample behavioural studies.

    Epstein & Dallery (2022)

    Describes the “family” of SCED structures (AB, reversal, multiple baseline, etc.).

    Offers classification and practical selection of SCED types for researchers.

    Jamshidi et al. (2023)

    Systematic review on SCED meta-analyses, data structures, and statistical methods.

    Highlights advanced modelling, Tau-U, and multilevel meta-analysis for SCEDs.

    Tanious et al. (2024)

    Emphasizes the importance of randomization and replication in SCED validity.

    Strengthens methodological rigor by advocating randomized phase changes.

    Krasny-Pacini (2023)

    Applies SCEDs to child neurology and developmental disability rehabilitation.

    Demonstrates SCED usefulness in clinical and neurorehabilitation settings.

    Tanious & Manolov (2022)

    Introduces violin plots for SCED meta-analysis visualization.

    Enhances clarity of effect size interpretation across cases.

    Dowdy et al. (2022)

    Reviews visual analysis advancements and automated SCED graphing tools.

    Modernizes SCED data interpretation with structured and technological methods.

    Aydin (2024)

    Evaluates missing-data issues and imputation techniques in SCEDs.

    Offers strategies for maintaining analytic accuracy when data gaps occur.

    5. Modern Advancements, Data Challenges, and Practical Use

    Recent developments have significantly improved the scientific precision of SCEDs.

    Advancements in Visual & Statistical Analysis

    • Automated visual analysis tools detect level and trend changes (Dowdy et al., 2022).

    • Effect size methods like Tau-U improve statistical inference.

    • Violin plots illustrate effect-size distributions across cases (Tanious & Manolov, 2022).

    • Multilevel modelling strengthens SCED meta-analyses (Jamshidi et al., 2023).

    Example:
    A researcher evaluating treatment effects across 20 SCED studies uses violin plots to visualise variability instead of relying on simple averages.

    Managing Missing Data

    Because SCEDs require dense time-series data, missing measurements threaten validity.
    Aydin (2024) emphasises:

    • Single imputation,

    • Sensitivity analysis, and

    • Improved data-collection practices.

    Example:
    If a participant misses two measurement days due to illness, imputation experimental methods can preserve analytic accuracy.

    Where SCEDs Are Most Useful

    • Clinical psychology: evaluating exposure therapy or habit reduction

    • Neurorehabilitation: tracking motor improvement

    • Education: testing instructional strategies

    • Behaviour analysis: assessing reinforcement or punishment systems

    Conclusion

    Single-Case Experimental Designs (SCEDs) are considered to be very practical methods for studying the change of individual behaviour in cases when large samples are not available. SCEDs, due to their emphasis on repeated measurement, clear phase manipulation, and replication, present strong causal claims. The application of modern analytic tools and the observance of methodologies standards have made SCEDs even more reliable. Nowadays, SCEDs are in the forefront of clinical, educational, and behavioural research which has made them a very important technique for assessing personal interventions.

    References

    1. Aydin, O. (2024). A description of missing data in single-case experimental designs studies and an evaluation of single imputation methods. Behavior Modification, 48(3), 312–359.
    2. Aydin, O. (2024). Rise of single-case experimental designs: A historical overview of the necessity of single-case methodology. Neuropsychological Rehabilitation, 34(3), 301–334.
    3. Dowdy, A., Jessel, J., Saini, V., & Peltier, C. (2022). Structured visual analysis of single‐case experimental design data: Developments and technological advancements. Journal of Applied Behavior Analysis, 55(2), 451–462.
    4. Epstein, L. H., & Dallery, J. (2022). The family of single-case experimental designs. Harvard Data Science Review, 4(SI3), 10–1162.
    5. Jamshidi, L., Heyvaert, M., Declercq, L., Fernández-Castilla, B., Ferron, J. M., Moeyaert, M., & Van den Noortgate, W. (2023). A systematic review of single-case experimental design meta-analyses: Characteristics of study designs, data, and analyses. Evidence-Based Communication Assessment and Intervention, 17(1), 6–30.
    6. Kazdin, A. E. (2021). Single‐case experimental designs: Characteristics, changes, and challenges. Journal of the Experimental Analysis of Behavior, 115(1), 56–85.
    7. Krasny‐Pacini, A. (2023). Single‐case experimental designs for child neurological rehabilitation and developmental disability research. Developmental Medicine & Child Neurology, 65(5), 611–624.
    8. Tanious, R., & Manolov, R. (2022). Violin plots as visual tools in the meta-analysis of single-case experimental designs. Methodology, 18(3), 221–238.*
    9. Tanious, R., Manolov, R., Onghena, P., & Vlaeyen, J. W. (2024). Single-case experimental designs: The importance of randomization and replication. Nature Reviews Methods Primers, 4(1), 27.

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