single case experimental design vs case study

Case Study vs. Single-Case Experimental Designs

What's the difference.

Case study and single-case experimental designs are both research methods used in psychology and other social sciences to investigate individual cases or subjects. However, they differ in their approach and purpose. Case studies involve in-depth examination of a single case, such as an individual, group, or organization, to gain a comprehensive understanding of the phenomenon being studied. On the other hand, single-case experimental designs focus on studying the effects of an intervention or treatment on a single subject over time. These designs use repeated measures and control conditions to establish cause-and-effect relationships. While case studies provide rich qualitative data, single-case experimental designs offer more rigorous experimental control and allow for the evaluation of treatment effectiveness.

Further Detail

Introduction.

When conducting research in various fields, it is essential to choose the appropriate study design to answer research questions effectively. Two commonly used designs are case study and single-case experimental designs. While both approaches aim to provide valuable insights into specific phenomena, they differ in several key attributes. This article will compare and contrast the attributes of case study and single-case experimental designs, highlighting their strengths and limitations.

Definition and Purpose

A case study is an in-depth investigation of a particular individual, group, or event. It involves collecting and analyzing qualitative or quantitative data to gain a comprehensive understanding of the subject under study. Case studies are often used to explore complex phenomena, generate hypotheses, or provide detailed descriptions of unique cases.

On the other hand, single-case experimental designs are a type of research design that focuses on studying a single individual or a small group over time. These designs involve manipulating an independent variable and measuring its effects on a dependent variable. Single-case experimental designs are particularly useful for examining cause-and-effect relationships and evaluating the effectiveness of interventions or treatments.

Data Collection and Analysis

In terms of data collection, case studies rely on various sources such as interviews, observations, documents, and artifacts. Researchers often employ multiple methods to gather rich and diverse data, allowing for a comprehensive analysis of the case. The data collected in case studies are typically qualitative in nature, although quantitative data may also be included.

In contrast, single-case experimental designs primarily rely on quantitative data collection methods. Researchers use standardized measures and instruments to collect data on the dependent variable before, during, and after the manipulation of the independent variable. This allows for a systematic analysis of the effects of the intervention or treatment on the individual or group being studied.

Generalizability

One of the key differences between case studies and single-case experimental designs is their generalizability. Case studies are often conducted on unique or rare cases, making it challenging to generalize the findings to a larger population. The focus of case studies is on providing detailed insights into specific cases rather than making broad generalizations.

On the other hand, single-case experimental designs aim to establish causal relationships and can provide evidence for generalizability. By systematically manipulating the independent variable and measuring its effects on the dependent variable, researchers can draw conclusions about the effectiveness of interventions or treatments that may be applicable to similar cases or populations.

Internal Validity

Internal validity refers to the extent to which a study accurately measures the cause-and-effect relationship between variables. In case studies, establishing internal validity can be challenging due to the lack of control over extraneous variables. The presence of multiple data sources and the potential for subjective interpretation may also introduce bias.

In contrast, single-case experimental designs prioritize internal validity by employing rigorous control over extraneous variables. Researchers carefully design the intervention or treatment, implement it consistently, and measure the dependent variable under controlled conditions. This allows for a more confident determination of the causal relationship between the independent and dependent variables.

Time and Resources

Case studies often require significant time and resources due to their in-depth nature. Researchers need to spend considerable time collecting and analyzing data from various sources, conducting interviews, and immersing themselves in the case. Additionally, case studies may involve multiple researchers or a research team, further increasing the required resources.

On the other hand, single-case experimental designs can be more time and resource-efficient. Since they focus on a single individual or a small group, data collection and analysis can be more streamlined. Researchers can also implement interventions or treatments in a controlled manner, reducing the time and resources needed for data collection.

Ethical Considerations

Both case studies and single-case experimental designs require researchers to consider ethical implications. In case studies, researchers must ensure the privacy and confidentiality of the individuals or groups being studied. Informed consent and ethical guidelines for data collection and analysis should be followed to protect the rights and well-being of the participants.

Similarly, in single-case experimental designs, researchers must consider ethical considerations when implementing interventions or treatments. The well-being and safety of the individual or group being studied should be prioritized, and informed consent should be obtained. Additionally, researchers should carefully monitor and evaluate the potential risks and benefits associated with the intervention or treatment.

Case studies and single-case experimental designs are valuable research approaches that offer unique insights into specific phenomena. While case studies provide in-depth descriptions and exploratory analyses of individual cases, single-case experimental designs focus on establishing causal relationships and evaluating interventions or treatments. Researchers should carefully consider the attributes and goals of their study when choosing between these two designs, ensuring that the selected approach aligns with their research questions and objectives.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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In This Article Expand or collapse the "in this article" section Single-Case Experimental Designs

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• Types of Single-Case Experimental Designs
• Model Building and Randomization in Single-Case Experimental Designs
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• Reporting Single-Case Design Intervention Research

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Single-Case Experimental Designs by S. Andrew Garbacz , Thomas R. Kratochwill LAST REVIEWED: 29 July 2020 LAST MODIFIED: 29 July 2020 DOI: 10.1093/obo/9780199828340-0265

Single-case experimental designs are a family of experimental designs that are characterized by researcher manipulation of an independent variable and repeated measurement of a dependent variable before (i.e., baseline) and after (i.e., intervention phase) introducing the independent variable. In single-case experimental designs a case is the unit of intervention and analysis (e.g., a child, a school). Because measurement within each case is conducted before and after manipulation of the independent variable, the case typically serves as its own control. Experimental variants of single-case designs provide a basis for determining a causal relation by replication of the intervention through (a) introducing and withdrawing the independent variable, (b) manipulating the independent variable across different phases, and (c) introducing the independent variable in a staggered fashion across different points in time. Due to their economy of resources, single-case designs may be useful during development activities and allow for rapid replication across studies.

Several sources provide overviews of single-case experimental designs. Barlow, et al. 2009 includes an overview for the development of single-case experimental designs, describes key considerations for designing and conducting single-case experimental design research, and reviews procedural elements, assessment strategies, and replication considerations. Kazdin 2011 provides detailed coverage of single-case experimental design variants as well as approaches for evaluating data in single-case experimental designs. Kratochwill and Levin 2014 describes key methodological features that underlie single-case experimental designs, including philosophical and statistical foundations and data evaluation. Ledford and Gast 2018 covers research conceptualization and writing, design variants within single-case experimental design, definitions of variables and associated measurement, and approaches to organize and evaluate data. Riley-Tillman and Burns 2009 provides a practical orientation to single-case experimental designs to facilitate uptake and use in applied settings.

Barlow, D. H., M. K. Nock, and M. Hersen, eds. 2009. Single case experimental designs: Strategies for studying behavior change . 3d ed. New York: Pearson.

A comprehensive reference about the process of designing and conducting single-case experimental design studies. Chapters are integrative but can stand alone.

Kazdin, A. E. 2011. Single-case research designs: Methods for clinical and applied settings . 2d ed. New York: Oxford Univ. Press.

A complete overview and description of single-case experimental design variants as well as information about data evaluation.

Kratochwill, T. R., and J. R. Levin, eds. 2014. Single-case intervention research: Methodological and statistical advances . New York: Routledge.

The authors describe in depth the methodological and analytic considerations necessary for designing and conducting research that uses a single-case experimental design. In addition, the text includes chapters from leaders in psychology and education who provide critical perspectives about the use of single-case experimental designs.

Ledford, J. R., and D. L. Gast, eds. 2018. Single case research methodology: Applications in special education and behavioral sciences . New York: Routledge.

Covers the research process from writing literature reviews, to designing, conducting, and evaluating single-case experimental design studies.

Riley-Tillman, T. C., and M. K. Burns. 2009. Evaluating education interventions: Single-case design for measuring response to intervention . New York: Guilford Press.

Focuses on accelerating uptake and use of single-case experimental designs in applied settings. This book provides a practical, “nuts and bolts” orientation to conducting single-case experimental design research.

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• CREd Library , Research Design and Method

Single-Subject Experimental Design: An Overview

Cred library, julie wambaugh, and ralf schlosser.

• December, 2014

DOI: 10.1044/cred-cred-ssd-r101-002

Single-subject experimental designs – also referred to as within-subject or single case experimental designs – are among the most prevalent designs used in CSD treatment research. These designs provide a framework for a quantitative, scientifically rigorous approach where each participant provides his or her own experimental control.

An Overview of Single-Subject Experimental Design

What is single-subject design.

Transcript of the video Q&A with Julie Wambaugh. The essence of single-subject design is using repeated measurements to really understand an individual’s variability, so that we can use our understanding of that variability to determine what the effects of our treatment are. For me, one of the first steps in developing a treatment is understanding what an individual does. So, if I were doing a group treatment study, I would not necessarily be able to see or to understand what was happening with each individual patient, so that I could make modifications to my treatment and understand all the details of what’s happening in terms of the effects of my treatment. For me it’s a natural first step in the progression of developing a treatment. Also with the disorders that we deal with, it’s very hard to get the number of participants that we would need for the gold standard randomized controlled trial. Using single-subject designs works around the possible limiting factor of not having enough subjects in a particular area of study. My mentor was Dr. Cynthia Thompson, who was trained by Leija McReynolds from the University of Kansas, which was where a lot of single-subject design in our field originated, and so I was fortunate to be on the cutting edge of this being implemented in our science back in the late ’70s early ’80s. We saw, I think, a nice revolution in terms of attention to these types of designs, giving credit to the type of data that could be obtained from these types of designs, and a flourishing of these designs really through the 1980s into the 1990s and into the 2000s. But I think — I’ve talked with other single-subject design investigators, and now we’re seeing maybe a little bit of a lapse of attention, and a lack of training again among our young folks. Maybe people assume that people understand the foundation, but they really don’t. And more problems are occurring with the science. I think we need to re-establish the foundations in our young scientists. And this project, I think, will be a big plus toward moving us in that direction.

What is the Role of Single-Subject Design?

Transcript of the video Q&A with Ralf Schlosser. So what has happened recently, is with the onset of evidence-based practice and the adoption of the common hierarchy of evidence in terms of designs. As you noted the randomized controlled trial and meta-analyses of randomized controlled trials are on top of common hierarchies. And that’s fine. But it doesn’t mean that single-subject cannot play a role. For example, single-subject design can be implemented prior to implementing a randomized controlled trial to get a better handle on the magnitude of the effects, the workings of the active ingredients, and all of that. It is very good to prepare that prior to developing a randomized controlled trial. After you have implemented the randomized controlled trial, and then you want to implement the intervention in a more naturalistic setting, it becomes very difficult to do that in a randomized form or at the group level. So again, single-subject design lends itself to more practice-oriented implementation. So I see it as a crucial methodology among several. What we can do to promote what single-subject design is good for is to speak up. It is important that it is being recognized for what it can do and what it cannot do.

Basic Features and Components of Single-Subject Experimental Designs

Defining Features Single-subject designs are defined by the following features:

• An individual “case” is the unit of intervention and unit of data analysis.
• The case provides its own control for purposes of comparison. For example, the case’s series of outcome variables are measured prior to the intervention and compared with measurements taken during (and after) the intervention.
• The outcome variable is measured repeatedly within and across different conditions or levels of the independent variable.

See Kratochwill, et al. (2010)

Structure and Phases of the Design Single-subject designs are typically described according to the arrangement of baseline and treatment phases.

The conditions in a single-subject experimental study are often assigned letters such as the A phase and the B phase, with A being the baseline, or no-treatment phase, and B the experimental, or treatment phase. (Other letters are sometimes used to designate other experimental phases.) Generally, the A phase serves as a time period in which the behavior or behaviors of interest are counted or scored prior to introducing treatment. In the B phase, the same behavior of the individual is counted over time under experimental conditions while treatment is administered. Decisions regarding the effect of treatment are then made by comparing an individual’s performance during the treatment, B phase, and the no-treatment. McReynolds and Thompson (1986)

Basic Components Important primary components of a single-subject study include the following:

• The participant is the unit of analysis, where a participant may be an individual or a unit such as a class or school.
• Participant and setting descriptions are provided with sufficient detail to allow another researcher to recruit similar participants in similar settings.
• Dependent variables are (a) operationally defined and (b) measured repeatedly.
• An independent variable is actively manipulated, with the fidelity of implementation documented.
• A baseline condition demonstrates a predictable pattern which can be compared with the intervention condition(s).
• Experimental control is achieved through introduction and withdrawal/reversal, staggered introduction, or iterative manipulation of the independent variable.
• Visual analysis is used to interpret the level, trend, and variability of the data within and across phases.
• External validity of results is accomplished through replication of the effects.
• Social validity is established by documenting that interventions are functionally related to change in socially important outcomes.

See Horner, et al. (2005)

Common Misconceptions

Single-Subject Experimental Designs versus Case Studies

Transcript of the video Q&A with Julie Wambaugh. One of the biggest mistakes, that is a huge problem, is misunderstanding that a case study is not a single-subject experimental design. There are controls that need to be implemented, and a case study does not equate to a single-subject experimental design. People misunderstand or they misinterpret the term “multiple baseline” to mean that because you are measuring multiple things, that that gives you the experimental control. You have to be demonstrating, instead, that you’ve measured multiple behaviors and that you’ve replicated your treatment effect across those multiple behaviors. So, one instance of one treatment being implemented with one behavior is not sufficient, even if you’ve measured other things. That’s a very common mistake that I see. There’s a design — an ABA design — that’s a very strong experimental design where you measure the behavior, you implement treatment, and you then to get experimental control need to see that treatment go back down to baseline, for you to have evidence of experimental control. It’s a hard behavior to implement in our field because we want our behaviors to stay up! We don’t want to see them return back to baseline. Oftentimes people will say they did an ABA. But really, in effect, all they did was an AB. They measured, they implemented treatment, and the behavior changed because the treatment was successful. That does not give you experimental control. They think they did an experimentally sound design, but because the behavior didn’t do what the design requires to get experimental control, they really don’t have experimental control with their design.

Single-subject studies should not be confused with case studies or other non-experimental designs.

In case study reports, procedures used in treatment of a particular client’s behavior are documented as carefully as possible, and the client’s progress toward habilitation or rehabilitation is reported. These investigations provide useful descriptions. . . .However, a demonstration of treatment effectiveness requires an experimental study. A better role for case studies is description and identification of potential variables to be evaluated in experimental studies. An excellent discussion of this issue can be found in the exchange of letters to the editor by Hoodin (1986) [Article] and Rubow and Swift (1986) [Article]. McReynolds and Thompson (1986)

Other Single-Subject Myths

Transcript of the video Q&A with Ralf Schlosser. Myth 1: Single-subject experiments only have one participant. Obviously, it requires only one subject, one participant. But that’s a misnomer to think that single-subject is just about one participant. You can have as many as twenty or thirty. Myth 2: Single-subject experiments only require one pre-test/post-test. I think a lot of students in the clinic are used to the measurement of one pre-test and one post-test because of the way the goals are written, and maybe there’s not enough time to collect continuous data.But single-case experimental designs require ongoing data collection. There’s this misperception that one baseline data point is enough. But for single-case experimental design you want to see at least three data points, because it allows you to see a trend in the data. So there’s a myth about the number of data points needed. The more data points we have, the better. Myth 3: Single-subject experiments are easy to do. Single-subject design has its own tradition of methodology. It seems very easy to do when you read up on one design. But there are lots of things to consider, and lots of things can go wrong.It requires quite a bit of training. It takes at least one three-credit course that you take over the whole semester.

Further Reading: Components of Single-Subject Designs

Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M. & Shadish, W. R. (2010). Single-case designs technical documentation. From the What Works Clearinghouse. http://ies.ed.gov/ncee/wwc/documentsum.aspx?sid=229

Further Reading: Single-Subject Design Textbooks

Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings. Oxford University Press.

McReynolds, L. V. & Kearns, K. (1983). Single-subject experimental designs in communicative disorders. Baltimore: University Park Press.

Further Reading: Foundational Articles

Julie Wambaugh University of Utah

Ralf Schlosser Northeastern University

The content of this page is based on selected clips from video interviews conducted at the ASHA National Office.

Additional digested resources and references for further reading were selected and implemented by CREd Library staff.

Copyright © 2015 American Speech-Language-Hearing Association

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• Published: 05 April 2024

Single-case experimental designs: the importance of randomization and replication

• René Tanious   ORCID: orcid.org/0000-0002-5466-1002 1 ,
• Rumen Manolov   ORCID: orcid.org/0000-0002-9387-1926 2 ,
• Patrick Onghena 3 &
• Johan W. S. Vlaeyen   ORCID: orcid.org/0000-0003-0437-6665 1  

Nature Reviews Methods Primers volume  4 , Article number:  27 ( 2024 ) Cite this article

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Single-case experimental designs are rapidly growing in popularity. This popularity needs to be accompanied by transparent and well-justified methodological and statistical decisions. Appropriate experimental design including randomization, proper data handling and adequate reporting are needed to ensure reproducibility and internal validity. The degree of generalizability can be assessed through replication.

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R.T. and J.W.S.V. disclose support for the research of this work from the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.004.016) within the research project ‘New Science of Mental Disorders’ ( www.nsmd.eu ). R.M. discloses support from the Generalitat de Catalunya’s Agència de Gestió d’Ajusts Universitaris i de Recerca (grant number 2021SGR00366).

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Tanious, R., Manolov, R., Onghena, P. et al. Single-case experimental designs: the importance of randomization and replication. Nat Rev Methods Primers 4 , 27 (2024). https://doi.org/10.1038/s43586-024-00312-8

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The Family of Single-Case Experimental Designs

Leonard h. epstein.

1 Jacobs School of Medicine and Biomedical Sciences, Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, Buffalo, New York, United States of America,

Jesse Dallery

2 Department of Psychology, University of Florida, Gainesville, Florida, United States of America

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case—which can be an individual, clinic, or community—ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design—including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs—are also discussed.

1. Introduction

Single-case experimental designs (SCEDs) represent a family of experimental designs to examine the relationship between one or more treatments or levels of treatment and changes in biological or behavioral outcomes. These designs originated in early experimental psychology research ( Boring, 1929 ; Ebbinghaus, 1913 ; Pavlov, 1927 ), and were later expanded and formalized in the fields of basic and applied behavior analysis ( Morgan & Morgan, 2001 ; Sidman, 1960 ). SCEDs have been extended to a number of fields, including medicine ( Lillie et al., 2011 ; Schork, 2015 ), public health ( Biglan et al., 2000 ; Duan et al., 2013 ), education ( Horner et al., 2005 ), counseling psychology ( Lundervold & Belwood, 2000 ), clinical psychology ( Vlaeyen et al., 2020 ), health behavior ( McDonald et al., 2017 ), and neuroscience ( Soto, 2020 ).

SCEDs provide a framework to determine whether changes in a target behavior(s) or symptom are in fact a function of the intervention. The fundamentals of an SCED involve repeated measurement, replication of conditions (e.g., baseline and intervention conditions), and the analysis of effects with respect to each individual serving as his or her own control. This process can be useful for identifying the optimal treatment for an individual ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ), treating rare diseases ( Abrahamyan et al., 2016 ), and implementing early phase translational research ( Czajkowski et al., 2015 ). SCEDs can be referred to as ‘personalized (N-of-1) trials’ when used this way, but they also have broad applicability to a range of scientific questions. Results from SCEDs can be aggregated using meta-analytic techniques to establish generalizable methods and treatment guidelines ( Shadish, 2014 ; Vannest et al., 2018 ). Figure 1 presents the main family of SCEDs, and shows how personalized (N-of-1) trials fit into these designs ( Vohra et al., 2016 ). The figure also distinguishes between experimental and nonexperimental single-case designs. In the current article, we provide an overview of SCEDs and thus a context for the articles in this special issue focused on personalized (N-of-1) trials. Our focus is to provide the fundamentals of these designs, and more detailed treatments of data analysis ( Moeyaert & Fingerhut, 2022 ; Schork, 2022 ) conduct and reporting standards ( Kravitz & Duan, 2022 ; Porcino & Vohra, 2022 ), and other methodological considerations are provided in this special issue. Our hope is that this article will inspire a diverse array of students, engineers, scientists, and practitioners to further explore the utility, rigor, and flexibility of these designs.

A = Baseline, B and C refer to different treatments.

The most common approach to evaluating the effectiveness of interventions on outcomes is using randomized controlled trials (RCTs). RCTs provide an idea of the average effect of an intervention on outcomes. People do not all change at the same rate or in the same way, however; variability in both how people change and the effect of the intervention is inevitable ( Fisher et al., 2018 ; Normand, 2016 ; Roustit et al., 2018 ). These sources of variability are conflated in a typical RCT, leading to heterogeneity of treatment effects (HTE). Research on HTE has shown variability in outcomes in RCTs, and in some studies very few people actually exhibit the benefits of that treatment ( Williams, 2010 ). One approach in RCTs is to assess moderators of treatment response to identify individual differences that may predict response to a treatment. This approach may not limit variability in response, and substantial reduction in variability of treatment for subgroups in comparison to the group as a whole is far from assured. Even if variability is reduced, the average effect for that subgroup may not be representative of individual members of the subgroup.

SCEDs can identify the optimal treatment for an individual person rather than the average person in a group ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Hekler et al., 2020 ). SCEDs are multiphase experimental designs in which a great deal of data is collected on a single person, said person serves as his or her own control ( Kazdin, 2011 , 2021 ), and the order of presentation of conditions can be randomized to enhance experimental control. That is, a person’s outcomes in one phase are compared to outcomes in another phase. In a typical study, replications are achieved within and/or across several individuals; this allows for strong inferences about causation between behavior and the treatment (or levels thereof). Achieving replications is synonymous with achieving experimental control.

We provide an overview of three experimental designs that can be adapted for personalized medicine: reversal, multiple baseline, and combined reversal and multiple baseline designs, and we discuss how SCEDs can be integrated into RCTs. These designs focus on demonstrating experimental control of the relationship between treatment and outcome. Several general principles common to all of the designs are noteworthy ( Lobo et al., 2017 ). First, in many studies, treatment effects are compared with control conditions with a no- intervention baseline as the initial condition. To reduce threats to internal validity of the study, the order of assignment of interventions can be randomized ( Kratochwill & Levin, 2010 ) and, when possible, the intervention and data collection can be blinded. The demonstration of experimental control across conditions or people needs to be replicated several times (three replications is the minimum) to ensure confidence of the relationship between treatment and outcome ( Kratochwill et al., 2010 ; Kratochwill & Levin, 2015 ). Demonstrating stability of data within a phase or, otherwise, no trend in the direction of treatment effects prior to starting treatment is particularly important. Stability refers to the degree of variability in the data path over time (e.g., data points must fall within a 15% range of the median for a condition). Thus, phase length needs to be flexible for the sake of determining stability and trend within a phase, but a minimum of 5 data points per phase has been recommended ( Kratochwill et al., 2013 ). The focus of the intervention’s effects is on clinically rather than statistically significant effects with the target effect prespecified and considered in interpretation of the relevance of the effect for clinical practice ( Epstein et al., 2021 ). In addition, multiple dependent outcomes can be simultaneously measured ( Epstein et al., 2021 ). SCEDs can be used to test whether a variable mediates the effect of a treatment on symptoms or behavior ( Miočević et al., 2020 ; Riley & Gaynor, 2014 ). Visual inspection of graphical data is typically used to determine treatment effects, and statistical methods are commonly used to assist in interpretation of graphical data ( Epstein et al., 2021 ). Furthermore, a growing number of statistical approaches can summarize treatment effects and provide effect sizes ( Kazdin, 2021 ; Moeyaert & Fingerhut, this issue; Pustejovsky, 2019 ; Shadish et al., 2014 ). Data across many SCED trials can be aggregated to assess the generality of the treatment effects to help address for whom and under what conditions an intervention is effective ( Branch & Pennypacker, 2013 ; Shadish, 2014 ; Van den Noortgate & Onghena, 2003 ).

2. Reversal Designs

A reversal design collects behavioral or biological outcome data in at least two phases: a baseline or no treatment phase (labeled as ‘A’) and the experimental or treatment phase (labeled as ‘B’). The design is called a reversal design because there must be reversals or replications of phases for each individual; for example, in an ABA design, the baseline phase is replicated ( Kazdin, 2011 ). Ideally, three replications of treatment effects are used to demonstrate experimental control ( Kratochwill et al., 2010 ; Kratochwill & Levin, 1992 ). Figure 2 shows hypothetical results from an A1B1A2B2 design. The graph shows three replications of treatment effects (A1 versus B1, B1 versus A2, A2 versus B2) across four participants. Each phase was carried out until stability was evident from visual inspection of the data as well as absence of trends in the direction of the desired effect. The replication across participants increases the confidence in the effectiveness of the intervention. Extension of this design is possible by comparing multiple interventions, as well. The order of the treatments should be randomized, especially when the goal is to combine SCEDs across participants.

A1 = First Baseline, B1 First Treatment, A2 = Return to Baseline, B2 = Return to Treatment. P1–P4 represent different hypothetical participants.

Reversal designs can be more dynamic and compare several treatments. A common approach in personalized medicine would be to compare two or more doses of or different components of the same treatment ( Ward-Horner & Sturmey, 2010 ). For example, two drug doses could be compared using an A1B1C1B2C2 design, where A represents placebo and B and C represent the different drug doses ( Guyatt et al., 1990 ). In the case of drug studies, the drug/placebo administration can be double blinded. A more complex design could be A1B1A2C1A3C2A4B2, which would yield multiple replications of the comparison between drug and placebo. Based on the kinetics of the drug and the need for a washout period, the design could also be A1B1C1B2C2. This would provide three demonstration of treatment effects: B1 to C1, C1 to B2, and B2 to C2. Other permutations could be planned strategically to identify the optimal dose for each individual.

Advantages of SCED reversal designs are their ability to experimentally show that a particular treatment was functionally related to a particular change in an outcome variable for that person . This is the core principle of personalized medicine: an optimal treatment for an individual can be identified ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Guyatt et al., 1990 ; Hekler et al., 2020 ; Lillie et al., 2011 ). These designs can work well for studying the effect of interventions on rare diseases in which collecting enough participants with similar characteristics for an RCT would be unlikely. An additional strength is the opportunity for the clinical researcher who also delivers clinical care to translate basic science findings or new findings from RCTs to their patients, who can potentially benefit ( Dallery & Raiff, 2014 ; Hayes, 1981 ). Research suggests that the trickledown of new developments and hypotheses to their support in RCTs can take more than 15 years; many important advancements in the medical and behavior sciences are likely not to be implemented rapidly enough ( Riley et al., 2013 ). The ability to test new intervention developments using scientific principles could speed up their translation into practice.

Limitations to SCED designs, however, are worth noting. Firstly, in line with the expectation that the outcome returns to baseline levels, reversals may require removal of the treatment. If the effect is not quickly reversible, then the designs are not relevant. A washout period may be placed in-between phases if the effect is not immediately reversible; for example, a drug washout period could be planned based on the half-life of drug. Secondly, the intervention should have a relatively immediate effect on the outcome. If many weeks to months are needed for some interventions to show effects, a reversal design may not be optimal unless the investigator is willing to plan a lengthy study. Thirdly, the design depends on comparing stable data over conditions. If achieving stability due to uncontrolled sources of biological or environmental variation is not possible, a reversal design may not be appropriate to evaluate a treatment, though it may be useful to identify the sources of variability ( Sidman, 1960 ). Finally, for a reversal to a baseline, a no-treatment phase may be inappropriate in investigating treatment effects for a very ill patient.

3. Multiple Baseline Designs

An alternative to a reversal design is the multiple baseline design, which does not require reversal of conditions to establish experimental control. There are three types of multiple baseline designs: multiple baseline across people, behaviors, and settings. The most popular is the multiple baseline across people, in which baselines are established for three or more people for the same outcome ( Cushing et al., 2011 ; Meredith et al., 2011 ). Treatment is implemented after different durations of baseline across individuals. The order of treatment implementation across people can be randomized ( Wen et al., 2019 ). Figure 3 shows an example across three individuals. In this hypothetical example, baseline data for each person are relatively stable and not decreasing, and reductions in the dependent variable are only observed after introduction of the intervention. Inclusion of one control person, who remains in baseline throughout the study and provides a control for extended monitoring, is also possible. Another variation is to collect baseline data intermittently in a ‘probe’ design, which can minimize burden associated with simultaneous and repeated measurement of outcomes ( Byiers et al., 2012 ; Horner & Baer, 1978 ). If the outcomes do not change during baseline conditions and the changes only occur across participants after the treatment has been implemented—and this sequence is replicated across several people—change in the outcome may be safely attributed to the treatment. The length of the baselines still must be long enough to show stability and no trend toward improvement until the treatment is implemented.

P1–P3 represent different hypothetical participants.

The two other multiple baseline designs focus on individual people: the multiple baseline across settings and the multiple baseline across behaviors ( Boles et al., 2008 ; Lane-Brown & Tate, 2010 ). An example of a multiple baseline across settings would be a dietary intervention implemented across meals. An intervention that targets a reduction in consumption of high–glycemic index foods, or foods with added sugar across meals, could be developed with the order of meals randomized. For example, someone may be randomized to reduce sugar-added or high–glycemic index foods for breakfast without any implementation at lunch or dinner. Implementation of the diet at lunch and then dinner would occur after different durations of baselines in these settings. An example of multiple baseline across behaviors might be to use feedback to develop a comprehensive exercise program that involves stretching, aerobic exercise, and resistance training. Feedback could target improvement in one of these randomly selected behaviors, implemented in a staggered manner.

The main limitation to a multiple baseline design is that some people (or behaviors) may be kept in baseline or control conditions for extended periods before treatment is implemented. Of course, failure to receive an effective treatment is common in RCTs for people who are randomized to control conditions, but unlike control groups in RCTs, all participants eventually receive treatment.

Finally, while the emphasis in personalized medicine is the identification of an optimal treatment plan for an individual person, situations in which multiple baselines across people prove relevant for precision medicine may arise. For example, identification of a small group of people with common characteristics—perhaps with a rare disease and for which a multiple-baseline-across-people design could be used to test an intervention more effectively than a series of personalized designs—is possible. In a similar vein, differential response to a common treatment in a multiple-baseline-across-people design can help to identify individual differences that can compromise the response to a treatment.

4. Integrating Multiple Baseline and Reversal Designs

While reversal designs can be used to compare effects of interventions, multiple baseline designs provide experimental control for testing one intervention but do not compare different interventions. One way to take advantage of the strengths of both designs is to combine them. For example, the effects of a first treatment could be studied using a multiple-baseline format and, after experimental control has been established, return to baseline prior to the commencement of a different treatment, which may be introduced in a different order. These comparisons can be made for several different interventions with the combination of both designs to demonstrate experimental control and compare effects of the interventions.

Figure 4 shows a hypothetical example of a combined approach to identify the best drug to decrease blood pressure. Baseline blood pressures are established for three people under placebo conditions before new drug X is introduced across participants in a staggered fashion to establish relative changes in blood pressure. All return to placebo after blood pressures reach stability, drug Y is introduced in a staggered sequence, participants are returned to placebo, and the most effective intervention for each individual (drug X or Y) is reintroduced to replicate the most important result: the most effective medication. This across-subjects design establishes experimental control for two different new drug interventions across three people while also establishing experimental control for five comparisons within subjects (placebo—drug X, drug Y—placebo, placebo—drug Y, drug Y—placebo, placebo—more effective drug). Though this combined design strengthens confidence beyond either reversal or multiple baseline designs, in many situations, experimental control demonstrated using a reversal design is sufficient.

BL = Baseline. Drug X and Drug Y represent hypothetical drugs to lower blood pressure, and Best Drug represents a reversal to the most effective drug as identified for each hypothetical participant, labeled P1–P3.

5. Other Varieties of Single-Case Experimental Designs

Other less commonly used designs within the family of SCEDs may be useful for personalized medicine. One of the most relevant may be the alternating treatment design ( Barlow & Hayes, 1979 ; Manolov et al., 2021 ), in which people are exposed to baseline and one or more treatments for very brief periods without the concern about stability before changing conditions. While the treatment period may be short, many more replications of treatments—and ineffective treatments—can be identified quickly. This type of design may be relevant for drugs that have rapid effects with a short half-life and behavioral interventions that have rapid effects ( Coyle & Robertson, 1998 )—for example, the effects of biofeedback on heart rate ( Weems, 1998 ). Another design is the changing criterion design, in which experimental control is demonstrated when the outcome meets certain preselected criteria that can be systematically increased or decreased over time ( Hartmann & Hall, 1976 ). The design is especially useful when learning a new skill or when outcomes change slowly over time ( Singh & Leung, 1988 )—for example, gradually increasing the range of foods chosen in a previously highly selective eater ( Russo et al., 2019 ).

6. Integrating Single-Case Experimental Designs Into Randomized Controlled Trials

SCEDs can be integrated into RCTs to compare the efficacy of treatments chosen for someone based on SCEDs versus a standardized or usual care treatment ( Epstein et al., 2021 ; Schork & Goetz, 2017 ). Such innovative designs may capture the best in SCEDs and randomized controlled designs. Kravitz et al. (2018) used an RCT in which one group ( n = 108) experienced a series of reversal AB conditions, or a personalized (N-of-1) trial. The specific conditions were chosen for each patient from among eight categories of treatments to reduce chronic musculoskeletal pain (e.g., acetaminophen, any nonsteroidal anti-inflammatory drug, acetaminophen/oxycodone, tramadol). The other group ( n = 107) received usual care. The study also incorporated mobile technology to record pain-related data daily (see Dallery et al., 2013 , for a discussion of technology and SCEDs). The results suggested that the N-of-1 approach was feasible and acceptable, but it did not yield statistically significant superior results in pain measures compared to the usual care group. However, as noted by Vohra and Punja (2019) , the results do not indicate a flaw in the methodological approach: finding that two treatments do not differ in superiority is a finding worth knowing.

Another example of a situation where an integrated approach may be useful is selecting a diet for weight control. Many diets for weight control that vary in their macronutrient intake—such as low carb, higher fat versus low fat, and higher carb—have their proponents and favorable biological mechanisms. However, direct comparisons of these diets basically show that they achieve similar weight control with large variability in outcome. Thus, while the average person on a low-fat diet does about the same as the average person on a lowcarb diet, some people on the low-carb diet do very well, while some fail. Some of the people who fail on the low-fat diet would undoubtedly do well on the low-carb diet, and some who fail on the low-fat diet would do well on the low-carb diet. Further, some would fail on both diets due to general problems in adherence.

Personalized medicine suggests that diets should be individualized to achieve the best results. SCEDs would be one way to show ‘proof of concept’ that a particular diet is better than a standard healthy diet. First, people would be randomized to experimental (including SCEDs) or control (not basing diet on SCEDs). Subject selection criteria would proceed as in any RCT. For the first 3 months, people in the experimental group would engage in individual reversal designs in which 2-week intervals of low-carb and low-fat diets would be interspersed with their usual eating, and weight loss, diet adherence, food preferences, and the reinforcing value of foods in the diet would be measured to assess biological, behavioral, and subjective changes.

Participants in the control group would experience a similar exposure to the different types of diets, but the diet to which they are assigned would be randomly chosen rather than chosen using SCED methods. In this way, they would have similar exposure to diets during the first 3 months of the study, but this experience would not impact group assignment. As with any RCT, the study would proceed with regular measures (e.g., 6, 12, 24 months) and the hypothesis that those assigned to a diet that results in better initial weight loss, and that they like and are motivated to continue, would do better than those receiving a randomly selected diet. The study could also be designed with three groups: a single-case design experimental group similar to the approach in the hypothetical study above and two control groups, one low-fat and one low-carb.

An alternative design would be to have everyone experience SCEDs for the first 3 months and then be randomized to either the optimal treatment identified during the first 3 months or an intervention randomly chosen among the interventions to be studied. This design has the advantage of randomization being after 3 months of study so that dropouts and non-adherers within the first 3 months would not be randomized in an intent-to-treat format.

The goal of either hypothesized study, or any study that attempts to incorporate SCEDs into RCTs, is that matching participants to treatments will provide superior results in comparison to providing the same treatment to everyone in a group. Two hypotheses can be generated in these types of designs: first, that the mean changes will differ between groups, and second, that the variability will differ between groups with less variability in outcome for people who have treatment selected after a single-case trial than people who have a treatment randomly selected. A reduction in variability plus mean differences in outcome should increase the effect size for people treated using individualized designs, increase power, and allow for a smaller sample size to ensure confidence about the differences observed between groups.

7. Limitations of Single-Case Experimental Designs

Single-case experimental designs have their common limitations. If a measure changes with repeated testing without intervention, it may not be useful for an SCED unless steps can be taken to mitigate such reactivity, such as more unobtrusive monitoring ( Kazdin, 2021 ). Given that the effects of interventions are evaluated over time, systematic environmental changes or maturation could influence the relationship between a treatment and outcome and thereby obscure the effect of a treatment. However, the design logic of reversal and multiple baseline designs largely control for such influences. Since SCEDs rely on repeated measures and a detailed study of the relationship between treatment and outcome, studies that use dependent measures that cannot be sampled frequently are not candidates for SCEDs. Likewise, the failure to identify a temporal relationship between the introduction of treatment and initiation of change in the outcome can make attribution of changes to the intervention challenging. A confounding variable’s association with introduction or removal of the intervention, which may cause inappropriate decisions about the effects of the intervention, is always possible. Dropout or uncontrolled events that occur to individuals can introduce confounding variables to the SCED. These problems are not unique to SCEDs and also occur with RCTs.

8. Single-Case Experimental Designs in Early Stage Translational Research

The emphasis of a research program may be on translating basic science findings to clinical interventions. The goal may be to collect early phase translational research as a step toward a fully powered RCT—( Epstein et al., 2021 ). The fact that a large amount of basic science does not get translated into clinical interventions is well known ( Butler, 2008 ; Seyhan, 2019 ); this served in part as the stimulus for the National Institutes of Health (NIH) to develop a network of clinical and translational science institutes in medical schools and universities throughout the United States. A common approach to early phase translational research is to implement a small, underpowered RCT to secure a ‘signal’ of a treatment effect and an effect size. This is a problematic approach to pilot research, and it is not advocated by the NIH as an approach to early phase translational research ( National Center for Complementary and Integrative Health, 2020 ). The number of participants needed for a fully powered RCT may be substantially different from the number projected from a small-sample RCT. These small, underpowered, early phase translational studies may provide too large an estimate of an effect size, leading to an underpowered RCT. Likewise, a small-sample RCT can lead to a small effect size that can, in turn, lead to a failure to implement a potentially effective intervention ( Kraemer et al., 2006 ). Therefore, SCEDs—especially reversal and multiple baseline designs—are evidently ideally suited to early phase translational research. This use complements the utility of SCEDs for identifying the optimal treatment for an individual or small group of individuals.

9. Conclusion

Single-case experimental designs provide flexible, rigorous, and cost-effective approaches that can be used in personalized medicine to identify the optimal treatment for an individual patient. SCEDs represent a broad array of designs, and personalized (N-of-1) designs are a prominent example, particularly in medicine. These designs can be incorporated into RCTs, and they can be integrated using meta-analysis techniques. SCEDs should become a standard part of the toolbox for clinical researchers to improve clinical care for their patients, and they can lead to the next generation of interventions that show maximal effects for individual cases as well as for early phase translational research to clinical practice.

Acknowledgments

We thank Lesleigh Stinson and Andrea Villegas for preparing the figures.

Disclosure Statement

Preparation of this special issue was supported by grants R01LM012836 from the National Library of Medicine of the National Institutes of Health and P30AG063786 from the National Institute on Aging of the National Institutes of Health. Funding to authors of this article was supported by grants U01 HL131552 from the National Heart, Lung, and Blood Institute, UH3 DK109543 from the National Institute of Diabetes, Digestive and Kidney Diseases, and RO1HD080292 and RO1HD088131 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. The views expressed in this paper are those of the authors and do not represent the views of the National Institutes of Health, the U.S. Department of Health and Human Services, or any other government entity.

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Single-Case Designs

• First Online: 30 April 2023

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• Lodi Lipien 3 ,
• Megan Kirby 3 &
• John M. Ferron 3  

Part of the book series: Autism and Child Psychopathology Series ((ACPS))

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Single-case design (SCD), also known as single-case experimental design, single-subject design, or N-of-1 trials, refers to a research methodology that involves examining the effect of an intervention on a single individual over time by repeatedly measuring a target behavior across different intervention conditions. These designs may include replication across cases, but the focus is on individual effects. Differences in the target behaviors and individuals studied, as well as differences in the research questions posed, have spurred the development of a variety of single-case designs, each with distinct advantages in specific situations. These designs include reversal designs, multiple baseline designs (MBD), alternating treatments designs (ATD), and changing criterion designs (CCD). Our purpose is to describe these designs and their application in behavioral research. In doing so, we consider the questions they address and the conditions under which they are well suited to answer those questions.

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Reversal designs, first described by Leitenberg ( 1973 ) and later reviewed by Wine et al. ( 2015 ), originally referred to a type of design in which the effects of one IV on two topographically distinct DVs (DV 1, DV 2) were repeatedly measured across time. The intervention, such as reinforcement, was presented in each phase but was in effect for either DV 1 or DV 2. The purpose of the use is to show changes in rates of responding when an IV is introduced to DV 1 and withdrawn from DV 2, as the rate of responding for each would change across phases when in the presence or absence of the IV. However, the reversal design as described is rarely used in contemporary behavior analytic literature and is often used interchangeably with withdrawal design .

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Lipien, L., Kirby, M., Ferron, J.M. (2023). Single-Case Designs. In: Matson, J.L. (eds) Handbook of Applied Behavior Analysis. Autism and Child Psychopathology Series. Springer, Cham. https://doi.org/10.1007/978-3-031-19964-6_20

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DOI : https://doi.org/10.1007/978-3-031-19964-6_20

Published : 30 April 2023

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Single-case experimental designs: Characteristics, changes, and challenges

Affiliation.

• 1 Yale University.
• PMID: 33205436
• DOI: 10.1002/jeab.638

Tactics of Scientific Research (Sidman, 1960) provides a visionary treatise on single-case designs, their scientific underpinnings, and their critical role in understanding behavior. Since the foundational base was provided, single-case designs have proliferated especially in areas of application where they have been used to evaluate interventions with an extraordinary range of clients, settings, and target foci. This article highlights core features of single-case experimental designs, how key and ancillary features of the designs have evolved, the special strengths of the designs, and challenges that have impeded their integration in many areas where their contributions are sorely needed. The article ends by placing the methodological approach in the context of other research traditions. In this way, the discussion moves from the specific designs toward foundations and philosophy of science issues in keeping with the strengths of the person and book we are honoring.

Keywords: challenges; changes; characteristics.

© 2020 Society for the Experimental Analysis of Behavior.

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